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Unverified Commit 4dcf31b6 authored by Xiaomeng Zhao's avatar Xiaomeng Zhao Committed by GitHub
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Merge pull request #1076 from opendatalab/release-0.10.1 

Release 0.10.1
parents dc37af0a 4f13c282
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demo/demo.py
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import os import os
from loguru import logger from loguru import logger
from magic_pdf.pipe.UNIPipe import UNIPipe
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.pipe.UNIPipe import UNIPipe
try: try:
current_script_dir = os.path.dirname(os.path.abspath(__file__)) current_script_dir = os.path.dirname(os.path.abspath(__file__))
demo_name = "demo1" demo_name = 'demo1'
pdf_path = os.path.join(current_script_dir, f"{demo_name}.pdf") pdf_path = os.path.join(current_script_dir, f'{demo_name}.pdf')
pdf_bytes = open(pdf_path, "rb").read() pdf_bytes = open(pdf_path, 'rb').read()
jso_useful_key = {"_pdf_type": "", "model_list": []} jso_useful_key = {'_pdf_type': '', 'model_list': []}
local_image_dir = os.path.join(current_script_dir, 'images') local_image_dir = os.path.join(current_script_dir, 'images')
image_dir = str(os.path.basename(local_image_dir)) image_dir = str(os.path.basename(local_image_dir))
image_writer = DiskReaderWriter(local_image_dir) image_writer = FileBasedDataWriter(local_image_dir)
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
pipe.pipe_classify() pipe.pipe_classify()
pipe.pipe_analyze() pipe.pipe_analyze()
pipe.pipe_parse() pipe.pipe_parse()
md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none") md_content = pipe.pipe_mk_markdown(image_dir, drop_mode='none')
with open(f"{demo_name}.md", "w", encoding="utf-8") as f: with open(f'{demo_name}.md', 'w', encoding='utf-8') as f:
f.write(md_content) f.write(md_content)
except Exception as e: except Exception as e:
logger.exception(e) logger.exception(e)
\ No newline at end of file
demo/demo1.json
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demo/demo2.json
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Note that individual disparities can be converted"}, {"category_id": 15, "poly": [864.0, 561.0, 1568.0, 561.0, 1568.0, 594.0, 864.0, 594.0], "score": 0.98, "text": "to actual depths if the geometry of the camera setup is"}, {"category_id": 15, "poly": [859.0, 587.0, 1568.0, 591.0, 1568.0, 630.0, 859.0, 626.0], "score": 0.98, "text": " known, i.e., the stereo configuration of cameras has been pre-"}, {"category_id": 15, "poly": [862.0, 626.0, 984.0, 626.0, 984.0, 658.0, 862.0, 658.0], "score": 1.0, "text": "calibrated."}, {"category_id": 15, "poly": [155.0, 921.0, 839.0, 924.0, 838.0, 963.0, 155.0, 960.0], "score": 0.98, "text": " Modern stereo matching algorithms achieve excellent results"}, {"category_id": 15, "poly": [127.0, 956.0, 838.0, 958.0, 838.0, 997.0, 127.0, 995.0], "score": 0.98, "text": " on static stereo images, as demonstrated by the Middlebury"}, {"category_id": 15, "poly": [132.0, 995.0, 836.0, 995.0, 836.0, 1027.0, 132.0, 1027.0], "score": 0.98, "text": "stereo performance benchmark [1], [2]. 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The problem of obtaining temporally"}, {"category_id": 15, "poly": [132.0, 1194.0, 838.0, 1194.0, 838.0, 1226.0, 132.0, 1226.0], "score": 0.98, "text": "consistent sequences of disparity maps from video streams is"}, {"category_id": 15, "poly": [134.0, 1228.0, 838.0, 1228.0, 838.0, 1260.0, 134.0, 1260.0], "score": 0.98, "text": "known as the temporal stereo correspondence problem, yet"}, {"category_id": 15, "poly": [129.0, 1258.0, 841.0, 1260.0, 841.0, 1293.0, 129.0, 1290.0], "score": 0.98, "text": "the amount of research efforts oriented towards finding an"}, {"category_id": 15, "poly": [134.0, 1292.0, 760.0, 1292.0, 760.0, 1325.0, 134.0, 1325.0], "score": 0.99, "text": "effective solution to this problem is surprisingly small."}, {"category_id": 15, "poly": [157.0, 1320.0, 836.0, 1322.0, 836.0, 1361.0, 157.0, 1359.0], "score": 0.98, "text": " A method is proposed for real-time temporal stereo match-"}, {"category_id": 15, "poly": [134.0, 1361.0, 836.0, 1361.0, 836.0, 1393.0, 134.0, 1393.0], "score": 1.0, "text": "ing that efficiently propagates matching cost information be-"}, {"category_id": 15, "poly": [134.0, 1393.0, 836.0, 1393.0, 836.0, 1425.0, 134.0, 1425.0], "score": 0.99, "text": "tween consecutive frames of a stereo video sequence. This"}, {"category_id": 15, "poly": [132.0, 1423.0, 834.0, 1425.0, 834.0, 1458.0, 132.0, 1455.0], "score": 0.98, "text": "method is invariant to the number of prior frames being"}, {"category_id": 15, "poly": [134.0, 1458.0, 836.0, 1458.0, 836.0, 1490.0, 134.0, 1490.0], "score": 0.99, "text": "considered, and can be easily incorporated into any local stereo"}, {"category_id": 15, "poly": [132.0, 1492.0, 836.0, 1492.0, 836.0, 1524.0, 132.0, 1524.0], "score": 0.98, "text": "method based on edge-aware filters. The iterative adaptive"}, {"category_id": 15, "poly": [132.0, 1526.0, 838.0, 1526.0, 838.0, 1558.0, 132.0, 1558.0], "score": 0.99, "text": "support matching algorithm presented in [3] serves as a"}, {"category_id": 15, "poly": [132.0, 1558.0, 557.0, 1558.0, 557.0, 1590.0, 132.0, 1590.0], "score": 0.99, "text": "foundation for the proposed method."}, {"category_id": 15, "poly": [887.0, 1483.0, 1571.0, 1485.0, 1571.0, 1524.0, 887.0, 1522.0], "score": 0.98, "text": " In contrast, local methods, which are typically built upon"}, {"category_id": 15, "poly": [859.0, 1517.0, 1573.0, 1519.0, 1573.0, 1558.0, 859.0, 1556.0], "score": 0.97, "text": " the Winner-Takes-All (WTA) framework, have the property of "}, {"category_id": 15, "poly": [864.0, 1556.0, 1566.0, 1556.0, 1566.0, 1588.0, 864.0, 1588.0], "score": 0.99, "text": "computational regularity and are thus suitable for implemen-"}, {"category_id": 15, "poly": [862.0, 1588.0, 1566.0, 1588.0, 1566.0, 1620.0, 862.0, 1620.0], "score": 1.0, "text": "tation on parallel graphics hardware. Within the WTA frame-"}, {"category_id": 15, "poly": [862.0, 1616.0, 1568.0, 1618.0, 1568.0, 1657.0, 862.0, 1655.0], "score": 0.98, "text": "work, local stereo algorithms consider a range of disparity"}, {"category_id": 15, "poly": [864.0, 1655.0, 1566.0, 1655.0, 1566.0, 1687.0, 864.0, 1687.0], "score": 0.98, "text": "hypotheses and compute a volume of pixel-wise dissimilarity"}, {"category_id": 15, "poly": [862.0, 1689.0, 1571.0, 1689.0, 1571.0, 1721.0, 862.0, 1721.0], "score": 0.99, "text": "metrics between the reference image and the matched image at"}, {"category_id": 15, "poly": [862.0, 1723.0, 1568.0, 1721.0, 1568.0, 1753.0, 862.0, 1755.0], "score": 0.99, "text": "every considered disparity value. Final disparities are chosen"}, {"category_id": 15, "poly": [864.0, 1755.0, 1568.0, 1755.0, 1568.0, 1785.0, 864.0, 1785.0], "score": 1.0, "text": "from the cost volume by traversing through its values and"}, {"category_id": 15, "poly": [866.0, 1788.0, 1568.0, 1788.0, 1568.0, 1820.0, 866.0, 1820.0], "score": 0.99, "text": "selecting the disparities associated with minimum matching"}, {"category_id": 15, "poly": [859.0, 1817.0, 1377.0, 1820.0, 1377.0, 1859.0, 859.0, 1856.0], "score": 0.98, "text": " costs for every pixel of the reference image."}, {"category_id": 15, "poly": [885.0, 1187.0, 1571.0, 1187.0, 1571.0, 1226.0, 885.0, 1226.0], "score": 0.97, "text": " In their excellent taxonomy paper [1], Scharstein and"}, {"category_id": 15, "poly": [864.0, 1224.0, 1566.0, 1224.0, 1566.0, 1254.0, 864.0, 1254.0], "score": 0.99, "text": "Szeliski classify stereo algorithms as local or global meth-"}, {"category_id": 15, "poly": [859.0, 1249.0, 1571.0, 1254.0, 1570.0, 1293.0, 859.0, 1288.0], "score": 0.99, "text": " ods. Global methods, which offer outstanding accuracy, are"}, {"category_id": 15, "poly": [862.0, 1288.0, 1571.0, 1288.0, 1571.0, 1327.0, 862.0, 1327.0], "score": 0.98, "text": "typically derived from an energy minimization framework"}, {"category_id": 15, "poly": [859.0, 1322.0, 1566.0, 1322.0, 1566.0, 1352.0, 859.0, 1352.0], "score": 0.99, "text": "that allows for explicit integration of disparity smoothness"}, {"category_id": 15, "poly": [864.0, 1357.0, 1568.0, 1357.0, 1568.0, 1389.0, 864.0, 1389.0], "score": 0.99, "text": "constraints and thus is capable of regularizing the solution"}, {"category_id": 15, "poly": [864.0, 1391.0, 1568.0, 1391.0, 1568.0, 1421.0, 864.0, 1421.0], "score": 1.0, "text": "in weakly textured areas. The minimization, however, is often"}, {"category_id": 15, "poly": [864.0, 1423.0, 1568.0, 1423.0, 1568.0, 1455.0, 864.0, 1455.0], "score": 0.99, "text": "achieved using iterative methods or graph cuts, which do not"}, {"category_id": 15, "poly": [864.0, 1458.0, 1418.0, 1458.0, 1418.0, 1487.0, 864.0, 1487.0], "score": 0.99, "text": "lend themselves well to parallel implementation."}, {"category_id": 15, "poly": [155.0, 1650.0, 839.0, 1652.0, 838.0, 1691.0, 155.0, 1689.0], "score": 0.97, "text": " Stereo matching is the process of identifying correspon-"}, {"category_id": 15, "poly": [134.0, 1687.0, 838.0, 1687.0, 838.0, 1719.0, 134.0, 1719.0], "score": 0.99, "text": "dences between pixels in stereo images obtained using a"}, {"category_id": 15, "poly": [132.0, 1723.0, 838.0, 1721.0, 838.0, 1753.0, 132.0, 1755.0], "score": 0.98, "text": "pair of synchronized cameras. These correspondences are"}, {"category_id": 15, "poly": [134.0, 1755.0, 836.0, 1755.0, 836.0, 1788.0, 134.0, 1788.0], "score": 0.99, "text": "conveniently represented using the notion of disparity, i.e. the"}, {"category_id": 15, "poly": [134.0, 1788.0, 836.0, 1788.0, 836.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "positional offset between two matching pixels. It is assumed"}, {"category_id": 15, "poly": [134.0, 1822.0, 836.0, 1822.0, 836.0, 1854.0, 134.0, 1854.0], "score": 0.99, "text": "that the stereo images are rectified, such that matching pixels"}, {"category_id": 15, "poly": [132.0, 1854.0, 836.0, 1854.0, 836.0, 1886.0, 132.0, 1886.0], "score": 0.99, "text": "are confined within corresponding rows of the images and"}, {"category_id": 15, "poly": [134.0, 1888.0, 838.0, 1888.0, 838.0, 1918.0, 134.0, 1918.0], "score": 1.0, "text": "thus disparities are restricted to the horizontal dimension, as"}, {"category_id": 15, "poly": [134.0, 1920.0, 838.0, 1920.0, 838.0, 1952.0, 134.0, 1952.0], "score": 1.0, "text": "illustrated in Figure 1. For visualization purposes, disparities"}, {"category_id": 15, "poly": [134.0, 1955.0, 838.0, 1955.0, 838.0, 1987.0, 134.0, 1987.0], "score": 0.99, "text": "recovered for every pixel of a reference image are stored"}, {"category_id": 15, "poly": [129.0, 1985.0, 841.0, 1982.0, 841.0, 2021.0, 129.0, 2024.0], "score": 0.98, "text": "together in the form of an image, which is known as the"}, {"category_id": 15, "poly": [370.0, 885.0, 594.0, 885.0, 594.0, 917.0, 370.0, 917.0], "score": 1.0, "text": "1. INTRODUCTION"}, {"category_id": 15, "poly": [638.0, 2099.0, 1062.0, 2099.0, 1062.0, 2131.0, 638.0, 2131.0], "score": 0.98, "text": "978-1-4673-5208-6/13/$31.00 @2013 IEEE"}, {"category_id": 15, "poly": [374.0, 1613.0, 591.0, 1613.0, 591.0, 1645.0, 374.0, 1645.0], "score": 0.95, "text": "II. BACKGROUND"}, {"category_id": 15, "poly": [859.0, 992.0, 1571.0, 995.0, 1571.0, 1034.0, 859.0, 1031.0], "score": 0.99, "text": " Figure 1: Geometry of two horizontally aligned views where p"}, {"category_id": 15, "poly": [864.0, 1098.0, 1291.0, 1098.0, 1291.0, 1130.0, 864.0, 1130.0], "score": 0.99, "text": "them along the horizontal dimension."}, {"category_id": 15, "poly": [859.0, 1061.0, 1194.0, 1059.0, 1194.0, 1098.0, 859.0, 1100.0], "score": 0.98, "text": " pixel in the target frame, and"}, {"category_id": 15, "poly": [1227.0, 1061.0, 1571.0, 1059.0, 1571.0, 1098.0, 1227.0, 1100.0], "score": 0.97, "text": " denotes the disparity between"}, {"category_id": 15, "poly": [864.0, 1034.0, 1303.0, 1034.0, 1303.0, 1063.0, 864.0, 1063.0], "score": 0.99, "text": "denotes a pixel in the reference frame,"}, {"category_id": 15, "poly": [1328.0, 1034.0, 1566.0, 1034.0, 1566.0, 1063.0, 1328.0, 1063.0], "score": 0.96, "text": " denotes its matching"}, {"category_id": 15, "poly": [508.0, 357.0, 1194.0, 360.0, 1194.0, 392.0, 508.0, 390.0], "score": 0.98, "text": "Jedrzej Kowalczuk, Eric T. Psota, and Lance C. P\u00e9rez"}, {"category_id": 15, "poly": [443.0, 392.0, 1245.0, 392.0, 1245.0, 424.0, 443.0, 424.0], "score": 0.99, "text": "Department of Electrical Engineering, University of Nebraska-Lincoln"}, {"category_id": 15, "poly": [614.0, 435.0, 1081.0, 435.0, 1081.0, 465.0, 614.0, 465.0], "score": 0.99, "text": "[jkowalczuk2,epsota,lperez] @unl.edu"}, {"category_id": 15, "poly": [159.0, 527.0, 836.0, 527.0, 836.0, 559.0, 159.0, 559.0], "score": 0.98, "text": "Abstract-Stereo matching algorithms are nearly always de-"}, {"category_id": 15, "poly": [132.0, 555.0, 838.0, 555.0, 838.0, 587.0, 132.0, 587.0], "score": 0.98, "text": "signed to find matches between a single pair of images. A method"}, {"category_id": 15, "poly": [134.0, 580.0, 836.0, 580.0, 836.0, 612.0, 134.0, 612.0], "score": 1.0, "text": "is presented that was specifically designed to operate on sequences"}, {"category_id": 15, "poly": [132.0, 605.0, 838.0, 607.0, 838.0, 646.0, 132.0, 644.0], "score": 0.99, "text": "of images. This method considers the cost of matching image"}, {"category_id": 15, "poly": [132.0, 637.0, 838.0, 637.0, 838.0, 669.0, 132.0, 669.0], "score": 0.98, "text": "points in both the spatial and temporal domain. To maintain"}, {"category_id": 15, "poly": [134.0, 667.0, 838.0, 667.0, 838.0, 699.0, 134.0, 699.0], "score": 0.97, "text": "real-time operation, a temporal cost aggregation method is used"}, {"category_id": 15, "poly": [132.0, 692.0, 836.0, 692.0, 836.0, 722.0, 132.0, 722.0], "score": 0.98, "text": "to evaluate the likelihood of matches that is invariant with respect"}, {"category_id": 15, "poly": [127.0, 717.0, 841.0, 715.0, 841.0, 754.0, 127.0, 756.0], "score": 0.97, "text": "to the number of prior images being considered. This method"}, {"category_id": 15, "poly": [127.0, 742.0, 841.0, 745.0, 841.0, 784.0, 127.0, 781.0], "score": 0.98, "text": "has been implemented on massively parallel GPU hardware,"}, {"category_id": 15, "poly": [132.0, 777.0, 838.0, 777.0, 838.0, 809.0, 132.0, 809.0], "score": 0.99, "text": "and the implementation ranks as one of the fastest and most"}, {"category_id": 15, "poly": [132.0, 802.0, 838.0, 804.0, 838.0, 836.0, 132.0, 834.0], "score": 0.99, "text": "accurate real-time stereo matching methods as measured by the"}, {"category_id": 15, "poly": [134.0, 830.0, 619.0, 830.0, 619.0, 862.0, 134.0, 862.0], "score": 0.99, "text": "Middlebury stereo performance benchmark."}, {"category_id": 15, "poly": [887.0, 1849.0, 1568.0, 1852.0, 1568.0, 1891.0, 887.0, 1888.0], "score": 0.99, "text": " Disparity maps obtained using this simple strategy are often"}, {"category_id": 15, "poly": [862.0, 1888.0, 1568.0, 1888.0, 1568.0, 1920.0, 862.0, 1920.0], "score": 0.98, "text": "too noisy to be considered useable. To reduce the effects"}, {"category_id": 15, "poly": [864.0, 1923.0, 1568.0, 1923.0, 1568.0, 1952.0, 864.0, 1952.0], "score": 0.99, "text": "of noise and enforce spatial consistency of matches, local"}, {"category_id": 15, "poly": [862.0, 1948.0, 1568.0, 1950.0, 1568.0, 1989.0, 861.0, 1987.0], "score": 0.99, "text": "stereo algorithms consider arbitrarily shaped and sized support"}, {"category_id": 15, "poly": [864.0, 1989.0, 1568.0, 1989.0, 1568.0, 2021.0, 864.0, 2021.0], "score": 0.99, "text": "windows centered at each pixel of the reference image, and"}], "page_info": {"page_no": 0, "height": 2200, "width": 1700}}, {"layout_dets": [{"category_id": 8, "poly": [962.3624267578125, 1513.2073974609375, 1465.4017333984375, 1513.2073974609375, 1465.4017333984375, 1669.1397705078125, 962.3624267578125, 1669.1397705078125], "score": 0.9999995231628418}, {"category_id": 9, "poly": [1530.72998046875, 1101.879638671875, 1565.2568359375, 1101.879638671875, 1565.2568359375, 1130.8609619140625, 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Here, the truncation of color difference for the red,"}, {"category_id": 15, "poly": [866.0, 1719.0, 1547.0, 1719.0, 1547.0, 1749.0, 866.0, 1749.0], "score": 0.99, "text": "sum of truncated absolute color differences between pixels"}, {"category_id": 15, "poly": [864.0, 163.0, 1568.0, 163.0, 1568.0, 192.0, 864.0, 192.0], "score": 1.0, "text": "temporal information, making it possible to process a temporal"}, {"category_id": 15, "poly": [859.0, 188.0, 1571.0, 193.0, 1570.0, 229.0, 859.0, 225.0], "score": 0.99, "text": " collection of cost volumes. The filtering operation was shown"}, {"category_id": 15, "poly": [864.0, 229.0, 1566.0, 229.0, 1566.0, 261.0, 864.0, 261.0], "score": 0.99, "text": "to preserve spatio-temporal edges present in the cost volumes,"}, {"category_id": 15, "poly": [859.0, 261.0, 1564.0, 264.0, 1564.0, 296.0, 859.0, 293.0], "score": 0.98, "text": " resulting in increased temporal consistency of disparity maps,"}, {"category_id": 15, "poly": [864.0, 296.0, 1566.0, 296.0, 1566.0, 328.0, 864.0, 328.0], "score": 0.99, "text": "greater robustness to image noise, and more accurate behavior"}, {"category_id": 15, "poly": [866.0, 328.0, 1160.0, 328.0, 1160.0, 360.0, 866.0, 360.0], "score": 1.0, "text": "around object boundaries."}, {"category_id": 15, "poly": [129.0, 158.0, 841.0, 153.0, 841.0, 192.0, 130.0, 197.0], "score": 0.99, "text": "aggregate cost values within the pixel neighborhoods defined"}, {"category_id": 15, "poly": [129.0, 188.0, 841.0, 190.0, 841.0, 229.0, 129.0, 227.0], "score": 0.99, "text": "by these windows. In 2005, Yoon and Kweon [4] proposed"}, {"category_id": 15, "poly": [132.0, 229.0, 838.0, 229.0, 838.0, 261.0, 132.0, 261.0], "score": 1.0, "text": "an adaptive matching cost aggregation scheme, which assigns"}, {"category_id": 15, "poly": [132.0, 261.0, 838.0, 261.0, 838.0, 293.0, 132.0, 293.0], "score": 0.98, "text": "a weight value to every pixel located in the support window"}, {"category_id": 15, "poly": [132.0, 293.0, 838.0, 293.0, 838.0, 325.0, 132.0, 325.0], "score": 0.98, "text": "of a given pixel of interest. The weight value is based on"}, {"category_id": 15, "poly": [132.0, 328.0, 836.0, 328.0, 836.0, 360.0, 132.0, 360.0], "score": 0.99, "text": "the spatial and color similarity between the pixel of interest"}, {"category_id": 15, "poly": [134.0, 360.0, 836.0, 360.0, 836.0, 392.0, 134.0, 392.0], "score": 1.0, "text": "and a pixel in its support window, and the aggregated cost is"}, {"category_id": 15, "poly": [134.0, 394.0, 836.0, 394.0, 836.0, 426.0, 134.0, 426.0], "score": 0.99, "text": "computed as a weighted average of the pixel-wise costs within"}, {"category_id": 15, "poly": [127.0, 422.0, 839.0, 424.0, 838.0, 463.0, 127.0, 461.0], "score": 0.98, "text": " the considered support window. The edge-preserving nature"}, {"category_id": 15, "poly": [129.0, 456.0, 838.0, 454.0, 838.0, 493.0, 129.0, 495.0], "score": 0.99, "text": " and matching accuracy of adaptive support weights have made"}, {"category_id": 15, "poly": [132.0, 490.0, 841.0, 490.0, 841.0, 529.0, 132.0, 529.0], "score": 0.99, "text": "them one of the most popular choices for cost aggregation in"}, {"category_id": 15, "poly": [132.0, 527.0, 797.0, 527.0, 797.0, 559.0, 132.0, 559.0], "score": 0.97, "text": "recently proposed stereo matching algorithms [3], [5]-[8]."}, {"category_id": 15, "poly": [157.0, 958.0, 836.0, 958.0, 836.0, 988.0, 157.0, 988.0], "score": 0.99, "text": "It has been demonstrated that the performance of stereo"}, {"category_id": 15, "poly": [132.0, 990.0, 838.0, 990.0, 838.0, 1022.0, 132.0, 1022.0], "score": 0.99, "text": "algorithms designed to match a single pair of images can"}, {"category_id": 15, "poly": [132.0, 1024.0, 836.0, 1024.0, 836.0, 1056.0, 132.0, 1056.0], "score": 0.99, "text": "be adapted to take advantage of the temporal dependencies"}, {"category_id": 15, "poly": [129.0, 1054.0, 838.0, 1054.0, 838.0, 1093.0, 129.0, 1093.0], "score": 0.97, "text": "available in stereo video sequences. Early proposed solutions"}, {"category_id": 15, "poly": [132.0, 1091.0, 836.0, 1091.0, 836.0, 1123.0, 132.0, 1123.0], "score": 0.99, "text": "to temporal stereo matching attempted to average matching"}, {"category_id": 15, "poly": [134.0, 1123.0, 836.0, 1123.0, 836.0, 1155.0, 134.0, 1155.0], "score": 0.99, "text": "costs across subsequent frames of a video sequence [13],"}, {"category_id": 15, "poly": [129.0, 1153.0, 841.0, 1150.0, 841.0, 1189.0, 129.0, 1192.0], "score": 0.98, "text": "[14]. Attempts have been made to integrate estimation of"}, {"category_id": 15, "poly": [134.0, 1192.0, 838.0, 1192.0, 838.0, 1224.0, 134.0, 1224.0], "score": 0.99, "text": "motion fields (optical flow) into temporal stereo matching. The"}, {"category_id": 15, "poly": [132.0, 1224.0, 838.0, 1224.0, 838.0, 1256.0, 132.0, 1256.0], "score": 0.99, "text": "methods of [15] and [16] perform smoothing of disparities"}, {"category_id": 15, "poly": [129.0, 1254.0, 841.0, 1254.0, 841.0, 1292.0, 129.0, 1292.0], "score": 0.99, "text": " along motion vectors recovered from the video sequence. The"}, {"category_id": 15, "poly": [132.0, 1290.0, 838.0, 1290.0, 838.0, 1322.0, 132.0, 1322.0], "score": 0.99, "text": "estimation of the motion field, however, prevents real-time"}, {"category_id": 15, "poly": [132.0, 1325.0, 838.0, 1325.0, 838.0, 1354.0, 132.0, 1354.0], "score": 0.99, "text": "implementation, since state-of-the-art optical flow algorithms"}, {"category_id": 15, "poly": [129.0, 1354.0, 841.0, 1354.0, 841.0, 1393.0, 129.0, 1393.0], "score": 0.99, "text": " do not, in general, approach real-time frame rates. In a related"}, {"category_id": 15, "poly": [129.0, 1386.0, 841.0, 1384.0, 841.0, 1423.0, 129.0, 1425.0], "score": 0.99, "text": "approach, Sizintsev and Wildes [17], [18] used steerable"}, {"category_id": 15, "poly": [134.0, 1423.0, 836.0, 1423.0, 836.0, 1455.0, 134.0, 1455.0], "score": 0.99, "text": "filters to obtain descriptors characterizing motion of image"}, {"category_id": 15, "poly": [134.0, 1455.0, 836.0, 1455.0, 836.0, 1487.0, 134.0, 1487.0], "score": 0.99, "text": "features in both space and time. Unlike traditional algorithms,"}, {"category_id": 15, "poly": [132.0, 1490.0, 838.0, 1490.0, 838.0, 1522.0, 132.0, 1522.0], "score": 0.98, "text": "their method performs matching on spatio-temporal motion"}, {"category_id": 15, "poly": [129.0, 1519.0, 841.0, 1517.0, 841.0, 1556.0, 129.0, 1558.0], "score": 0.99, "text": " descriptors, rather than on pure pixel intensity values, which"}, {"category_id": 15, "poly": [132.0, 1554.0, 841.0, 1554.0, 841.0, 1593.0, 132.0, 1593.0], "score": 0.99, "text": "leads to improved temporal coherence of disparity maps at the"}, {"category_id": 15, "poly": [132.0, 1586.0, 698.0, 1586.0, 698.0, 1618.0, 132.0, 1618.0], "score": 0.99, "text": "cost of reduced accuracy at depth discontinuities."}, {"category_id": 15, "poly": [159.0, 559.0, 838.0, 559.0, 838.0, 591.0, 159.0, 591.0], "score": 0.99, "text": "Recently, Rheman et al. [9], [10] have revisited the cost"}, {"category_id": 15, "poly": [132.0, 594.0, 838.0, 589.0, 839.0, 621.0, 132.0, 626.0], "score": 1.0, "text": "aggregation step of stereo algorithms, and demonstrated that"}, {"category_id": 15, "poly": [132.0, 626.0, 838.0, 626.0, 838.0, 658.0, 132.0, 658.0], "score": 0.99, "text": "cost aggregation can be performed by filtering of subsequent"}, {"category_id": 15, "poly": [134.0, 660.0, 834.0, 660.0, 834.0, 692.0, 134.0, 692.0], "score": 1.0, "text": "layers of the initially computed matching cost volume. In par-"}, {"category_id": 15, "poly": [132.0, 692.0, 836.0, 692.0, 836.0, 724.0, 132.0, 724.0], "score": 0.99, "text": "ticular, the edge-aware image filters, such as the bilateral filter"}, {"category_id": 15, "poly": [127.0, 719.0, 839.0, 724.0, 838.0, 761.0, 127.0, 756.0], "score": 0.99, "text": " of Tomasi and Manducci [11] or the guided filter of He [12],"}, {"category_id": 15, "poly": [132.0, 759.0, 838.0, 759.0, 838.0, 791.0, 132.0, 791.0], "score": 0.98, "text": "have been rendered useful for the problem of matching cost"}, {"category_id": 15, "poly": [132.0, 793.0, 838.0, 791.0, 838.0, 823.0, 132.0, 825.0], "score": 0.99, "text": "aggregation, enabling stereo algorithms to correctly recover"}, {"category_id": 15, "poly": [134.0, 825.0, 838.0, 825.0, 838.0, 857.0, 134.0, 857.0], "score": 0.98, "text": "disparities along object boundaries. In fact, Yoon and Kweon's"}, {"category_id": 15, "poly": [134.0, 859.0, 838.0, 859.0, 838.0, 891.0, 134.0, 891.0], "score": 1.0, "text": "adaptive support-weight cost aggregation scheme is equivalent"}, {"category_id": 15, "poly": [132.0, 891.0, 838.0, 891.0, 838.0, 924.0, 132.0, 924.0], "score": 0.98, "text": "to the application of the so-called joint bilateral filter to the"}, {"category_id": 15, "poly": [134.0, 924.0, 547.0, 924.0, 547.0, 956.0, 134.0, 956.0], "score": 1.0, "text": "layers of the matching cost volume."}, {"category_id": 15, "poly": [889.0, 422.0, 1568.0, 424.0, 1568.0, 456.0, 889.0, 454.0], "score": 0.98, "text": "The proposed temporal stereo matching algorithm is an"}, {"category_id": 15, "poly": [862.0, 456.0, 1571.0, 456.0, 1571.0, 495.0, 862.0, 495.0], "score": 1.0, "text": "extension of the real-time iterative adaptive support-weight"}, {"category_id": 15, "poly": [864.0, 490.0, 1568.0, 490.0, 1568.0, 522.0, 864.0, 522.0], "score": 0.99, "text": "algorithm described in [3]. In addition to real-time two-"}, {"category_id": 15, "poly": [864.0, 525.0, 1566.0, 525.0, 1566.0, 557.0, 864.0, 557.0], "score": 1.0, "text": "pass aggregation of the cost values in the spatial domain,"}, {"category_id": 15, "poly": [864.0, 557.0, 1568.0, 557.0, 1568.0, 589.0, 864.0, 589.0], "score": 0.99, "text": "the proposed algorithm enhances stereo matching on video"}, {"category_id": 15, "poly": [866.0, 594.0, 1566.0, 594.0, 1566.0, 626.0, 866.0, 626.0], "score": 0.97, "text": "sequences by aggregating costs along the time dimension."}, {"category_id": 15, "poly": [864.0, 626.0, 1568.0, 626.0, 1568.0, 658.0, 864.0, 658.0], "score": 1.0, "text": "The operation of the algorithm has been divided into four"}, {"category_id": 15, "poly": [866.0, 660.0, 1568.0, 660.0, 1568.0, 692.0, 866.0, 692.0], "score": 0.99, "text": "stages: 1) two-pass spatial cost aggregation, 2) temporal cost"}, {"category_id": 15, "poly": [862.0, 688.0, 1568.0, 685.0, 1568.0, 724.0, 862.0, 727.0], "score": 1.0, "text": "aggregation, 3) disparity selection and confidence assessment,"}, {"category_id": 15, "poly": [866.0, 724.0, 1568.0, 724.0, 1568.0, 756.0, 866.0, 756.0], "score": 1.0, "text": "and 4) iterative disparity refinement. In the following, each of"}, {"category_id": 15, "poly": [864.0, 759.0, 1254.0, 759.0, 1254.0, 791.0, 864.0, 791.0], "score": 1.0, "text": "these stages is described in detail."}, {"category_id": 15, "poly": [860.0, 1265.0, 1194.0, 1270.0, 1194.0, 1306.0, 859.0, 1301.0], "score": 0.99, "text": " color similarity, respectively."}, {"category_id": 15, "poly": [1433.0, 1169.0, 1566.0, 1169.0, 1566.0, 1201.0, 1433.0, 1201.0], "score": 0.98, "text": "is the color"}, {"category_id": 15, "poly": [864.0, 1169.0, 938.0, 1169.0, 938.0, 1201.0, 864.0, 1201.0], "score": 1.0, "text": "where"}, {"category_id": 15, "poly": [1040.0, 1169.0, 1334.0, 1169.0, 1334.0, 1201.0, 1040.0, 1201.0], "score": 0.98, "text": "is the geometric distance,"}, {"category_id": 15, "poly": [1517.0, 1196.0, 1566.0, 1201.0, 1566.0, 1240.0, 1517.0, 1235.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [862.0, 1196.0, 1158.0, 1201.0, 1158.0, 1240.0, 861.0, 1235.0], "score": 1.0, "text": "difference between pixels"}, {"category_id": 15, "poly": [894.0, 1233.0, 1566.0, 1231.0, 1566.0, 1270.0, 894.0, 1272.0], "score": 0.97, "text": "regulate the strength of grouping by geometric distance and"}, {"category_id": 15, "poly": [1179.0, 1196.0, 1229.0, 1201.0, 1229.0, 1240.0, 1179.0, 1235.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [1248.0, 1196.0, 1484.0, 1201.0, 1484.0, 1240.0, 1248.0, 1235.0], "score": 0.99, "text": ", and the coefficients"}, {"category_id": 15, "poly": [887.0, 848.0, 1568.0, 850.0, 1568.0, 889.0, 887.0, 887.0], "score": 0.99, "text": " Humans group shapes by observing the geometric distance"}, {"category_id": 15, "poly": [859.0, 885.0, 1568.0, 882.0, 1568.0, 921.0, 859.0, 924.0], "score": 0.98, "text": " and color similarity of points in space. To mimic this vi-"}, {"category_id": 15, "poly": [864.0, 921.0, 1568.0, 921.0, 1568.0, 953.0, 864.0, 953.0], "score": 0.99, "text": "sual grouping, the adaptive support-weight stereo matching"}, {"category_id": 15, "poly": [864.0, 1054.0, 899.0, 1054.0, 899.0, 1084.0, 864.0, 1084.0], "score": 1.0, "text": "by"}, {"category_id": 15, "poly": [866.0, 956.0, 1350.0, 956.0, 1350.0, 988.0, 866.0, 988.0], "score": 0.98, "text": "algorithm [4] considers a support window"}, {"category_id": 15, "poly": [1389.0, 956.0, 1566.0, 956.0, 1566.0, 988.0, 1389.0, 988.0], "score": 0.98, "text": " centered at the"}, {"category_id": 15, "poly": [952.0, 1022.0, 1370.0, 1022.0, 1370.0, 1054.0, 952.0, 1054.0], "score": 0.98, "text": ". The support weight relating pixels"}, {"category_id": 15, "poly": [1392.0, 1022.0, 1446.0, 1022.0, 1446.0, 1054.0, 1392.0, 1054.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [1466.0, 1022.0, 1566.0, 1022.0, 1566.0, 1054.0, 1466.0, 1054.0], "score": 0.98, "text": "is given"}, {"category_id": 15, "poly": [866.0, 990.0, 1049.0, 990.0, 1049.0, 1022.0, 866.0, 1022.0], "score": 1.0, "text": "pixel of interest"}, {"category_id": 15, "poly": [1069.0, 990.0, 1566.0, 990.0, 1566.0, 1022.0, 1069.0, 1022.0], "score": 1.0, "text": ", and assigns a support weight to each pixel"}, {"category_id": 15, "poly": [862.0, 1948.0, 1568.0, 1950.0, 1568.0, 1989.0, 861.0, 1987.0], "score": 0.98, "text": "vides additional robustness to outliers. Rather than evaluating"}, {"category_id": 15, "poly": [864.0, 1989.0, 1566.0, 1989.0, 1566.0, 2021.0, 864.0, 2021.0], "score": 0.98, "text": "Equation (2) directly, real-time algorithms often approximate"}, {"category_id": 15, "poly": [862.0, 1920.0, 1406.0, 1920.0, 1406.0, 1952.0, 862.0, 1952.0], "score": 0.99, "text": "This limits each of their magnitudes to at most"}, {"category_id": 15, "poly": [1426.0, 1920.0, 1561.0, 1920.0, 1561.0, 1952.0, 1426.0, 1952.0], "score": 0.96, "text": ",whichpro-"}, {"category_id": 15, "poly": [859.0, 1331.0, 1571.0, 1334.0, 1571.0, 1373.0, 859.0, 1370.0], "score": 0.98, "text": " iterative adaptive support-weight algorithm evaluates matching"}, {"category_id": 15, "poly": [859.0, 1464.0, 912.0, 1467.0, 912.0, 1506.0, 859.0, 1503.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [950.0, 1464.0, 1474.0, 1467.0, 1474.0, 1506.0, 950.0, 1503.0], "score": 1.0, "text": ", the initial matching cost is aggregated using"}, {"category_id": 15, "poly": [1442.0, 1370.0, 1530.0, 1370.0, 1530.0, 1402.0, 1442.0, 1402.0], "score": 0.98, "text": ", where"}, {"category_id": 15, "poly": [1197.0, 1437.0, 1527.0, 1437.0, 1527.0, 1469.0, 1197.0, 1469.0], "score": 0.97, "text": ", and their support windows"}, {"category_id": 15, "poly": [866.0, 1402.0, 1539.0, 1402.0, 1539.0, 1435.0, 866.0, 1435.0], "score": 1.0, "text": "denotes a set of matching candidates associated with pixel"}, {"category_id": 15, "poly": [864.0, 1437.0, 1100.0, 1437.0, 1100.0, 1469.0, 864.0, 1469.0], "score": 0.97, "text": "For a pair of pixels"}, {"category_id": 15, "poly": [1122.0, 1437.0, 1176.0, 1437.0, 1176.0, 1469.0, 1122.0, 1469.0], "score": 0.94, "text": " and"}, {"category_id": 15, "poly": [887.0, 1299.0, 1388.0, 1304.0, 1388.0, 1336.0, 887.0, 1331.0], "score": 0.96, "text": " To identify a match for the pixel of interest"}, {"category_id": 15, "poly": [1408.0, 1299.0, 1568.0, 1304.0, 1568.0, 1336.0, 1408.0, 1331.0], "score": 1.0, "text": ", the real-time"}, {"category_id": 15, "poly": [864.0, 1370.0, 1028.0, 1370.0, 1028.0, 1402.0, 864.0, 1402.0], "score": 1.0, "text": "costs between"}, {"category_id": 15, "poly": [1049.0, 1370.0, 1361.0, 1370.0, 1361.0, 1402.0, 1049.0, 1402.0], "score": 0.99, "text": " and every match candidate"}, {"category_id": 15, "poly": [160.0, 1618.0, 836.0, 1623.0, 836.0, 1655.0, 159.0, 1650.0], "score": 0.99, "text": "Most recently, local stereo algorithms based on edge-aware"}, {"category_id": 15, "poly": [127.0, 1650.0, 841.0, 1652.0, 841.0, 1691.0, 127.0, 1689.0], "score": 0.97, "text": " filters were extended to incorporate temporal evidence into"}, {"category_id": 15, "poly": [132.0, 1687.0, 836.0, 1687.0, 836.0, 1719.0, 132.0, 1719.0], "score": 0.97, "text": "the matching process. The method of Richardt et al. [19]"}, {"category_id": 15, "poly": [134.0, 1723.0, 838.0, 1723.0, 838.0, 1753.0, 134.0, 1753.0], "score": 0.99, "text": "employs a variant of the bilateral grid [20] implemented on"}, {"category_id": 15, "poly": [134.0, 1755.0, 838.0, 1755.0, 838.0, 1788.0, 134.0, 1788.0], "score": 0.99, "text": "graphics hardware, which accelerates cost aggregation and"}, {"category_id": 15, "poly": [134.0, 1788.0, 838.0, 1788.0, 838.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "allows for weighted propagation of pixel dissimilarity metrics"}, {"category_id": 15, "poly": [132.0, 1822.0, 838.0, 1822.0, 838.0, 1854.0, 132.0, 1854.0], "score": 0.99, "text": "from previous frames to the current one. Although this method"}, {"category_id": 15, "poly": [129.0, 1856.0, 838.0, 1856.0, 838.0, 1888.0, 129.0, 1888.0], "score": 1.0, "text": " outperforms the baseline frame-to-frame approach, the amount"}, {"category_id": 15, "poly": [132.0, 1888.0, 838.0, 1888.0, 838.0, 1920.0, 132.0, 1920.0], "score": 0.97, "text": "of hardware memory necessary to construct the bilateral grid"}, {"category_id": 15, "poly": [127.0, 1916.0, 841.0, 1918.0, 841.0, 1957.0, 127.0, 1955.0], "score": 0.99, "text": "limits its application to single-channel, i.e., grayscale images "}, {"category_id": 15, "poly": [132.0, 1955.0, 838.0, 1955.0, 838.0, 1985.0, 132.0, 1985.0], "score": 0.99, "text": "only. Hosni et al. [10], on the other hand, reformulated kernels"}, {"category_id": 15, "poly": [132.0, 1989.0, 838.0, 1989.0, 838.0, 2021.0, 132.0, 2021.0], "score": 0.99, "text": "of the guided image filter to operate on both spatial and"}, {"category_id": 15, "poly": [859.0, 809.0, 1307.0, 809.0, 1307.0, 848.0, 859.0, 848.0], "score": 0.99, "text": "A. Two-Pass Spatial Cost Aggregation"}, {"category_id": 15, "poly": [1129.0, 376.0, 1300.0, 376.0, 1300.0, 417.0, 1129.0, 417.0], "score": 0.94, "text": "III. 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"poly": [581, 1713, 694, 1713, 694, 1747, 581, 1747], "score": 0.93, "latex": "{\\bar{p}}=m(p)"}, {"category_id": 14, "poly": [947, 373, 1478, 373, 1478, 454, 947, 454], "score": 0.93, "latex": "\\Lambda^{i}(p,\\bar{p})=\\alpha\\times\\sum_{q\\in\\Omega_{p}}w(p,q)F_{q}^{i-1}\\left|D_{q}^{i-1}-d_{p}\\right|\\,,"}, {"category_id": 13, "poly": [426, 445, 512, 445, 512, 479, 426, 479], "score": 0.93, "latex": "C(p,{\\bar{p}})"}, {"category_id": 13, "poly": [337, 356, 414, 356, 414, 391, 337, 391], "score": 0.93, "latex": "\\mathcal{O}(\\omega^{2})"}, {"category_id": 13, "poly": [1341, 730, 1565, 730, 1565, 765, 1341, 765], "score": 0.92, "latex": "C_{a}(p,\\bar{p})\\gets C(p,\\bar{p})"}, {"category_id": 13, "poly": [629, 1436, 691, 1436, 691, 1470, 629, 1470], "score": 0.92, "latex": "m(p)"}, {"category_id": 13, "poly": [277, 1469, 361, 1469, 361, 1504, 277, 1504], "score": 0.92, "latex": "\\bar{p}\\in S_{p}"}, {"category_id": 14, "poly": [1030, 541, 1398, 541, 1398, 582, 1030, 582], "score": 0.92, "latex": "C^{i}(p,\\bar{p})=C^{0}(p,\\bar{p})+{\\Lambda^{i}}(p,\\bar{p})\\,,"}, {"category_id": 13, "poly": [453, 356, 518, 356, 518, 391, 453, 391], "score": 0.91, "latex": "\\mathcal{O}(\\omega)"}, {"category_id": 14, "poly": [146, 714, 787, 714, 787, 791, 146, 791], "score": 0.91, "latex": "C(p,\\bar{p})\\gets\\frac{(1-\\lambda)\\cdot C(p,\\bar{p})+\\lambda\\cdot w_{t}(p,p_{t-1})\\cdot C_{a}(p,\\bar{p})}{(1-\\lambda)+\\lambda\\cdot w_{t}(p,p_{t-1})},"}, {"category_id": 13, "poly": [1095, 231, 1134, 231, 1134, 270, 1095, 270], "score": 0.9, "latex": "D_{p}^{i}"}, {"category_id": 13, "poly": [1313, 1752, 1447, 1752, 1447, 1783, 1313, 1783], "score": 0.89, "latex": "640~\\times~480"}, {"category_id": 13, "poly": [593, 1782, 627, 1782, 627, 1815, 593, 1815], "score": 0.89, "latex": "F_{p}"}, {"category_id": 13, "poly": [133, 326, 209, 326, 209, 355, 133, 355], "score": 0.88, "latex": "\\omega\\times\\omega"}, {"category_id": 13, "poly": [208, 1089, 236, 1089, 236, 1116, 208, 1116], "score": 0.85, "latex": "\\gamma_{t}"}, {"category_id": 13, "poly": [1466, 769, 1484, 769, 1484, 797, 1466, 797], "score": 0.83, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [133, 935, 177, 935, 177, 963, 133, 963], "score": 0.83, "latex": "p_{t-1}"}, {"category_id": 13, "poly": [608, 1753, 627, 1753, 627, 1779, 608, 1779], "score": 0.81, "latex": "p"}, {"category_id": 13, "poly": [491, 799, 511, 799, 511, 825, 491, 825], "score": 0.81, "latex": "\\lambda"}, {"category_id": 13, "poly": [1018, 770, 1037, 770, 1037, 796, 1018, 796], "score": 0.81, "latex": "p"}, {"category_id": 13, "poly": [1086, 470, 1107, 470, 1107, 491, 1086, 491], "score": 0.8, "latex": "\\alpha"}, {"category_id": 13, "poly": [466, 901, 485, 901, 485, 929, 466, 929], "score": 0.8, "latex": "p"}, {"category_id": 13, "poly": [208, 484, 227, 484, 227, 511, 208, 511], "score": 0.79, "latex": "p"}, {"category_id": 13, "poly": [462, 1443, 480, 1443, 480, 1468, 462, 1468], "score": 0.77, "latex": "p"}, {"category_id": 13, "poly": [266, 514, 288, 514, 288, 544, 266, 544], "score": 0.77, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [816, 1716, 836, 1716, 836, 1746, 816, 1746], "score": 0.73, "latex": "\\bar{p}"}, {"category_id": 13, "poly": [132, 405, 154, 405, 154, 432, 132, 432], "score": 0.27, "latex": "B"}, {"category_id": 13, "poly": [862, 160, 887, 160, 887, 187, 862, 187], "score": 0.26, "latex": "D"}, {"category_id": 15, "poly": [887.0, 852.0, 1568.0, 855.0, 1568.0, 894.0, 887.0, 891.0], "score": 0.98, "text": " The speed and accuracy of real-time stereo matching al-"}, {"category_id": 15, "poly": [864.0, 891.0, 1566.0, 891.0, 1566.0, 924.0, 864.0, 924.0], "score": 0.99, "text": "gorithms are traditionally demonstrated using still-frame im-"}, {"category_id": 15, "poly": [859.0, 921.0, 1571.0, 919.0, 1571.0, 958.0, 859.0, 960.0], "score": 0.97, "text": " ages from the Middlebury stereo benchmark [1], [2]. Still"}, {"category_id": 15, "poly": [862.0, 956.0, 1568.0, 958.0, 1568.0, 990.0, 862.0, 988.0], "score": 0.99, "text": "frames, however, are insufficient for evaluating stereo match-"}, {"category_id": 15, "poly": [864.0, 992.0, 1571.0, 992.0, 1571.0, 1024.0, 864.0, 1024.0], "score": 1.0, "text": "ing algorithms that incorporate frame-to-frame prediction to"}, {"category_id": 15, "poly": [864.0, 1027.0, 1568.0, 1027.0, 1568.0, 1059.0, 864.0, 1059.0], "score": 0.97, "text": "enhance matching accuracy. An alternative approach is to"}, {"category_id": 15, "poly": [864.0, 1059.0, 1566.0, 1059.0, 1566.0, 1089.0, 864.0, 1089.0], "score": 0.99, "text": "use a stereo video sequence with a ground truth disparity"}, {"category_id": 15, "poly": [862.0, 1091.0, 1566.0, 1091.0, 1566.0, 1123.0, 862.0, 1123.0], "score": 1.0, "text": "for each frame. Obtaining the ground truth disparity of real"}, {"category_id": 15, "poly": [866.0, 1125.0, 1566.0, 1125.0, 1566.0, 1157.0, 866.0, 1157.0], "score": 0.98, "text": "world video sequences is a difficult undertaking due to the"}, {"category_id": 15, "poly": [859.0, 1153.0, 1568.0, 1155.0, 1568.0, 1194.0, 859.0, 1192.0], "score": 0.99, "text": "high frame rate of video and limitations in depth sensing-"}, {"category_id": 15, "poly": [864.0, 1192.0, 1568.0, 1192.0, 1568.0, 1224.0, 864.0, 1224.0], "score": 0.99, "text": "technology. To address the need for stereo video with ground"}, {"category_id": 15, "poly": [864.0, 1224.0, 1568.0, 1224.0, 1568.0, 1256.0, 864.0, 1256.0], "score": 0.99, "text": "truth disparities, five pairs of synthetic stereo video sequences"}, {"category_id": 15, "poly": [864.0, 1258.0, 1568.0, 1258.0, 1568.0, 1290.0, 864.0, 1290.0], "score": 0.99, "text": "of a computer-generated scene were given in [19]. While these"}, {"category_id": 15, "poly": [864.0, 1290.0, 1566.0, 1290.0, 1566.0, 1322.0, 864.0, 1322.0], "score": 1.0, "text": "videos incorporate a sufficient amount of movement variation,"}, {"category_id": 15, "poly": [862.0, 1325.0, 1568.0, 1325.0, 1568.0, 1357.0, 862.0, 1357.0], "score": 0.99, "text": "they were generated from relatively simple models using low-"}, {"category_id": 15, "poly": [862.0, 1359.0, 1571.0, 1359.0, 1571.0, 1389.0, 862.0, 1389.0], "score": 0.99, "text": "resolution rendering, and they do not provide occlusion or"}, {"category_id": 15, "poly": [862.0, 1386.0, 1088.0, 1394.0, 1087.0, 1426.0, 861.0, 1418.0], "score": 0.98, "text": "discontinuity maps."}, {"category_id": 15, "poly": [129.0, 156.0, 839.0, 158.0, 838.0, 197.0, 129.0, 195.0], "score": 0.99, "text": "the matching cost by performing two-pass aggregation using"}, {"category_id": 15, "poly": [130.0, 188.0, 841.0, 193.0, 841.0, 229.0, 129.0, 225.0], "score": 0.98, "text": "two orthogonal 1D windows [5], [6], [8]. The two-pass method "}, {"category_id": 15, "poly": [129.0, 225.0, 841.0, 222.0, 841.0, 261.0, 129.0, 264.0], "score": 0.99, "text": "first aggregates matching costs in the vertical direction, and"}, {"category_id": 15, "poly": [134.0, 261.0, 838.0, 261.0, 838.0, 293.0, 134.0, 293.0], "score": 0.99, "text": "then computes a weighted sum of the aggregated costs in the"}, {"category_id": 15, "poly": [132.0, 291.0, 838.0, 291.0, 838.0, 330.0, 132.0, 330.0], "score": 0.99, "text": "horizontal direction. Given that support regions are of size"}, {"category_id": 15, "poly": [136.0, 360.0, 336.0, 360.0, 336.0, 392.0, 136.0, 392.0], "score": 0.99, "text": "aggregation from"}, {"category_id": 15, "poly": [415.0, 360.0, 452.0, 360.0, 452.0, 392.0, 415.0, 392.0], "score": 0.98, "text": "to"}, {"category_id": 15, "poly": [210.0, 321.0, 836.0, 321.0, 836.0, 360.0, 210.0, 360.0], "score": 0.98, "text": ", the two-pass method reduces the complexity of cost"}, {"category_id": 15, "poly": [887.0, 1416.0, 1571.0, 1419.0, 1571.0, 1458.0, 887.0, 1455.0], "score": 0.98, "text": " To evaluate the performance of temporal aggregation, a"}, {"category_id": 15, "poly": [862.0, 1453.0, 1566.0, 1453.0, 1566.0, 1485.0, 862.0, 1485.0], "score": 0.98, "text": "new synthetic stereo video sequence is introduced along with"}, {"category_id": 15, "poly": [862.0, 1490.0, 1566.0, 1487.0, 1566.0, 1519.0, 862.0, 1522.0], "score": 0.99, "text": "corresponding disparity maps, occlusion maps, and disconti-"}, {"category_id": 15, "poly": [862.0, 1519.0, 1571.0, 1519.0, 1571.0, 1558.0, 862.0, 1558.0], "score": 0.99, "text": "nuity maps for evaluating the performance of temporal stereo"}, {"category_id": 15, "poly": [864.0, 1556.0, 1568.0, 1556.0, 1568.0, 1588.0, 864.0, 1588.0], "score": 1.0, "text": "matching algorithms. To create the video sequence, a complex"}, {"category_id": 15, "poly": [864.0, 1590.0, 1568.0, 1590.0, 1568.0, 1620.0, 864.0, 1620.0], "score": 0.99, "text": "scene was constructed using Google Sketchup and a pair"}, {"category_id": 15, "poly": [864.0, 1622.0, 1568.0, 1622.0, 1568.0, 1655.0, 864.0, 1655.0], "score": 0.99, "text": "of animated paths were rendered photorealistically using the"}, {"category_id": 15, "poly": [859.0, 1650.0, 1571.0, 1652.0, 1571.0, 1691.0, 859.0, 1689.0], "score": 0.99, "text": " Kerkythea rendering software. Realistic material properties"}, {"category_id": 15, "poly": [864.0, 1689.0, 1566.0, 1689.0, 1566.0, 1721.0, 864.0, 1721.0], "score": 1.0, "text": "were used to give surfaces a natural-looking appearance by"}, {"category_id": 15, "poly": [864.0, 1723.0, 1566.0, 1723.0, 1566.0, 1755.0, 864.0, 1755.0], "score": 0.98, "text": "adjusting their specularity, reflectance, and diffusion. The"}, {"category_id": 15, "poly": [864.0, 1788.0, 1568.0, 1788.0, 1568.0, 1820.0, 864.0, 1820.0], "score": 1.0, "text": "frame rate of 30 frames per second, and a duration of 4"}, {"category_id": 15, "poly": [862.0, 1817.0, 1568.0, 1820.0, 1568.0, 1859.0, 861.0, 1856.0], "score": 0.98, "text": "seconds. In addition to performing photorealistic rendering."}, {"category_id": 15, "poly": [864.0, 1856.0, 1568.0, 1856.0, 1568.0, 1888.0, 864.0, 1888.0], "score": 0.99, "text": "depth renders of both video sequences were also generated and"}, {"category_id": 15, "poly": [864.0, 1888.0, 1566.0, 1888.0, 1566.0, 1920.0, 864.0, 1920.0], "score": 0.98, "text": "converted to ground truth disparity for the stereo video. The"}, {"category_id": 15, "poly": [862.0, 1920.0, 1564.0, 1920.0, 1564.0, 1952.0, 862.0, 1952.0], "score": 0.99, "text": "video sequences and ground truth data have been made avail-"}, {"category_id": 15, "poly": [862.0, 1950.0, 1566.0, 1953.0, 1566.0, 1985.0, 862.0, 1982.0], "score": 0.99, "text": "able at http://mc2.unl.edu/current-research"}, {"category_id": 15, "poly": [866.0, 1989.0, 1566.0, 1989.0, 1566.0, 2019.0, 866.0, 2019.0], "score": 0.98, "text": "/ image-processing/. Figure 2 shows two sample frames"}, {"category_id": 15, "poly": [862.0, 1755.0, 1312.0, 1755.0, 1312.0, 1788.0, 862.0, 1788.0], "score": 0.97, "text": "video sequence has a resolution of "}, {"category_id": 15, "poly": [1448.0, 1755.0, 1566.0, 1755.0, 1566.0, 1788.0, 1448.0, 1788.0], "score": 0.99, "text": "pixels,a"}, {"category_id": 15, "poly": [889.0, 197.0, 1566.0, 199.0, 1566.0, 238.0, 889.0, 236.0], "score": 1.0, "text": "Once the first iteration of stereo matching is complete,"}, {"category_id": 15, "poly": [864.0, 268.0, 1566.0, 268.0, 1566.0, 300.0, 864.0, 300.0], "score": 0.99, "text": "subsequent iterations. This is done by penalizing disparities"}, {"category_id": 15, "poly": [864.0, 302.0, 1568.0, 302.0, 1568.0, 335.0, 864.0, 335.0], "score": 1.0, "text": "that deviate from their expected values. The penalty function"}, {"category_id": 15, "poly": [862.0, 337.0, 996.0, 337.0, 996.0, 369.0, 862.0, 369.0], "score": 0.97, "text": "is given by"}, {"category_id": 15, "poly": [864.0, 236.0, 1094.0, 236.0, 1094.0, 268.0, 864.0, 268.0], "score": 0.96, "text": "disparityestimates"}, {"category_id": 15, "poly": [1135.0, 236.0, 1568.0, 236.0, 1568.0, 268.0, 1135.0, 268.0], "score": 0.97, "text": " can be used to guide matching in"}, {"category_id": 15, "poly": [157.0, 1366.0, 839.0, 1368.0, 838.0, 1407.0, 157.0, 1405.0], "score": 1.0, "text": "Having performed temporal cost aggregation, matches are"}, {"category_id": 15, "poly": [134.0, 1405.0, 834.0, 1405.0, 834.0, 1437.0, 134.0, 1437.0], "score": 0.99, "text": "determined using the Winner-Takes-All (WTA) match selec-"}, {"category_id": 15, "poly": [132.0, 1506.0, 374.0, 1506.0, 374.0, 1538.0, 132.0, 1538.0], "score": 1.0, "text": "cost, and is given by"}, {"category_id": 15, "poly": [692.0, 1439.0, 834.0, 1439.0, 834.0, 1471.0, 692.0, 1471.0], "score": 0.99, "text": ", is the can-"}, {"category_id": 15, "poly": [134.0, 1474.0, 276.0, 1474.0, 276.0, 1506.0, 134.0, 1506.0], "score": 0.98, "text": "didate pixel"}, {"category_id": 15, "poly": [362.0, 1474.0, 836.0, 1474.0, 836.0, 1506.0, 362.0, 1506.0], "score": 0.99, "text": " characterized by the minimum matching"}, {"category_id": 15, "poly": [134.0, 1439.0, 461.0, 1439.0, 461.0, 1471.0, 134.0, 1471.0], "score": 1.0, "text": "tion criteria. The match for"}, {"category_id": 15, "poly": [481.0, 1439.0, 628.0, 1439.0, 628.0, 1471.0, 481.0, 1471.0], "score": 0.96, "text": ", denoted as"}, {"category_id": 15, "poly": [134.0, 548.0, 838.0, 545.0, 838.0, 577.0, 134.0, 580.0], "score": 0.99, "text": "aggregation routine is exectuted. At each time instance, the"}, {"category_id": 15, "poly": [134.0, 614.0, 834.0, 614.0, 834.0, 646.0, 134.0, 646.0], "score": 1.0, "text": "weighted summation of costs obtained in the previous frames."}, {"category_id": 15, "poly": [132.0, 646.0, 838.0, 644.0, 838.0, 676.0, 132.0, 678.0], "score": 1.0, "text": "During temporal aggregation, the auxiliary cost is merged with"}, {"category_id": 15, "poly": [132.0, 678.0, 675.0, 681.0, 674.0, 713.0, 132.0, 710.0], "score": 0.99, "text": "the cost obtained from the current frame using"}, {"category_id": 15, "poly": [134.0, 580.0, 549.0, 580.0, 549.0, 612.0, 134.0, 612.0], "score": 1.0, "text": "algorithm stores an auxiliary cost"}, {"category_id": 15, "poly": [649.0, 580.0, 841.0, 580.0, 841.0, 612.0, 649.0, 612.0], "score": 0.96, "text": "which holds a"}, {"category_id": 15, "poly": [157.0, 445.0, 425.0, 442.0, 425.0, 481.0, 157.0, 484.0], "score": 0.98, "text": " Once aggregated costs"}, {"category_id": 15, "poly": [513.0, 445.0, 838.0, 442.0, 838.0, 481.0, 513.0, 484.0], "score": 0.96, "text": " have been computed for all"}, {"category_id": 15, "poly": [132.0, 481.0, 207.0, 481.0, 207.0, 513.0, 132.0, 513.0], "score": 1.0, "text": "pixels"}, {"category_id": 15, "poly": [228.0, 481.0, 838.0, 481.0, 838.0, 513.0, 228.0, 513.0], "score": 0.97, "text": " in the reference image and their respective matching"}, {"category_id": 15, "poly": [134.0, 516.0, 265.0, 516.0, 265.0, 548.0, 134.0, 548.0], "score": 1.0, "text": "candidates"}, {"category_id": 15, "poly": [289.0, 516.0, 838.0, 516.0, 838.0, 548.0, 289.0, 548.0], "score": 0.98, "text": " in the target image, a single-pass temporal"}, {"category_id": 15, "poly": [132.0, 1116.0, 841.0, 1116.0, 841.0, 1155.0, 132.0, 1155.0], "score": 0.99, "text": "in the temporal dimension. 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Next, the penalty"}, {"category_id": 15, "poly": [134.0, 866.0, 838.0, 866.0, 838.0, 898.0, 134.0, 898.0], "score": 0.99, "text": "temporal domain. 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RESULTS"}, {"category_id": 15, "poly": [155.0, 401.0, 481.0, 406.0, 480.0, 445.0, 155.0, 440.0], "score": 0.99, "text": "Temporal cost aggregation"}, {"category_id": 15, "poly": [129.0, 1325.0, 718.0, 1327.0, 718.0, 1366.0, 129.0, 1363.0], "score": 0.99, "text": "C. 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results of temporal stereo matching are given in Figure"}, {"category_id": 15, "poly": [132.0, 1657.0, 838.0, 1657.0, 838.0, 1689.0, 132.0, 1689.0], "score": 0.99, "text": "stereo matching methods, improvements are negligible when"}, {"category_id": 15, "poly": [132.0, 1691.0, 838.0, 1691.0, 838.0, 1723.0, 132.0, 1723.0], "score": 0.99, "text": "no noise is added to the images [10], [19]. This is largely due"}, {"category_id": 15, "poly": [132.0, 1723.0, 836.0, 1723.0, 836.0, 1753.0, 132.0, 1753.0], "score": 0.98, "text": "to the fact that the video used to evaluate these methods is"}, {"category_id": 15, "poly": [129.0, 1753.0, 838.0, 1751.0, 839.0, 1790.0, 129.0, 1792.0], "score": 0.99, "text": " computer generated with very little noise to start with, thus"}, {"category_id": 15, "poly": [134.0, 1790.0, 836.0, 1790.0, 836.0, 1822.0, 134.0, 1822.0], "score": 0.99, "text": "the noise suppression achieved with temporal stereo matching"}, {"category_id": 15, "poly": [132.0, 1817.0, 839.0, 1822.0, 838.0, 1859.0, 132.0, 1854.0], "score": 0.99, "text": "shows little to no improvement over methods that operate on"}, {"category_id": 15, "poly": [130.0, 1856.0, 319.0, 1859.0, 318.0, 1891.0, 129.0, 1888.0], "score": 0.99, "text": "pairs of images."}, {"category_id": 15, "poly": [187.0, 1590.0, 248.0, 1590.0, 248.0, 1622.0, 187.0, 1622.0], "score": 0.87, "text": ",and"}, {"category_id": 15, "poly": [303.0, 1590.0, 838.0, 1590.0, 838.0, 1622.0, 303.0, 1622.0], "score": 0.98, "text": ". Each performance plot is given as a function"}, {"category_id": 15, "poly": [127.0, 1551.0, 786.0, 1554.0, 786.0, 1593.0, 127.0, 1590.0], "score": 0.98, "text": " 3 for uniform additive noise confined to the ranges of"}, {"category_id": 15, "poly": [134.0, 1622.0, 435.0, 1622.0, 435.0, 1655.0, 134.0, 1655.0], "score": 0.99, "text": "of the feedback coefficient"}, {"category_id": 15, "poly": [456.0, 1622.0, 836.0, 1622.0, 836.0, 1655.0, 456.0, 1655.0], "score": 0.97, "text": ". As with the majority of temporal"}, {"category_id": 15, "poly": [134.0, 1359.0, 834.0, 1359.0, 834.0, 1391.0, 134.0, 1391.0], "score": 0.99, "text": "Figure 2: Two sample frames from the synthetic video se-"}, {"category_id": 15, "poly": [573.0, 1418.0, 836.0, 1421.0, 836.0, 1460.0, 573.0, 1457.0], "score": 1.0, "text": "row), and discontinuity"}, {"category_id": 15, "poly": [134.0, 1393.0, 229.0, 1393.0, 229.0, 1425.0, 134.0, 1425.0], "score": 0.96, "text": "quence ("}, {"category_id": 15, "poly": [267.0, 1393.0, 836.0, 1393.0, 836.0, 1425.0, 267.0, 1425.0], "score": 0.98, "text": "row), along with their corresponding ground truth"}, {"category_id": 15, "poly": [127.0, 1456.0, 199.0, 1450.0, 199.0, 1489.0, 128.0, 1495.0], "score": 0.91, "text": "map ("}, {"category_id": 15, "poly": [241.0, 1456.0, 309.0, 1450.0, 310.0, 1489.0, 241.0, 1495.0], "score": 1.0, "text": "row)."}, {"category_id": 15, "poly": [129.0, 1418.0, 245.0, 1421.0, 245.0, 1460.0, 129.0, 1457.0], "score": 0.93, "text": " disparity "}, {"category_id": 15, "poly": [290.0, 1418.0, 531.0, 1421.0, 531.0, 1460.0, 290.0, 1457.0], "score": 1.0, "text": "row), occlusion map ("}, {"category_id": 15, "poly": [159.0, 1888.0, 836.0, 1888.0, 836.0, 1920.0, 159.0, 1920.0], "score": 0.99, "text": " Significant improvements in accuracy can be seen in Figure"}, {"category_id": 15, "poly": [132.0, 1950.0, 839.0, 1955.0, 838.0, 1987.0, 132.0, 1982.0], "score": 1.0, "text": "the effect of noise in the current frame is reduced by increasing"}, {"category_id": 15, "poly": [134.0, 1920.0, 480.0, 1920.0, 480.0, 1952.0, 134.0, 1952.0], "score": 0.99, "text": "3 when the noise has ranges of"}, {"category_id": 15, "poly": [535.0, 1920.0, 590.0, 1920.0, 590.0, 1952.0, 535.0, 1952.0], "score": 0.92, "text": " and"}, {"category_id": 15, "poly": [645.0, 1920.0, 836.0, 1920.0, 836.0, 1952.0, 645.0, 1952.0], "score": 0.96, "text": ". In this scenario,"}, {"category_id": 15, "poly": [676.0, 1989.0, 838.0, 1989.0, 838.0, 2019.0, 676.0, 2019.0], "score": 0.98, "text": "has the effect"}, {"category_id": 15, "poly": [134.0, 1989.0, 409.0, 1989.0, 409.0, 2019.0, 134.0, 2019.0], "score": 1.0, "text": "the feedback coefficient"}, {"category_id": 15, "poly": [431.0, 1989.0, 654.0, 1989.0, 654.0, 2019.0, 431.0, 2019.0], "score": 0.97, "text": ". This increasing of"}, {"category_id": 15, "poly": [864.0, 1920.0, 1566.0, 1920.0, 1566.0, 1952.0, 864.0, 1952.0], "score": 0.98, "text": "of averaging out noise in the per-pixel costs by selecting"}, {"category_id": 15, "poly": [861.0, 1950.0, 1566.0, 1948.0, 1566.0, 1987.0, 862.0, 1989.0], "score": 0.98, "text": "matches based more heavily upon the auxiliary cost, which"}, {"category_id": 15, "poly": [862.0, 1989.0, 1568.0, 1989.0, 1568.0, 2021.0, 862.0, 2021.0], "score": 0.99, "text": "is essentially a much more stable running average of the cost"}, {"category_id": 15, "poly": [864.0, 1788.0, 1564.0, 1785.0, 1564.0, 1817.0, 864.0, 1820.0], "score": 0.99, "text": "responding to the smallest mean squared error (MSE) of the"}, {"category_id": 15, "poly": [864.0, 1822.0, 1427.0, 1822.0, 1427.0, 1854.0, 864.0, 1854.0], "score": 0.99, "text": "disparity estimates for a range of noise strengths."}, {"category_id": 15, "poly": [862.0, 1748.0, 1488.0, 1753.0, 1488.0, 1785.0, 861.0, 1781.0], "score": 0.99, "text": "Figure 4: Optimal values of the feedback coefficient "}, {"category_id": 15, "poly": [1511.0, 1748.0, 1561.0, 1753.0, 1561.0, 1785.0, 1511.0, 1781.0], "score": 0.96, "text": "cor-"}, {"category_id": 15, "poly": [864.0, 866.0, 1566.0, 866.0, 1566.0, 898.0, 864.0, 898.0], "score": 0.99, "text": "Figure 3: Performance of temporal matching at different levels"}, {"category_id": 15, "poly": [864.0, 935.0, 1566.0, 933.0, 1566.0, 965.0, 864.0, 967.0], "score": 0.98, "text": "squared error (MSE) of disparities is plotted versus the values"}, {"category_id": 15, "poly": [864.0, 1001.0, 1492.0, 1001.0, 1492.0, 1031.0, 864.0, 1031.0], "score": 0.99, "text": "values of MSE obtained without temporal aggregation."}, {"category_id": 15, "poly": [864.0, 901.0, 1294.0, 901.0, 1294.0, 933.0, 864.0, 933.0], "score": 0.99, "text": "of uniformly distributed image noise"}, {"category_id": 15, "poly": [1484.0, 901.0, 1568.0, 901.0, 1568.0, 933.0, 1484.0, 933.0], "score": 0.99, "text": ".Mean"}, {"category_id": 15, "poly": [864.0, 967.0, 1175.0, 967.0, 1175.0, 999.0, 864.0, 999.0], "score": 0.99, "text": "of the feedback coefficient"}, {"category_id": 15, "poly": [1196.0, 967.0, 1568.0, 967.0, 1568.0, 999.0, 1196.0, 999.0], "score": 0.99, "text": ". Dashed lines correspond to the"}, {"category_id": 15, "poly": [857.0, 1061.0, 1566.0, 1068.0, 1566.0, 1107.0, 857.0, 1100.0], "score": 0.99, "text": " Table I: Parameters used in the evaluation of real-time tempo-"}, {"category_id": 15, "poly": [859.0, 1102.0, 1093.0, 1105.0, 1092.0, 1137.0, 859.0, 1134.0], "score": 1.0, "text": "ral stereo matching."}, {"category_id": 15, "poly": [1178.0, 151.0, 1282.0, 151.0, 1282.0, 186.0, 1178.0, 186.0], "score": 1.0, "text": "Noise: \u00b10"}, {"category_id": 15, "poly": [1178.0, 151.0, 1282.0, 151.0, 1282.0, 186.0, 1178.0, 186.0], "score": 1.0, "text": "Noise: \u00b10"}], "page_info": {"page_no": 3, "height": 2200, "width": 1700}}, {"layout_dets": [{"category_id": 5, "poly": [880.81298828125, 613.750244140625, 1552.5638427734375, 613.750244140625, 1552.5638427734375, 855.9174194335938, 880.81298828125, 855.9174194335938], "score": 0.9999957084655762}, {"category_id": 1, "poly": [862.7925415039062, 158.05548095703125, 1569.6671142578125, 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259.0], "score": 0.99, "text": "evaluated per second, as shown in Table I1. It is also the second"}, {"category_id": 15, "poly": [862.0, 261.0, 1568.0, 261.0, 1568.0, 293.0, 862.0, 293.0], "score": 0.99, "text": "most accurate real-time method in terms of error rate, as"}, {"category_id": 15, "poly": [864.0, 296.0, 1564.0, 296.0, 1564.0, 325.0, 864.0, 325.0], "score": 1.0, "text": "measured using the Middlebury stereo evaluation benchmark."}, {"category_id": 15, "poly": [859.0, 323.0, 1568.0, 325.0, 1568.0, 358.0, 859.0, 355.0], "score": 0.98, "text": " It should be noted that it is difficult to establish an unbiased"}, {"category_id": 15, "poly": [862.0, 358.0, 1566.0, 358.0, 1566.0, 390.0, 862.0, 390.0], "score": 1.0, "text": "metric for speed comparisons, as the architecture, number of"}, {"category_id": 15, "poly": [866.0, 394.0, 1568.0, 394.0, 1568.0, 426.0, 866.0, 426.0], "score": 0.98, "text": "cores, and clock speed of graphics hardware used are not"}, {"category_id": 15, "poly": [862.0, 424.0, 1259.0, 429.0, 1259.0, 461.0, 861.0, 456.0], "score": 0.99, "text": "consistent across implementations."}, {"category_id": 15, "poly": [889.0, 1061.0, 1571.0, 1061.0, 1571.0, 1100.0, 889.0, 1100.0], "score": 1.0, "text": "While the majority of stereo matching algorithms focus"}, {"category_id": 15, "poly": [859.0, 1093.0, 1571.0, 1095.0, 1571.0, 1134.0, 859.0, 1132.0], "score": 0.99, "text": " on achieving high accuracy on still images, the volume of"}, {"category_id": 15, "poly": [862.0, 1130.0, 1564.0, 1130.0, 1564.0, 1162.0, 862.0, 1162.0], "score": 0.99, "text": "research aimed at recovery of temporally consistent disparity"}, {"category_id": 15, "poly": [862.0, 1162.0, 1568.0, 1162.0, 1568.0, 1201.0, 862.0, 1201.0], "score": 0.99, "text": "maps remains disproportionally small. This paper introduces"}, {"category_id": 15, "poly": [862.0, 1196.0, 1568.0, 1196.0, 1568.0, 1235.0, 862.0, 1235.0], "score": 0.98, "text": "an efficient temporal cost aggregation scheme that can easily"}, {"category_id": 15, "poly": [859.0, 1226.0, 1571.0, 1228.0, 1571.0, 1267.0, 859.0, 1265.0], "score": 0.99, "text": "be combined with conventional spatial cost aggregation to"}, {"category_id": 15, "poly": [864.0, 1265.0, 1568.0, 1265.0, 1568.0, 1297.0, 864.0, 1297.0], "score": 1.0, "text": "improve the accuracy of stereo matching when operating on"}, {"category_id": 15, "poly": [864.0, 1297.0, 1568.0, 1297.0, 1568.0, 1329.0, 864.0, 1329.0], "score": 0.99, "text": "video sequences. A synthetic video sequence, along with"}, {"category_id": 15, "poly": [864.0, 1331.0, 1568.0, 1331.0, 1568.0, 1364.0, 864.0, 1364.0], "score": 0.99, "text": "ground truth disparity data, was generated to evaluate the"}, {"category_id": 15, "poly": [862.0, 1361.0, 1571.0, 1361.0, 1571.0, 1400.0, 862.0, 1400.0], "score": 0.98, "text": "performance of the proposed method. It was shown that"}, {"category_id": 15, "poly": [864.0, 1398.0, 1571.0, 1398.0, 1571.0, 1430.0, 864.0, 1430.0], "score": 0.98, "text": "temporal aggregation is significantly more robust to noise than"}, {"category_id": 15, "poly": [862.0, 1430.0, 1497.0, 1430.0, 1497.0, 1462.0, 862.0, 1462.0], "score": 0.99, "text": "a method that only considers the current stereo frames."}, {"category_id": 15, "poly": [157.0, 1517.0, 838.0, 1517.0, 838.0, 1556.0, 157.0, 1556.0], "score": 0.99, "text": "The optimal value of the feedback coefficient is largely"}, {"category_id": 15, "poly": [134.0, 1554.0, 836.0, 1554.0, 836.0, 1584.0, 134.0, 1584.0], "score": 0.97, "text": "dependent on the noise being added to the image. Figure 4"}, {"category_id": 15, "poly": [132.0, 1655.0, 838.0, 1655.0, 838.0, 1684.0, 132.0, 1684.0], "score": 0.99, "text": "rely on the auxiliary cost when noise is high and it is more"}, {"category_id": 15, "poly": [132.0, 1684.0, 839.0, 1689.0, 838.0, 1721.0, 132.0, 1716.0], "score": 0.98, "text": "beneficial to rely on the current cost when noise is low. Figure"}, {"category_id": 15, "poly": [132.0, 1719.0, 839.0, 1723.0, 838.0, 1755.0, 132.0, 1751.0], "score": 1.0, "text": "5 illustrates the improvements that are achieved when applying"}, {"category_id": 15, "poly": [134.0, 1755.0, 836.0, 1755.0, 836.0, 1785.0, 134.0, 1785.0], "score": 0.98, "text": "temporal stereo matching to a particular pair of frames in the"}, {"category_id": 15, "poly": [134.0, 1788.0, 834.0, 1788.0, 834.0, 1820.0, 134.0, 1820.0], "score": 1.0, "text": "synthetic video sequence. Clearly, the noise in the disparity"}, {"category_id": 15, "poly": [134.0, 1822.0, 836.0, 1822.0, 836.0, 1854.0, 134.0, 1854.0], "score": 0.99, "text": "map is drastically reduced when temporal stereo matching is"}, {"category_id": 15, "poly": [132.0, 1856.0, 196.0, 1856.0, 196.0, 1886.0, 132.0, 1886.0], "score": 1.0, "text": "used."}, {"category_id": 15, "poly": [132.0, 1620.0, 165.0, 1620.0, 165.0, 1652.0, 132.0, 1652.0], "score": 0.99, "text": "to"}, {"category_id": 15, "poly": [220.0, 1620.0, 838.0, 1620.0, 838.0, 1652.0, 220.0, 1652.0], "score": 0.98, "text": ". As intuition would suggest, it is more beneficial to"}, {"category_id": 15, "poly": [127.0, 1584.0, 461.0, 1581.0, 461.0, 1620.0, 127.0, 1623.0], "score": 0.96, "text": " shows the optimal values of"}, {"category_id": 15, "poly": [483.0, 1584.0, 794.0, 1581.0, 794.0, 1620.0, 483.0, 1623.0], "score": 0.99, "text": "for noise ranging between"}, {"category_id": 15, "poly": [134.0, 160.0, 836.0, 160.0, 836.0, 192.0, 134.0, 192.0], "score": 0.99, "text": "over the most recent frames. By maintaining a reasonably"}, {"category_id": 15, "poly": [134.0, 229.0, 836.0, 229.0, 836.0, 261.0, 134.0, 261.0], "score": 0.98, "text": "edges, essentially reducing over-smoothing of a pixel's dis-"}, {"category_id": 15, "poly": [132.0, 261.0, 838.0, 261.0, 838.0, 293.0, 132.0, 293.0], "score": 0.99, "text": "parity when a pixel transitions from one depth to another in"}, {"category_id": 15, "poly": [130.0, 293.0, 354.0, 296.0, 353.0, 328.0, 129.0, 325.0], "score": 1.0, "text": "subsequent frames."}, {"category_id": 15, "poly": [134.0, 192.0, 300.0, 192.0, 300.0, 225.0, 134.0, 225.0], "score": 0.93, "text": "high value of"}, {"category_id": 15, "poly": [330.0, 192.0, 836.0, 192.0, 836.0, 225.0, 330.0, 225.0], "score": 0.99, "text": ", the auxiliary cost also preserves temporal"}, {"category_id": 15, "poly": [132.0, 1345.0, 836.0, 1348.0, 836.0, 1382.0, 132.0, 1380.0], "score": 1.0, "text": "Figure 5: A comparison of stereo matching without temporal"}, {"category_id": 15, "poly": [132.0, 1382.0, 834.0, 1382.0, 834.0, 1414.0, 132.0, 1414.0], "score": 0.98, "text": "cost aggregation (top\uff09 and with temporal cost aggregation"}, {"category_id": 15, "poly": [134.0, 1416.0, 836.0, 1416.0, 836.0, 1446.0, 134.0, 1446.0], "score": 0.98, "text": "(bottom) for a single frame in the synthetic video sequence"}, {"category_id": 15, "poly": [134.0, 1448.0, 337.0, 1446.0, 337.0, 1478.0, 134.0, 1480.0], "score": 0.98, "text": "where the noise is"}, {"category_id": 15, "poly": [392.0, 1448.0, 735.0, 1446.0, 735.0, 1478.0, 392.0, 1480.0], "score": 0.99, "text": "and the feedback coefficient is"}, {"category_id": 15, "poly": [896.0, 855.0, 1324.0, 857.0, 1323.0, 896.0, 896.0, 894.0], "score": 0.95, "text": "1I Millions of Disparity Estimates per Second."}, {"category_id": 15, "poly": [903.0, 912.0, 1550.0, 912.0, 1550.0, 944.0, 903.0, 944.0], "score": 0.99, "text": "3 As measured by the Middlebury stereo performance benchmark using"}, {"category_id": 15, "poly": [901.0, 887.0, 1002.0, 887.0, 1002.0, 919.0, 901.0, 919.0], "score": 0.99, "text": "2Assumes"}, {"category_id": 15, "poly": [1106.0, 887.0, 1404.0, 887.0, 1404.0, 919.0, 1106.0, 919.0], "score": 0.98, "text": "images with 32 disparity levels."}, {"category_id": 15, "poly": [915.0, 937.0, 1036.0, 937.0, 1036.0, 969.0, 915.0, 969.0], "score": 0.96, "text": "the avgerage"}, {"category_id": 15, "poly": [1060.0, 937.0, 1192.0, 937.0, 1192.0, 969.0, 1060.0, 969.0], "score": 0.96, "text": "of bad pixels."}, {"category_id": 15, "poly": [873.0, 1515.0, 1571.0, 1515.0, 1571.0, 1545.0, 873.0, 1545.0], "score": 0.97, "text": "[1] D. 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\ No newline at end of file \ No newline at end of file
demo/magic_pdf_parse_main.py
View file @ 4dcf31b6
import os import copy
import json import json
import copy import os
from loguru import logger from loguru import logger
from magic_pdf.libs.draw_bbox import draw_layout_bbox, draw_span_bbox from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.pipe.UNIPipe import UNIPipe from magic_pdf.libs.draw_bbox import draw_layout_bbox, draw_span_bbox
from magic_pdf.pipe.OCRPipe import OCRPipe from magic_pdf.pipe.OCRPipe import OCRPipe
from magic_pdf.pipe.TXTPipe import TXTPipe from magic_pdf.pipe.TXTPipe import TXTPipe
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter from magic_pdf.pipe.UNIPipe import UNIPipe
# todo: 设备类型选择 （？）
# todo: 设备类型选择 （？）
def json_md_dump( def json_md_dump(
pipe, pipe,
md_writer, md_writer,
pdf_name, pdf_name,
content_list, content_list,
md_content, md_content,
orig_model_list, orig_model_list,
): ):
# 写入模型结果到 model.json # 写入模型结果到 model.json
md_writer.write( md_writer.write_string(
content=json.dumps(orig_model_list, ensure_ascii=False, indent=4), f'{pdf_name}_model.json',
path=f"{pdf_name}_model.json" json.dumps(orig_model_list, ensure_ascii=False, indent=4)
) )
# 写入中间结果到 middle.json # 写入中间结果到 middle.json
md_writer.write( md_writer.write_string(
content=json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4), f'{pdf_name}_middle.json',
path=f"{pdf_name}_middle.json" json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4)
) )
# text文本结果写入到 conent_list.json # text文本结果写入到 conent_list.json
md_writer.write( md_writer.write_string(
content=json.dumps(content_list, ensure_ascii=False, indent=4), f'{pdf_name}_content_list.json',
path=f"{pdf_name}_content_list.json" json.dumps(content_list, ensure_ascii=False, indent=4)
) )
# 写入结果到 .md 文件中 # 写入结果到 .md 文件中
md_writer.write( md_writer.write_string(
content=md_content, f'{pdf_name}.md',
path=f"{pdf_name}.md" md_content,
) )
# 可视化 # 可视化
def draw_visualization_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name): def draw_visualization_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name):
# 画布局框，附带排序结果 # 画布局框，附带排序结果
draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name) draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
# 画 span 框 # 画 span 框
draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name) draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
def pdf_parse_main( def pdf_parse_main(
pdf_path: str, pdf_path: str,
parse_method: str = 'auto', parse_method: str = 'auto',
model_json_path: str = None, model_json_path: str = None,
is_json_md_dump: bool = True, is_json_md_dump: bool = True,
is_draw_visualization_bbox: bool = True, is_draw_visualization_bbox: bool = True,
output_dir: str = None output_dir: str = None
): ):
""" """执行从 pdf 转换到 json、md 的过程，输出 md 和 json 文件到 pdf 文件所在的目录.
执行从 pdf 转换到 json、md 的过程，输出 md 和 json 文件到 pdf 文件所在的目录
:param pdf_path: .pdf 文件的路径，可以是相对路径，也可以是绝对路径
:param pdf_path: .pdf 文件的路径，可以是相对路径，也可以是绝对路径 :param parse_method: 解析方法， 共 auto、ocr、txt 三种，默认 auto，如果效果不好，可以尝试 ocr
:param parse_method: 解析方法， 共 auto、ocr、txt 三种，默认 auto，如果效果不好，可以尝试 ocr :param model_json_path: 已经存在的模型数据文件，如果为空则使用内置模型，pdf 和 model_json 务必对应
:param model_json_path: 已经存在的模型数据文件，如果为空则使用内置模型，pdf 和 model_json 务必对应 :param is_json_md_dump: 是否将解析后的数据写入到 .json 和 .md 文件中，默认 True，会将不同阶段的数据写入到不同的 .json 文件中（共3个.json文件），md内容会保存到 .md 文件中
:param is_json_md_dump: 是否将解析后的数据写入到 .json 和 .md 文件中，默认 True，会将不同阶段的数据写入到不同的 .json 文件中（共3个.json文件），md内容会保存到 .md 文件中 :param is_draw_visualization_bbox: 是否绘制可视化边界框，默认 True，会生成布局框和 span 框的图像
:param output_dir: 输出结果的目录地址，会生成一个以 pdf 文件名命名的文件夹并保存所有结果 :param output_dir: 输出结果的目录地址，会生成一个以 pdf 文件名命名的文件夹并保存所有结果
""" """
try: try:
pdf_name = os.path.basename(pdf_path).split(".")[0] pdf_name = os.path.basename(pdf_path).split('.')[0]
pdf_path_parent = os.path.dirname(pdf_path) pdf_path_parent = os.path.dirname(pdf_path)
if output_dir: if output_dir:
output_path = os.path.join(output_dir, pdf_name) output_path = os.path.join(output_dir, pdf_name)
else: else:
output_path = os.path.join(pdf_path_parent, pdf_name) output_path = os.path.join(pdf_path_parent, pdf_name)
output_image_path = os.path.join(output_path, 'images') output_image_path = os.path.join(output_path, 'images')
# 获取图片的父路径，为的是以相对路径保存到 .md 和 conent_list.json 文件中 # 获取图片的父路径，为的是以相对路径保存到 .md 和 conent_list.json 文件中
image_path_parent = os.path.basename(output_image_path) image_path_parent = os.path.basename(output_image_path)
pdf_bytes = open(pdf_path, "rb").read() # 读取 pdf 文件的二进制数据 pdf_bytes = open(pdf_path, 'rb').read() # 读取 pdf 文件的二进制数据
orig_model_list = [] orig_model_list = []
if model_json_path: if model_json_path:
# 读取已经被模型解析后的pdf文件的 json 原始数据，list 类型 # 读取已经被模型解析后的pdf文件的 json 原始数据，list 类型
model_json = json.loads(open(model_json_path, "r", encoding="utf-8").read()) model_json = json.loads(open(model_json_path, 'r', encoding='utf-8').read())
orig_model_list = copy.deepcopy(model_json) orig_model_list = copy.deepcopy(model_json)
else: else:
model_json = [] model_json = []
# 执行解析步骤 # 执行解析步骤
# image_writer = DiskReaderWriter(output_image_path) image_writer, md_writer = FileBasedDataWriter(output_image_path), FileBasedDataWriter(output_path)
image_writer, md_writer = DiskReaderWriter(output_image_path), DiskReaderWriter(output_path)
# 选择解析方式
# 选择解析方式 if parse_method == 'auto':
# jso_useful_key = {"_pdf_type": "", "model_list": model_json} jso_useful_key = {'_pdf_type': '', 'model_list': model_json}
# pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
if parse_method == "auto": elif parse_method == 'txt':
jso_useful_key = {"_pdf_type": "", "model_list": model_json} pipe = TXTPipe(pdf_bytes, model_json, image_writer)
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer) elif parse_method == 'ocr':
elif parse_method == "txt": pipe = OCRPipe(pdf_bytes, model_json, image_writer)
pipe = TXTPipe(pdf_bytes, model_json, image_writer) else:
elif parse_method == "ocr": logger.error('unknown parse method, only auto, ocr, txt allowed')
pipe = OCRPipe(pdf_bytes, model_json, image_writer) exit(1)
else:
logger.error("unknown parse method, only auto, ocr, txt allowed") # 执行分类
exit(1) pipe.pipe_classify()
# 执行分类 # 如果没有传入模型数据，则使用内置模型解析
pipe.pipe_classify() if len(model_json) == 0:
pipe.pipe_analyze() # 解析
# 如果没有传入模型数据，则使用内置模型解析 orig_model_list = copy.deepcopy(pipe.model_list)
if len(model_json) == 0:
pipe.pipe_analyze() # 解析 # 执行解析
orig_model_list = copy.deepcopy(pipe.model_list) pipe.pipe_parse()
# 执行解析 # 保存 text 和 md 格式的结果
pipe.pipe_parse() content_list = pipe.pipe_mk_uni_format(image_path_parent, drop_mode='none')
md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode='none')
# 保存 text 和 md 格式的结果
content_list = pipe.pipe_mk_uni_format(image_path_parent, drop_mode="none") if is_json_md_dump:
md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode="none") json_md_dump(pipe, md_writer, pdf_name, content_list, md_content, orig_model_list)
if is_json_md_dump: if is_draw_visualization_bbox:
json_md_dump(pipe, md_writer, pdf_name, content_list, md_content, orig_model_list) draw_visualization_bbox(pipe.pdf_mid_data['pdf_info'], pdf_bytes, output_path, pdf_name)
if is_draw_visualization_bbox: except Exception as e:
draw_visualization_bbox(pipe.pdf_mid_data['pdf_info'], pdf_bytes, output_path, pdf_name) logger.exception(e)
except Exception as e:
logger.exception(e) # 测试
if __name__ == '__main__':
current_script_dir = os.path.dirname(os.path.abspath(__file__))
# 测试 demo_names = ['demo1', 'demo2', 'small_ocr']
if __name__ == '__main__': for name in demo_names:
pdf_path = r"D:\project\20240617magicpdf\Magic-PDF\demo\demo1.pdf" file_path = os.path.join(current_script_dir, f'{name}.pdf')
pdf_parse_main(pdf_path) pdf_parse_main(file_path)
demo/small_ocr.json 0 → 100644
View file @ 4dcf31b6
[{"layout_dets":[{"category_id":1,"poly":[395.15179443359375,665.647705078125,2895.3388671875,665.647705078125,2895.3388671875,1069.8433837890625,395.15179443359375,1069.8433837890625],"score":0.9999968409538269},{"category_id":1,"poly":[400.7656555175781,2562.201904296875,2893.774658203125,2562.201904296875,2893.774658203125,2979.179931640625,400.7656555175781,2979.179931640625],"score":0.9999957084655762},{"category_id":2,"poly":[1333.9251708984375,469.05078125,1956.5269775390625,469.05078125,1956.5269775390625,540.6293334960938,1333.9251708984375,540.6293334960938],"score":0.9999939799308777},{"category_id":1,"poly":[374.78314208984375,1101.09521484375,2899.834228515625,1101.09521484375,2899.834228515625,2531.031005859375,374.78314208984375,2531.031005859375],"score":0.9999887347221375},{"category_id":2,"poly":[390.22503662109375,3074.9091796875,2898.39453125,3074.9091796875,2898.39453125,4304.73876953125,390.22503662109375,4304.73876953125],"score":0.9999833106994629},{"category_id":2,"poly":[487.03173828125,470.8531799316406,569.35693359375,470.8531799316406,569.35693359375,534.6068725585938,487.03173828125,534.6068725585938],"score":0.9998923540115356},{"category_id":1,"poly":[394.21929931640625,3073.115966796875,2899.8740234375,3073.115966796875,2899.8740234375,4301.3271484375,394.21929931640625,4301.3271484375],"score":0.2840508222579956},{"category_id":15,"poly":[402,681,2896,681,2896,770,402,770],"score":0.98,"text":"史的事情。（3)为有用物的量找到社会尺度，也是这样。商品的这些"},{"category_id":15,"poly":[399,828,2888,828,2888,914,399,914],"score":1,"text":"尺度之所以不同，部分是由于被计量的物的性质不同，部分是由"},{"category_id":15,"poly":[397,964,887,964,887,1070,397,1070],"score":1,"text":"于约定俗成。"},{"category_id":15,"poly":[582,2579,2884,2579,2884,2668,582,2668],"score":1,"text":"交换价值首先表现为量的关系，表现为不同种使用价值彼此"},{"category_id":15,"poly":[404,2722,2878,2722,2878,2819,404,2819],"score":0.97,"text":"相交换的比例(6)，即随着时间和地点的不同而不断改变的关系。"},{"category_id":15,"poly":[412,2878,2873,2878,2873,2962,412,2962],"score":0.97,"text":"因此，交换价值好象是一种任意的、纯粹相对的东西；商品固有的、"},{"category_id":15,"poly":[1337,463,1542,463,1542,544,1337,544],"score":0.98,"text":"第一篇"},{"category_id":15,"poly":[1617,474,1946,474,1946,536,1617,536],"score":1,"text":"商品和货币"},{"category_id":15,"poly":[576,1113,2897,1113,2897,1211,576,1211],"score":0.99,"text":"物的有用性使物成为使用价值。（4)但这种有用性不是飘忽不"},{"category_id":15,"poly":[401,1262,2831,1262,2831,1355,401,1355],"score":1,"text":"定的。它决定于商品体的属性，离开了商品体就不存在。因此，"},{"category_id":15,"poly":[406,1411,2886,1411,2886,1498,406,1498],"score":1,"text":"商品体本身，例如铁、小麦、金钢石等等，就是使用价值。赋予"},{"category_id":15,"poly":[409,1560,2883,1560,2883,1639,409,1639],"score":0.99,"text":"商品体以这种性质的，不是人为了取得它的有用性质所耗费的劳"},{"category_id":15,"poly":[406,1703,2889,1703,2889,1791,406,1791],"score":1,"text":"动的多少。在谈到使用价值时，总是指一定的量而言，如一打"},{"category_id":15,"poly":[403,1847,2883,1847,2883,1934,403,1934],"score":0.98,"text":"表，一米布，一吨铁等等。商品的使用价值为商品学和商业成规"},{"category_id":15,"poly":[403,1988,2886,1988,2886,2083,403,2083],"score":0.99,"text":"这种专门的知识提供材料。（5)使用价值只是在使用或消费中得到"},{"category_id":15,"poly":[403,2137,2889,2137,2889,2229,403,2229],"score":1,"text":"实现。不论财富的社会形式如何，使用价值构成财富的物质。在"},{"category_id":15,"poly":[406,2286,2886,2286,2886,2373,406,2373],"score":1,"text":"我们所要考察的社会形式中，使用价值同时又是交换价值的物质"},{"category_id":15,"poly":[401,2426,712,2426,712,2528,401,2528],"score":1,"text":"承担者。"},{"category_id":15,"poly":[533,3093,2888,3093,2888,3163,533,3163],"score":0.99,"text":"（3）“物都有内在的长处（这是巴尔本用来表示使用价值的专门用语），这种长"},{"category_id":15,"poly":[403,3193,2888,3193,2888,3265,403,3265],"score":0.99,"text":"处在任何地方都具有同样的性质，如磁石吸铁的长处就是如此。”（尼古拉·巴尔本"},{"category_id":15,"poly":[400,3298,2886,3298,2886,3368,400,3368],"score":0.99,"text":"《新币轻铸论。答洛克先生关于提高货币价值的意见》1696年伦敦版第16页）磁石吸"},{"category_id":15,"poly":[405,3404,2052,3404,2052,3465,405,3465],"score":1,"text":"铁的属性只是在通过它发现了磁极性以后才成为有用的，"},{"category_id":15,"poly":[528,3495,2877,3495,2877,3578,528,3578],"score":0.99,"text":"（4）“物的自然价值产生于它能满足需要，或者给人类生活带来方便。”约翰·"},{"category_id":15,"poly":[400,3606,2883,3606,2883,3675,400,3675],"score":0.98,"text":"洛克：《论降低利虑的后果》（1691年）。在十七世纪，我们还常常看到英国著作家用"},{"category_id":15,"poly":[392,3700,2888,3700,2888,3783,392,3783],"score":0.93,"text":"Worth表示使用价值，用valuu）表示交换价值；这完全符合英语的精神，英语喜欢"},{"category_id":15,"poly":[405,3814,2524,3814,2524,3875,405,3875],"score":0.98,"text":"用日耳曼语源的词表示直接的东西，用罗马语源的词表示被反射的东西。"},{"category_id":15,"poly":[533,3911,2886,3911,2886,3980,533,3980],"score":0.97,"text":"（5）在资产阶级社会中“任何人都不能推托不知道法律”。～—按照经济法的"},{"category_id":15,"poly":[397,4016,1785,4016,1785,4082,397,4082],"score":1,"text":"假定，每个买者都具有百科全书般的商品知识。"},{"category_id":15,"poly":[530,4116,2886,4116,2886,4185,530,4185],"score":0.98,"text":"（6）“价值就是一物和另一物，一定量的这种产品和一定量的别种产品之间的"},{"category_id":15,"poly":[400,4215,2717,4215,2717,4290,400,4290],"score":0.98,"text":"交换关系。”（列特隆《论社会利益》，德尔编《重农学派》1846年巴黎版第889页）"},{"category_id":15,"poly":[488,469,570,469,570,539,488,539],"score":1,"text":"12"},{"category_id":15,"poly":[534,3089,2889,3089,2889,3164,534,3164],"score":1,"text":"（3）“物都有内在的长处（这是巴尔本用来表示使用价值的专门用语），这种长"},{"category_id":15,"poly":[404,3195,2889,3195,2889,3264,404,3264],"score":0.99,"text":"处在任何地方都具有同样的性质，如磁石吸铁的长处就是如此。”（尼古拉·巴尔本"},{"category_id":15,"poly":[393,3291,2889,3291,2889,3372,393,3372],"score":1,"text":"《新币轻铸论。答洛克先生关于提高货币价值的意见》1696年伦敦版第16页）磁石吸"},{"category_id":15,"poly":[404,3397,2054,3397,2054,3466,404,3466],"score":0.99,"text":"铁的属性只是在通过它发现了磁极性以后才成为有用的，"},{"category_id":15,"poly":[529,3499,2876,3499,2876,3574,529,3574],"score":0.99,"text":"（4）“物的自然价值产生于它能满足需要，或者给人类生活带来方便。”约翰·"},{"category_id":15,"poly":[396,3604,2884,3604,2884,3676,396,3676],"score":0.98,"text":"洛克：《论降低利虑的后果》（1691年）。在十七世纪，我们还常常看到英国著作家用"},{"category_id":15,"poly":[396,3704,2887,3704,2887,3778,396,3778],"score":0.96,"text":"worth表示使用价值，用valuu表示交换价值：这完全符合英语的精神，英语喜欢"},{"category_id":15,"poly":[404,3814,2523,3814,2523,3875,404,3875],"score":0.98,"text":"用日耳曼语源的词表示直接的东西，用罗马语源的词表示被反射的东西。"},{"category_id":15,"poly":[534,3908,2884,3908,2884,3977,534,3977],"score":0.98,"text":"（5）在资产阶级社会中“任何人都不能推托不知道法律”。～一按照经济法的"},{"category_id":15,"poly":[404,4016,1779,4016,1779,4077,404,4077],"score":1,"text":"假定，每个买者都具有百科全书般的商品知识。"},{"category_id":15,"poly":[534,4116,2884,4116,2884,4185,534,4185],"score":0.99,"text":"（6）“价值就是一物和另一物，一定量的这种产品和一定量的别种产品之间的"},{"category_id":15,"poly":[398,4215,2718,4215,2718,4293,398,4293],"score":0.99,"text":"交换关系。”（列特隆《论社会利益》，德尔编《重农学派》1846年巴黎版第889页）"}],"page_info":{"page_no":0,"height":5000,"width":3405}},{"layout_dets":[{"category_id":1,"poly":[344.1777038574219,678.7516479492188,2800.564453125,678.7516479492188,2800.564453125,930.7621459960938,344.1777038574219,930.7621459960938],"score":0.9999980926513672},{"category_id":1,"poly":[343.77142333984375,969.0947265625,2839.3525390625,969.0947265625,2839.3525390625,1523.2760009765625,343.77142333984375,1523.2760009765625],"score":0.999995231628418},{"category_id":1,"poly":[345.6445617675781,2574.56005859375,2837.068603515625,2574.56005859375,2837.068603515625,3266.256103515625,345.6445617675781,3266.256103515625],"score":0.9999947547912598},{"category_id":1,"poly":[509.7772521972656,3305.733642578125,2830.307373046875,3305.733642578125,2830.307373046875,3416.622802734375,509.7772521972656,3416.622802734375],"score":0.9999911785125732},{"category_id":1,"poly":[339.6177673339844,1550.982177734375,3060.858642578125,1550.982177734375,3060.858642578125,2542.067138671875,339.6177673339844,2542.067138671875],"score":0.9999850988388062},{"category_id":2,"poly":[338.4281005859375,4094.802978515625,2826.7080078125,4094.802978515625,2826.7080078125,4286.5654296875,338.4281005859375,4286.5654296875],"score":0.9997429251670837},{"category_id":2,"poly":[1289.113525390625,478.922119140625,1908.499755859375,478.922119140625,1908.499755859375,548.927734375,1289.113525390625,548.927734375],"score":0.9962558746337891},{"category_id":2,"poly":[2671.2412109375,482.0177917480469,2748.818115234375,482.0177917480469,2748.818115234375,542.70751953125,2671.2412109375,542.70751953125],"score":0.9902564883232117},{"category_id":2,"poly":[337.44989013671875,3525.9443359375,2818.002197265625,3525.9443359375,2818.002197265625,3919.84765625,337.44989013671875,3919.84765625],"score":0.939180314540863},{"category_id":2,"poly":[2871.44384765625,2162.59228515625,3076.815673828125,2162.59228515625,3076.815673828125,2233.8427734375,2871.44384765625,2233.8427734375],"score":0.8846070766448975},{"category_id":1,"poly":[338.0018005371094,3526.410888671875,2817.889404296875,3526.410888671875,2817.889404296875,3915.434814453125,338.0018005371094,3915.434814453125],"score":0.3099205493927002},{"category_id":13,"poly":[1056,1853,1200,1853,1200,1939,1056,1939],"score":0.82,"latex":"=a"},{"category_id":13,"poly":[1862,2008,1927,2008,1927,2085,1862,2085],"score":0.66,"latex":"a"},{"category_id":13,"poly":[469,4095,553,4095,553,4176,469,4176],"score":0.54,"latex":"[I]"},{"category_id":13,"poly":[2516,678,2635,678,2635,782,2516,782],"score":0.38,"latex":"\\textcircled{1}_{0}"},{"category_id":15,"poly":[342,686,2515,686,2515,785,342,785],"score":1,"text":"内在的交换价值似乎是经院哲学家所说的形容语的矛盾"},{"category_id":15,"poly":[2636,686,2796,686,2796,785,2636,785],"score":0.98,"text":"(7)"},{"category_id":15,"poly":[350,840,1532,840,1532,925,350,925],"score":0.99,"text":"现在我们进一步考察这个问题。"},{"category_id":15,"poly":[520,978,2830,978,2830,1070,520,1070],"score":1,"text":"某种特殊的商品，例如一夸特小麦，按各种极不相同的比例"},{"category_id":15,"poly":[345,1123,2827,1123,2827,1215,345,1215],"score":1,"text":"同别的商品交换。但是，它的交换价值，无论采用何种表现方"},{"category_id":15,"poly":[339,1271,2832,1271,2832,1364,339,1364],"score":0.93,"text":"式，X量鞋油、y量绸缎、z量金等等，都是不变的。因此，它"},{"category_id":15,"poly":[342,1417,2067,1417,2067,1509,342,1509],"score":1,"text":"必定有一种与这些不同的表现相区别的内容。"},{"category_id":15,"poly":[527,2584,2822,2584,2822,2681,527,2681],"score":1,"text":"用一个初级几何的例子就可以说明这一点。为了测量和比较"},{"category_id":15,"poly":[344,2730,2822,2730,2822,2827,344,2827],"score":1,"text":"各种直线形的面积，就把它们分成三角形，再把三角形化成与它"},{"category_id":15,"poly":[346,2879,2825,2879,2825,2970,346,2970],"score":0.98,"text":"的外形完全不同的表现一一底乘高的一半。各种商品的交换价值"},{"category_id":15,"poly":[346,3030,2814,3030,2814,3113,346,3113],"score":0.97,"text":"也同样要化成一种对它们来说是共同的东西，各自代表这种共同"},{"category_id":15,"poly":[346,3173,1091,3173,1091,3261,346,3261],"score":1,"text":"东西的多量或少量。"},{"category_id":15,"poly":[517,3314,2822,3314,2822,3411,517,3411],"score":0.97,"text":"这种共同东西不可能是商品的某种天然属性，几何的、物理"},{"category_id":15,"poly":[527,1560,2825,1560,2825,1659,527,1659],"score":0.98,"text":"我们再拿两种商品例奶小麦和铁来说。不管二者的交换比例"},{"category_id":15,"poly":[348,1713,2822,1713,2822,1802,348,1802],"score":1,"text":"怎样，总是可以用一个等式来表示：一定量的小麦等于若干量的"},{"category_id":15,"poly":[345,1855,1055,1855,1055,1949,345,1949],"score":0.99,"text":"铁，如1夸特小麦"},{"category_id":15,"poly":[1201,1855,2831,1855,2831,1949,1201,1949],"score":0.99,"text":"公斤铁。这个等式说明什么呢？它说明在"},{"category_id":15,"poly":[345,2003,1861,2003,1861,2097,345,2097],"score":1,"text":"两种不同的物里面，即在1夸特小麦和"},{"category_id":15,"poly":[1928,2003,2822,2003,2822,2097,1928,2097],"score":0.99,"text":"公斤铁里面，有一种共"},{"category_id":15,"poly":[345,2145,3066,2145,3066,2245,345,2245],"score":0.97,"text":"同的东西。因而这二者都等于第三种东西，后者本身既不是第一14（Ⅱ）"},{"category_id":15,"poly":[345,2293,2825,2293,2825,2387,345,2387],"score":1,"text":"种物，也不是第二种物。这二者中的每一个作为交换价值，都必"},{"category_id":15,"poly":[342,2438,1797,2438,1797,2532,342,2532],"score":1,"text":"定能不依赖另一个而化为第三种东西。"},{"category_id":15,"poly":[554,4099,2822,4099,2822,4190,554,4190],"score":0.96,"text":"形容语的矛盾的原文是《coatradictioin ajecto》，指“圆形的方”，“木制"},{"category_id":15,"poly":[339,4208,1248,4208,1248,4284,339,4284],"score":0.87,"text":"的铁”一类的矛盾。一—一译者注"},{"category_id":15,"poly":[1291,478,1630,478,1630,550,1291,550],"score":0.95,"text":"第一章商"},{"category_id":15,"poly":[1832,475,1904,475,1904,554,1832,554],"score":1,"text":"品"},{"category_id":15,"poly":[2671,480,2749,480,2749,545,2671,545],"score":1,"text":"13"},{"category_id":15,"poly":[473,3537,2820,3537,2820,3619,473,3619],"score":0.97,"text":"（7）“任何东西部不可能有内在的交换价值。”（尼·巴尔本《新币轻铸论。答洛"},{"category_id":15,"poly":[341,3640,2217,3640,2217,3714,341,3714],"score":0.96,"text":"克先生关了提高货币价值的意见》第16页）：或者象巴特勒所说："},{"category_id":15,"poly":[1130,3740,1446,3740,1446,3823,1130,3823],"score":0.99,"text":"“物的价值"},{"category_id":15,"poly":[1142,3848,2013,3848,2013,3920,1142,3920],"score":0.99,"text":"正好和它会换来的东西相等。”"},{"category_id":15,"poly":[2870,2165,3073,2165,3073,2232,2870,2232],"score":0.89,"text":"14(I)"},{"category_id":15,"poly":[474,3537,2821,3537,2821,3619,474,3619],"score":0.97,"text":"（7）“任创东西部不可能有内在的交换价值。”（尼·巴尔本《新币轻铸论。答洛"},{"category_id":15,"poly":[342,3641,2214,3641,2214,3713,342,3713],"score":0.98,"text":"克先生关于提高货币价值的意见》第16页）：或者象巴特勒所说："},{"category_id":15,"poly":[1132,3743,1443,3743,1443,3817,1132,3817],"score":0.99,"text":"“物的价值"},{"category_id":15,"poly":[1137,3845,2013,3845,2013,3922,1137,3922],"score":0.96,"text":"正好和它会换来的东西相等。”"}],"page_info":{"page_no":1,"height":5000,"width":3405}},{"layout_dets":[{"category_id":2,"poly":[1327.838623046875,477.8331604003906,1943.4556884765625,477.8331604003906,1943.4556884765625,553.0089721679688,1327.838623046875,553.0089721679688],"score":0.9999961853027344},{"category_id":1,"poly":[392.85247802734375,1842.046142578125,2879.21533203125,1842.046142578125,2879.21533203125,3421.4755859375,392.85247802734375,3421.4755859375],"score":0.9999904632568359},{"category_id":1,"poly":[385.5883483886719,3453.063232421875,2875.83349609375,3453.063232421875,2875.83349609375,3713.21484375,385.5883483886719,3713.21484375],"score":0.9999902248382568},{"category_id":1,"poly":[396.40093994140625,672.6717529296875,2878.79541015625,672.6717529296875,2878.79541015625,1815.5244140625,396.40093994140625,1815.5244140625],"score":0.9999882578849792},{"category_id":2,"poly":[477.78692626953125,479.1949157714844,561.9874267578125,479.1949157714844,561.9874267578125,543.1449584960938,477.78692626953125,543.1449584960938],"score":0.9999648928642273},{"category_id":2,"poly":[381.5726623535156,3891.904541015625,2870.3447265625,3891.904541015625,2870.3447265625,4294.64013671875,381.5726623535156,4294.64013671875],"score":0.9999456405639648},{"category_id":15,"poly":[1328,476,1535,476,1535,555,1328,555],"score":1,"text":"第一篇"},{"category_id":15,"poly":[1598,482,1935,482,1935,550,1598,550],"score":0.97,"text":"商品和货币"},{"category_id":15,"poly":[574,1864,2867,1864,2867,1949,574,1949],"score":0.98,"text":"如果把商品的使用价值撇开，商品就只剩下一个性质，即劳"},{"category_id":15,"poly":[393,2005,2870,2005,2870,2099,393,2099],"score":1,"text":"动产品这个性质。可是劳动产品不知不觉已经起了变化。如果我"},{"category_id":15,"poly":[396,2157,2870,2157,2870,2242,396,2242],"score":1,"text":"们把劳动产品的使用价值抽去，那么，赋予劳动产品以这种价值"},{"category_id":15,"poly":[396,2303,2821,2303,2821,2388,396,2388],"score":0.99,"text":"的一切物质要素和形式要素也就同时消失了。它们不再是桌子，"},{"category_id":15,"poly":[396,2449,2867,2449,2867,2535,396,2535],"score":0.98,"text":"房屋，纱或别的什么有用物；它们也不再是旋匠劳动、瓦匠劳"},{"category_id":15,"poly":[396,2598,2859,2598,2859,2676,396,2676],"score":0.99,"text":"动，或任何一定的生产劳动的产品了。随着劳动产品的特殊的有"},{"category_id":15,"poly":[396,2736,2864,2736,2864,2822,396,2822],"score":1,"text":"用性质的消失，包含在劳动产品中的各种劳动的有用性质也消失"},{"category_id":15,"poly":[396,2886,2864,2886,2864,2971,396,2971],"score":1,"text":"了，这些劳动相互区别的各种具体形式也消失了。因此，留下来"},{"category_id":15,"poly":[391,3029,2867,3029,2867,3118,391,3118],"score":1,"text":"的只是这些劳动的共同性质；这些劳动全都化为相同的人类劳"},{"category_id":15,"poly":[391,3173,2870,3173,2870,3264,391,3264],"score":1,"text":"动，化为与人类劳动力耗费的特殊形式无关的人类劳动力的耗"},{"category_id":15,"poly":[382,3316,523,3316,523,3421,382,3421],"score":1,"text":"费。"},{"category_id":15,"poly":[572,3473,2860,3473,2860,3552,572,3552],"score":1,"text":"现在我们来考察劳动产品剩下来的东西。它们中的每一个和"},{"category_id":15,"poly":[392,3608,2868,3608,2868,3707,392,3707],"score":1,"text":"另一个都完全相同。它们都具有同一的幽灵般的现实性。它们变"},{"category_id":15,"poly":[394,689,2820,689,2820,786,394,786],"score":1,"text":"的、化学的属性等等。商品的天然属性只是就它们使商品有用，"},{"category_id":15,"poly":[400,841,2874,841,2874,924,400,924],"score":1,"text":"从而使商品成为使用价值来说，才加以考虑。但是，另一方面很"},{"category_id":15,"poly":[397,985,2874,985,2874,1071,397,1071],"score":1,"text":"清楚，商品的使用价值在商品交换时被抽象掉了，而一切商品交"},{"category_id":15,"poly":[397,1133,2872,1133,2872,1219,397,1219],"score":1,"text":"换关系的特点正是这种抽象。在交换中，只要比例适当，一种使"},{"category_id":15,"poly":[394,1274,2874,1274,2874,1368,394,1368],"score":1,"text":"用价值就和其他任何一种使用价值完全相等。或者象老巴尔本说"},{"category_id":15,"poly":[400,1424,2869,1424,2869,1510,400,1510],"score":0.99,"text":"的：“只要交换价值相等，一种商品就同另一种商品一样；交换价"},{"category_id":15,"poly":[392,1560,2874,1560,2874,1662,392,1662],"score":0.98,"text":"值相等的物是没有任何差别或区别的。”(8)作为使用价值，商品"},{"category_id":15,"poly":[395,1712,2552,1712,2552,1804,395,1804],"score":0.99,"text":"首先有质的差别；作为交换价值，商品只能有量的差别。"},{"category_id":15,"poly":[479,472,566,472,566,553,479,553],"score":1,"text":"14"},{"category_id":15,"poly":[517,3904,2869,3904,2869,3983,517,3983],"score":0.99,"text":"（8）“只要交换价值相等，一种商品就同另一种商品一样；交换价值相等的物"},{"category_id":15,"poly":[385,4006,2866,4006,2866,4085,385,4085],"score":0.96,"text":"是没有任何差别或区别的。价值100锈的铅或铁与价值100铸的银和金具有相等的"},{"category_id":15,"poly":[390,4113,2861,4113,2861,4179,390,4179],"score":0.98,"text":"交换价值。”（尼·巴尔本《新币轻铸论。答洛克先生关于提高货币价值的意见》第7页"},{"category_id":15,"poly":[388,4213,684,4213,684,4281,388,4281],"score":0.99,"text":"和第53页）"}],"page_info":{"page_no":2,"height":5000,"width":3405}},{"layout_dets":[{"category_id":1,"poly":[306.7962951660156,1556.903564453125,2794.922607421875,1556.903564453125,2794.922607421875,2103.880859375,306.7962951660156,2103.880859375],"score":0.9999971389770508},{"category_id":1,"poly":[305.2056579589844,3451.38232421875,2790.525634765625,3451.38232421875,2790.525634765625,4295.9443359375,305.2056579589844,4295.9443359375],"score":0.9999949336051941},{"category_id":1,"poly":[305.8328857421875,671.9126586914062,3050.56884765625,671.9126586914062,3050.56884765625,1093.463623046875,305.8328857421875,1093.463623046875],"score":0.9999949336051941},{"category_id":1,"poly":[311.4427185058594,1120.2196044921875,2795.731201171875,1120.2196044921875,2795.731201171875,1525.5904541015625,311.4427185058594,1525.5904541015625],"score":0.9999923706054688},{"category_id":1,"poly":[304.9309387207031,2134.734130859375,2799.203369140625,2134.734130859375,2799.203369140625,3422.9482421875,304.9309387207031,3422.9482421875],"score":0.9999920725822449},{"category_id":2,"poly":[1254.085205078125,475.64703369140625,1868.686767578125,475.64703369140625,1868.686767578125,555.4613647460938,1254.085205078125,555.4613647460938],"score":0.999935507774353},{"category_id":2,"poly":[2639.77978515625,482.96240234375,2717.211181640625,482.96240234375,2717.211181640625,543.316650390625,2639.77978515625,543.316650390625],"score":0.9994043111801147},{"category_id":15,"poly":[485,1567,2790,1567,2790,1663,485,1663],"score":1,"text":"那么，它的价值量是怎样计量的呢？是用它所包含的“创造"},{"category_id":15,"poly":[313,1716,2782,1716,2782,1804,313,1804],"score":1,"text":"价值”的实体即劳动的量来计量。劳动本身的量是用劳动持续时"},{"category_id":15,"poly":[310,1860,2782,1860,2782,1948,310,1948],"score":0.99,"text":"间来计量，而劳动时间又是用一定的时间单位如小时，日等作尺"},{"category_id":15,"poly":[299,1996,450,1996,450,2102,299,2102],"score":1,"text":"度。"},{"category_id":15,"poly":[479,3463,2784,3463,2784,3559,479,3559],"score":1,"text":"生产商品的社会必要劳动时间是在一定社会的正常的条件"},{"category_id":15,"poly":[309,3610,2778,3610,2778,3699,309,3699],"score":1,"text":"下，在平均熟练程度和劳动强度下劳动所需要的时间。在英国采"},{"category_id":15,"poly":[309,3755,2778,3755,2778,3846,309,3846],"score":1,"text":"用蒸汽织布机以后，把一定量的纱织成布所需要的劳动可能比过"},{"category_id":15,"poly":[301,3897,2775,3897,2775,3994,301,3994],"score":1,"text":"去少一半。英国的手工织布工人把纱织成布仍旧要用以前那样多"},{"category_id":15,"poly":[304,4047,2770,4047,2770,4136,304,4136],"score":1,"text":"的劳动时间，但这时他一小时的个人劳动的产品只代表半小时的"},{"category_id":15,"poly":[298,4192,1841,4192,1841,4281,298,4281],"score":1,"text":"社会劳动，并且只提供以前价值的一半。"},{"category_id":15,"poly":[302,686,3052,686,3052,787,302,787],"score":0.97,"text":"成了同一的升华物，同一的无差别的劳动的样品。它们只是表 15（I)"},{"category_id":15,"poly":[311,847,2795,847,2795,936,311,936],"score":1,"text":"示，在它们的生产上耗费了人类劳动力，积累了人类劳动。这些"},{"category_id":15,"poly":[311,996,2214,996,2214,1080,311,1080],"score":1,"text":"物，作为这个共同的社会实体的结晶，就是价值。"},{"category_id":15,"poly":[490,1133,2794,1133,2794,1228,490,1228],"score":1,"text":"因此，在商品交换关系或商品的交换价值中表现出来的某种"},{"category_id":15,"poly":[312,1283,2786,1283,2786,1370,312,1370],"score":1,"text":"共同的东西，就是商品的价值；而使用价值或某种物品具有价"},{"category_id":15,"poly":[307,1425,1758,1425,1758,1515,307,1515],"score":1,"text":"值，只是因为有人类劳动物化在里面。"},{"category_id":15,"poly":[487,2152,2787,2152,2787,2244,487,2244],"score":1,"text":"可能会有人这样认为，既然商品的价值由生产商品所耗费的"},{"category_id":15,"poly":[308,2298,2781,2298,2781,2390,308,2390],"score":0.98,"text":"劳动量来决定，那么一个人越懒，越不熟练，他的商品就越有价"},{"category_id":15,"poly":[305,2442,2784,2442,2784,2534,305,2534],"score":1,"text":"值，因为他制造商品需要花费的时间越多。但是，形成商品价值"},{"category_id":15,"poly":[303,2585,2784,2585,2784,2683,303,2683],"score":1,"text":"实体的劳动是相同的无差别的劳动，是同一的力量的耗费。因"},{"category_id":15,"poly":[305,2735,2781,2735,2781,2824,305,2824],"score":1,"text":"此，体现在全部价值中的社会的全部劳动力，只是当作唯一的力"},{"category_id":15,"poly":[308,2878,2781,2878,2781,2968,308,2968],"score":1,"text":"量，虽然它是由无数单个劳动力构成的。每一个单个劳动力，同"},{"category_id":15,"poly":[308,3025,2781,3025,2781,3114,308,3114],"score":1,"text":"任何另一个单个劳动力是相同的，只要它具有社会平均的力量的"},{"category_id":15,"poly":[306,3168,2782,3168,2782,3261,306,3261],"score":1,"text":"性质，作为这种力量起作用，就是说，在商品的生产上只使用平"},{"category_id":15,"poly":[303,3317,1752,3317,1752,3410,303,3410],"score":0.99,"text":"均必要劳动时间或社会必要劳动时间。"},{"category_id":15,"poly":[1256,481,1597,481,1597,551,1256,551],"score":0.99,"text":"第一章商"},{"category_id":15,"poly":[1793,486,1860,486,1860,549,1793,549],"score":1,"text":"品"},{"category_id":15,"poly":[2637,480,2719,480,2719,550,2637,550],"score":1,"text":"15"}],"page_info":{"page_no":3,"height":5000,"width":3405}},{"layout_dets":[{"category_id":2,"poly":[1321.068603515625,487.19891357421875,1932.37744140625,487.19891357421875,1932.37744140625,561.1749267578125,1321.068603515625,561.1749267578125],"score":0.9999957084655762},{"category_id":1,"poly":[361.2233581542969,685.0840454101562,2871.822998046875,685.0840454101562,2871.822998046875,1525.35400390625,361.2233581542969,1525.35400390625],"score":0.9999942779541016},{"category_id":2,"poly":[362.17352294921875,3703.583740234375,2852.736083984375,3703.583740234375,2852.736083984375,4315.783203125,362.17352294921875,4315.783203125],"score":0.999991774559021},{"category_id":1,"poly":[379.93292236328125,1549.5421142578125,2873.397705078125,1549.5421142578125,2873.397705078125,3598.7412109375,379.93292236328125,3598.7412109375],"score":0.9999860525131226},{"category_id":2,"poly":[469.35284423828125,492.3908996582031,549.4078369140625,492.3908996582031,549.4078369140625,554.1589965820312,469.35284423828125,554.1589965820312],"score":0.9999639391899109},{"category_id":15,"poly":[1321,486,1523,486,1523,560,1321,560],"score":0.99,"text":"第一篇"},{"category_id":15,"poly":[1596,492,1921,492,1921,556,1596,556],"score":0.99,"text":"商品和货币"},{"category_id":15,"poly":[569,702,2872,702,2872,785,569,785],"score":1,"text":"可见，只是在一定社会内生产物品所必要的劳动量或劳动时"},{"category_id":15,"poly":[382,838,2867,838,2867,934,382,934],"score":0.98,"text":"间，决定该物品的价值量。（9)在这里，单个商品是当作该种商品"},{"category_id":15,"poly":[382,979,2872,979,2872,1084,382,1084],"score":0.98,"text":"的平均样品。（10)因此，含有等量劳动或能在同样时间内生产出来"},{"category_id":15,"poly":[385,1134,2864,1134,2864,1225,385,1225],"score":1,"text":"的商品，具有同样的价值。一种商品的价值同其他任何一种商品"},{"category_id":15,"poly":[387,1281,2867,1281,2867,1369,387,1369],"score":1,"text":"的价值的比例，就是生产前者的必要劳动时间同生产后者的必要"},{"category_id":15,"poly":[387,1431,1042,1431,1042,1516,387,1516],"score":1,"text":"劳动时间的比例。"},{"category_id":15,"poly":[498,3714,2851,3714,2851,3789,498,3789],"score":0.99,"text":"（9）“当有用物互相交换的时候，它们的价值取决于生产它们所必需的和通常"},{"category_id":15,"poly":[369,3809,2851,3809,2851,3903,369,3903],"score":0.97,"text":"所用掉的劳动量。\"（《对货币利息，特别是公债利息的一些看法》伦敦版第36页）上一"},{"category_id":15,"poly":[374,3923,2848,3923,2848,3995,374,3995],"score":0.99,"text":"世纪的这部值得注意的匿名著作没有注明出版日期。但从它的内容可以看出，该书"},{"category_id":15,"poly":[374,4026,1885,4026,1885,4095,374,4095],"score":0.98,"text":"是在乔治二世时代，大约1739年或1740年出版的。"},{"category_id":15,"poly":[504,4120,2845,4120,2845,4204,504,4204],"score":1,"text":"（10）“全部同类产品其实只是一个量，这个量的价格是整个地决定的，而不以"},{"category_id":15,"poly":[363,4226,1847,4226,1847,4301,363,4301],"score":0.97,"text":"特殊情况为转移。”（列特隆《论社会利益》第893页）"},{"category_id":15,"poly":[564,1571,2864,1571,2864,1666,564,1666],"score":1,"text":"显然，如果生产商品所需要的时间不变，商品的价值量也就"},{"category_id":15,"poly":[386,1719,2864,1719,2864,1813,386,1813],"score":1,"text":"不变。但是，生产商品所需要的时间随着劳动生产力的每一变动"},{"category_id":15,"poly":[383,1864,2864,1864,2864,1961,383,1961],"score":1,"text":"而变动，而劳动生产力是由多种情况决定的，其中包括：劳动者"},{"category_id":15,"poly":[383,2012,2861,2012,2861,2106,383,2106],"score":1,"text":"的平均熟练程度，科学的发展水平和它在工艺上应用的程度，生"},{"category_id":15,"poly":[381,2157,2807,2157,2807,2251,381,2251],"score":1,"text":"产的社会结合，生产资料的规模和效能，以及纯粹的自然条件。"},{"category_id":15,"poly":[386,2302,2858,2302,2858,2402,386,2402],"score":1,"text":"例如，同一劳动量在丰收年表现为8蒲式耳小麦，在相反的场合"},{"category_id":15,"poly":[383,2450,2858,2450,2858,2547,383,2547],"score":1,"text":"只表现为4蒲式耳。同一劳动量用在富矿比用在贫矿能提供更多"},{"category_id":15,"poly":[380,2600,2853,2600,2853,2689,380,2689],"score":1,"text":"的金属等等。金刚石在地壳中是很稀少的，因而发现金刚石平均"},{"category_id":15,"poly":[378,2748,2855,2748,2855,2837,378,2837],"score":1,"text":"要花很多劳动时间，因此很小一块金刚石就代表很多劳动。金是"},{"category_id":15,"poly":[378,2893,2853,2893,2853,2985,378,2985],"score":1,"text":"否按其全部价值支付过是值得怀疑的。至于金刚石，就更可以这"},{"category_id":15,"poly":[370,3035,2858,3035,2858,3132,370,3132],"score":0.99,"text":"样说了。厄什韦葛说过，到1823年，巴西金刚石矿八十年的总产"},{"category_id":15,"poly":[378,3183,2853,3183,2853,3275,378,3275],"score":1,"text":"量的价格还赶不上巴西甘蔗种植园或咖啡种植园一年半平均产量"},{"category_id":15,"poly":[380,3336,2853,3336,2853,3420,380,3420],"score":1,"text":"的价格，虽然前者代表的劳动多得多，从而价值也多得多。如果"},{"category_id":15,"poly":[378,3476,2847,3476,2847,3567,378,3567],"score":1,"text":"发现富矿，同一劳动量就会实现为更大量的金刚石，而金刚石的"},{"category_id":15,"poly":[468,492,551,492,551,559,468,559],"score":1,"text":"16"}],"page_info":{"page_no":4,"height":5000,"width":3405}},{"layout_dets":[{"category_id":1,"poly":[344.4116516113281,696.4637451171875,2834.3232421875,696.4637451171875,2834.3232421875,1563.0045166015625,344.4116516113281,1563.0045166015625],"score":0.999994158744812},{"category_id":1,"poly":[337.1441345214844,1579.0399169921875,2829.40234375,1579.0399169921875,2829.40234375,1834.083984375,337.1441345214844,1834.083984375],"score":0.9999927282333374},{"category_id":2,"poly":[449.9178161621094,4241.7216796875,2510.4609375,4241.7216796875,2510.4609375,4336.64892578125,449.9178161621094,4336.64892578125],"score":0.9999920725822449},{"category_id":1,"poly":[322.3527526855469,3628.9736328125,2817.54052734375,3628.9736328125,2817.54052734375,4192.1171875,322.3527526855469,4192.1171875],"score":0.9999861717224121},{"category_id":1,"poly":[331.0948791503906,1865.8778076171875,3028.783447265625,1865.8778076171875,3028.783447265625,3008.90966796875,331.0948791503906,3008.90966796875],"score":0.9999809265136719},{"category_id":0,"poly":[860.1250610351562,3276.212646484375,2263.005859375,3276.212646484375,2263.005859375,3440.292236328125,860.1250610351562,3440.292236328125],"score":0.9999785423278809},{"category_id":2,"poly":[1298.62646484375,502.2377014160156,1908.6483154296875,502.2377014160156,1908.6483154296875,574.3719482421875,1298.62646484375,574.3719482421875],"score":0.9984298944473267},{"category_id":2,"poly":[2679.096435546875,515.3965454101562,2752.3359375,515.3965454101562,2752.3359375,575.4978637695312,2679.096435546875,575.4978637695312],"score":0.9969415664672852},{"category_id":2,"poly":[2871.089599609375,2038.3990478515625,3068.003173828125,2038.3990478515625,3068.003173828125,2109.9892578125,2871.089599609375,2109.9892578125],"score":0.7686592936515808},{"category_id":15,"poly":[351,715,2833,715,2833,814,351,814],"score":1,"text":"价值就会降低。假如能用不多的劳动把煤变成金刚石，金刚石的"},{"category_id":15,"poly":[348,860,2828,860,2828,962,348,962],"score":1,"text":"价值就会低于砖的价值。总之，劳动生产力越高，生产一种物品"},{"category_id":15,"poly":[348,1003,2830,1003,2830,1105,348,1105],"score":1,"text":"所需要的时间就越少，凝结在该物品中的劳动量就越小，该物品"},{"category_id":15,"poly":[346,1143,2830,1143,2830,1253,346,1253],"score":1,"text":"的价值就越小。相反地，劳动生产力越低，生产一种物品的必要"},{"category_id":15,"poly":[346,1294,2825,1294,2825,1396,346,1396],"score":1,"text":"时间就越多，该物品的价值就越大。可见，商品的价值量与实现"},{"category_id":15,"poly":[338,1434,2598,1434,2598,1553,338,1553],"score":1,"text":"在商品中的劳动的成正比，与这一劳动的生产力成反比。"},{"category_id":15,"poly":[522,1584,2825,1584,2825,1696,522,1696],"score":1,"text":"现在我们知道：价值实体就是劳动；劳动量的尺度就是劳动"},{"category_id":15,"poly":[337,1728,738,1728,738,1832,337,1832],"score":1,"text":"持续时间。"},{"category_id":15,"poly":[458,4248,2499,4248,2499,4329,458,4329],"score":0.99,"text":"（11）卡尔·马克思《政治经济学批判》1859年柏林版第12、13等页。"},{"category_id":15,"poly":[313,4068,2805,4068,2805,4193,313,4193],"score":0.99,"text":"的这种二重性，是首先由我明确指出的。(11)这一点是政治经济学"},{"category_id":15,"poly":[496.75,3649.5,2801.75,3649.5,2801.75,3737.5,496.75,3737.5],"score":1,"text":"起初我们看到，商品是一种二重的东西，即使用价值和交换"},{"category_id":15,"poly":[320.75,3797,2801.75,3797,2801.75,3885.5,320.75,3885.5],"score":1,"text":"价值。后来我们看到，一旦生产使用价值的劳动表现为价值本"},{"category_id":15,"poly":[318.25,3942.5,2799.25,3942.5,2799.25,4030.5,318.25,4030.5],"score":1,"text":"身，那么，这种劳动的一切特点也就消失了。商品中包含的劳动"},{"category_id":15,"poly":[520,1872,2816,1872,2816,1986,520,1986],"score":1,"text":"一个物可以是使用价值而不是价值。这就使一个物可以对人"},{"category_id":15,"poly":[331,2018,3026,2018,3026,2132,331,2132],"score":0.98,"text":"有用而不必是人的劳动的产物。例如，空气，天然草地、处女16(I"},{"category_id":15,"poly":[334,2170,2819,2170,2819,2278,334,2278],"score":0.99,"text":"地、等等。一个物可以有用，而且是人类劳动产品，但不是商"},{"category_id":15,"poly":[334,2317,2813,2317,2813,2425,334,2425],"score":0.99,"text":"品。谁用自己的产品来满足自己的需要，他生产的就只是个人的"},{"category_id":15,"poly":[334,2466,2813,2466,2813,2571,334,2571],"score":1,"text":"使用价值。要生产商品，他不仅要生产使用价值，而且要为别人"},{"category_id":15,"poly":[331,2610,2813,2610,2813,2718,331,2718],"score":1,"text":"生产使用价值，即生产社会的使用价值。最后，没有一个物可以"},{"category_id":15,"poly":[334,2759,2810,2759,2810,2861,334,2861],"score":1,"text":"是价值而不是有用物。如果物没有用，那么其中包含的劳动也就"},{"category_id":15,"poly":[334,2905,1519,2905,1519,2998,334,2998],"score":1,"text":"白白耗费了，因此不创造价值。"},{"category_id":15,"poly":[868,3290,2249,3290,2249,3418,868,3418],"score":0.98,"text":"2.商品所体现的劳动的二重性"},{"category_id":15,"poly":[1300,502,1505,502,1505,577,1300,577],"score":1,"text":"第一章"},{"category_id":15,"poly":[1564,507,1637,507,1637,576,1564,576],"score":1,"text":"商"},{"category_id":15,"poly":[1838,501,1909,501,1909,582,1838,582],"score":1,"text":"品"},{"category_id":15,"poly":[2674,514,2758,514,2758,580,2674,580],"score":1,"text":"17"},{"category_id":15,"poly":[2864,2041,3066,2041,3066,2108,2864,2108],"score":0.95,"text":"16(1)"}],"page_info":{"page_no":5,"height":5000,"width":3405}},{"layout_dets":[{"category_id":1,"poly":[377.5026550292969,1256.7139892578125,2866.861083984375,1256.7139892578125,2866.861083984375,1951.9281005859375,377.5026550292969,1951.9281005859375],"score":0.9999935030937195},{"category_id":1,"poly":[374.8831481933594,1988.3514404296875,2867.05029296875,1988.3514404296875,2867.05029296875,2684.714599609375,374.8831481933594,2684.714599609375],"score":0.9999927282333374},{"category_id":1,"poly":[379.2370910644531,819.9422607421875,2869.646728515625,819.9422607421875,2869.646728515625,1219.536376953125,379.2370910644531,1219.536376953125],"score":0.9999918341636658},{"category_id":2,"poly":[1314.675537109375,479.0633239746094,1926.4168701171875,479.0633239746094,1926.4168701171875,553.719482421875,1314.675537109375,553.719482421875],"score":0.9999911189079285},{"category_id":2,"poly":[465.20361328125,481.85626220703125,546.4675903320312,481.85626220703125,546.4675903320312,542.8040771484375,465.20361328125,542.8040771484375],"score":0.9999901652336121},{"category_id":1,"poly":[365.0695495605469,3883.75,2853.8740234375,3883.75,2853.8740234375,4291.607421875,365.0695495605469,4291.607421875],"score":0.9999831914901733},{"category_id":1,"poly":[315.95550537109375,2719.0244140625,2862.50048828125,2719.0244140625,2862.50048828125,3851.922607421875,315.95550537109375,3851.922607421875],"score":0.999983012676239},{"category_id":1,"poly":[382.1902160644531,682.7503051757812,2113.547607421875,682.7503051757812,2113.547607421875,787.54052734375,382.1902160644531,787.54052734375],"score":0.9999698400497437},{"category_id":13,"poly":[2076,973,2241,973,2241,1066,2076,1066],"score":0.73,"latex":"\\frac{1}{20-10}=\\pi"},{"category_id":13,"poly":[2777,974,2870,974,2870,1057,2777,1057],"score":0.69,"latex":"="},{"category_id":13,"poly":[373,1121,521,1121,521,1220,373,1220],"score":0.27,"latex":"2x_{\\alpha}"},{"category_id":15,"poly":[559,1275,2865,1275,2865,1361,559,1361],"score":1,"text":"上衣是满足一种特殊需要的使用价值。上衣产生于一种特定"},{"category_id":15,"poly":[384,1421,2862,1421,2862,1507,384,1507],"score":0.97,"text":"种类的生产活动。这种生产活动是由它的目的、操作方式，对"},{"category_id":15,"poly":[378,1568,2857,1568,2857,1650,378,1650],"score":0.98,"text":"象，手段和结果决定的。表现为自己产品的有用性或使用价值的"},{"category_id":15,"poly":[381,1714,2865,1714,2865,1799,381,1799],"score":1,"text":"劳动，我们简称为有用劳动。从这个观点来看，劳动总是联系到"},{"category_id":15,"poly":[384,1863,1295,1863,1295,1940,384,1940],"score":0.99,"text":"它的有用效果来考察的。"},{"category_id":15,"poly":[559,2004,2855,2004,2855,2090,559,2090],"score":1,"text":"上衣和麻布是二种不同的有用物，同样，生产上衣的裁缝劳"},{"category_id":15,"poly":[375,2151,2860,2151,2860,2236,375,2236],"score":1,"text":"动和生产麻布的织工劳动也不相同。如果这些物不是不同质的使"},{"category_id":15,"poly":[381,2295,2860,2295,2860,2380,381,2380],"score":1,"text":"用价值，从而不是不同质的有用劳动的产品，它们就根本不能作"},{"category_id":15,"poly":[373,2438,2858,2438,2858,2532,373,2532],"score":1,"text":"为商品来互相对立。上衣不会与上衣交换，一种使用价值不会与"},{"category_id":15,"poly":[376,2582,1210,2582,1210,2676,376,2676],"score":1,"text":"同种的使用价值交换。"},{"category_id":15,"poly":[561,839,2865,839,2865,920,561,920],"score":0.99,"text":"我们就拿两种商品如1件上衣和10米麻布来说。假定前者"},{"category_id":15,"poly":[380,983,2075,983,2075,1066,380,1066],"score":1,"text":"的价值比后者的价值大一倍。假设10米麻布"},{"category_id":15,"poly":[2242,983,2776,983,2776,1066,2242,1066],"score":0.99,"text":"，则1件上衣"},{"category_id":15,"poly":[1318,480,1522,480,1522,552,1318,552],"score":1,"text":"第一篇"},{"category_id":15,"poly":[1590,484,1917,484,1917,548,1590,548],"score":0.99,"text":"商品和货币"},{"category_id":15,"poly":[464,480,548,480,548,551,464,551],"score":1,"text":"18"},{"category_id":15,"poly":[547,3899,2849,3899,2849,3989,547,3989],"score":1,"text":"可见，每个商品的使用价值都包含着特殊的有用劳动或有"},{"category_id":15,"poly":[363,4045,2849,4045,2849,4132,363,4132],"score":0.98,"text":"特殊目的的生产活动。各种使用价值只有包含不同质的有用劳"},{"category_id":15,"poly":[366,4190,2849,4190,2849,4280,366,4280],"score":1,"text":"动，才能作为商品互相对立。在产品普遍采取商品形式的社会"},{"category_id":15,"poly":[557,2735,2799,2735,2799,2820,557,2820],"score":0.99,"text":"与各种使用价值的总和相对应的有同样多种的、按照属，"},{"category_id":15,"poly":[375,2883,2851,2883,2851,2960,375,2960],"score":0.98,"text":"种、科分类的有用劳动的总和，即社会分工。没有这种分工就没"},{"category_id":15,"poly":[373,3021,2846,3021,2846,3106,373,3106],"score":1,"text":"有商品生产，虽然不能反过来说商品生产对社会分工是不可缺少"},{"category_id":15,"poly":[370,3170,2851,3170,2851,3255,370,3255],"score":0.99,"text":"的。在古代印度公社中就有社会分工，但产品并不因此而成为商"},{"category_id":15,"poly":[375,3318,2788,3318,2788,3403,375,3403],"score":0.98,"text":"品。或者拿一个熟悉的例子来说，每个工厂内都有系统的分工，"},{"category_id":15,"poly":[370,3464,2848,3464,2848,3543,370,3543],"score":1,"text":"但是这种分工不是由于工人交换他们个人的产品而产生的。只有"},{"category_id":15,"poly":[367,3607,2848,3607,2848,3692,367,3692],"score":1,"text":"独立的互不依赖的私人劳动的产品，才表现为可以互相交换的商"},{"category_id":15,"poly":[362,3741,510,3741,510,3857,362,3857],"score":1,"text":"品。"},{"category_id":15,"poly":[385,690,2104,690,2104,777,385,777],"score":0.99,"text":"的枢纽，因此，在这里要较详细地加以说明。"}],"page_info":{"page_no":6,"height":5000,"width":3405}},{"layout_dets":[{"category_id":1,"poly":[332.3732604980469,665.1901245117188,2832.19189453125,665.1901245117188,2832.19189453125,1072.64501953125,332.3732604980469,1072.64501953125],"score":0.9999982714653015},{"category_id":1,"poly":[328.2882080078125,1100.2418212890625,2833.665771484375,1100.2418212890625,2833.665771484375,2522.64208984375,328.2882080078125,2522.64208984375],"score":0.9999918937683105},{"category_id":1,"poly":[322.688232421875,2550.721923828125,3052.0263671875,2550.721923828125,3052.0263671875,3394.32177734375,322.688232421875,3394.32177734375],"score":0.9999860525131226},{"category_id":2,"poly":[1286.9974365234375,463.7050476074219,1904.880859375,463.7050476074219,1904.880859375,538.1673583984375,1286.9974365234375,538.1673583984375],"score":0.9999286532402039},{"category_id":2,"poly":[318.3165283203125,3555.543701171875,2810.735107421875,3555.543701171875,2810.735107421875,4263.3212890625,318.3165283203125,4263.3212890625],"score":0.9999019503593445},{"category_id":2,"poly":[2672.804443359375,468.5804748535156,2750.83056640625,468.5804748535156,2750.83056640625,530.396484375,2672.804443359375,530.396484375],"score":0.9989616274833679},{"category_id":15,"poly":[339,676,2828,676,2828,771,339,771],"score":1,"text":"里，也就是在一切生产者都必定是商人的社会里，作为自由生产"},{"category_id":15,"poly":[339,832,2822,832,2822,913,339,913],"score":0.99,"text":"者的私事而各自独立进行的各种有用劳动的这种区别，发展成一"},{"category_id":15,"poly":[334,969,1609,969,1609,1064,334,1064],"score":1,"text":"个多支的体系，发展成社会分工。"},{"category_id":15,"poly":[509,1119,2823,1119,2823,1206,509,1206],"score":0.98,"text":"对上衣来说，无论是裁缝自已穿还是他的顾客穿，都是一样"},{"category_id":15,"poly":[335,1261,2818,1261,2818,1357,335,1357],"score":1,"text":"的。在这两种场合，它都是起使用价值的作用。同样，上衣和生"},{"category_id":15,"poly":[327,1407,2761,1407,2761,1499,327,1499],"score":1,"text":"产上衣的劳动之间的关系，也并不因为裁缝劳动成为专门职业，"},{"category_id":15,"poly":[330,1552,2766,1552,2766,1642,330,1642],"score":1,"text":"成为社会分工的一个环节就有所改变。自从人有了穿衣的需要，"},{"category_id":15,"poly":[324,1695,2815,1695,2815,1787,324,1787],"score":0.99,"text":"人已经缝了几千年的衣服，但并没有人因此而成为裁缝。但是，麻"},{"category_id":15,"poly":[324,1837,2813,1837,2813,1930,324,1930],"score":0.98,"text":"布、上衣以及任何一种不是天然存在的物质财富要素，总是必须"},{"category_id":15,"poly":[330,1983,2813,1983,2813,2078,330,2078],"score":1,"text":"通过某种旨在使自然物质适合于人类需要的特殊生产活动创造出"},{"category_id":15,"poly":[324,2128,2813,2128,2813,2221,324,2221],"score":0.99,"text":"来。劳动就它生产使用价值，就它是有用劳动而言，它与一切社"},{"category_id":15,"poly":[330,2273,2756,2273,2756,2366,330,2366],"score":1,"text":"会形式无关，是人类生存的不可缺少的条件，是永恒的必然性，"},{"category_id":15,"poly":[319,2416,1695,2416,1695,2511,319,2511],"score":1,"text":"是人和自然之间的物质循环的中介。"},{"category_id":15,"poly":[502,2562,2810,2562,2810,2662,502,2662],"score":0.99,"text":"上衣、麻布等等使用价值，即种种商品体，是物质和劳动这"},{"category_id":15,"poly":[328,2703,2807,2703,2807,2810,328,2810],"score":0.98,"text":"两种要素的结合。如果把上衣、麻布等等包含的各种不同的有用"},{"category_id":15,"poly":[325,2854,2807,2854,2807,2949,325,2949],"score":1,"text":"劳动的总和除外，总还剩有物质，剩有某种天然存在的，完全不"},{"category_id":15,"poly":[319,2998,2813,2998,2813,3099,319,3099],"score":1,"text":"依赖人的东西。人只能象自然本身那样发挥作用，就是说，只能"},{"category_id":15,"poly":[316,3134,3055,3134,3055,3246,316,3246],"score":0.97,"text":"改变物质的形态。（12)不仅如此，他在这种单纯改变形态的劳动中 17(I)"},{"category_id":15,"poly":[322,3291,2798,3291,2798,3382,322,3382],"score":1,"text":"还要经常依靠自然力的帮助。因此，劳动并不是它所生产的使用"},{"category_id":15,"poly":[1291,466,1633,466,1633,539,1291,539],"score":0.99,"text":"第一章商"},{"category_id":15,"poly":[1828,460,1905,460,1905,543,1828,543],"score":0.99,"text":"品"},{"category_id":15,"poly":[460,3564,2809,3564,2809,3651,460,3651],"score":0.99,"text":"（12）“宇宙的一切现象，不论是由人手创造的，还是由自然的一般规律引起"},{"category_id":15,"poly":[319,3668,2806,3668,2806,3749,319,3749],"score":1,"text":"的，都不是真正的创造，而只是物质的形态变化。结合和分离是人的智慧在分析再"},{"category_id":15,"poly":[319,3771,2803,3771,2803,3853,319,3853],"score":0.99,"text":"生产的观念时发现的唯一要素；土地、空气和水在田地上变成谷物，或者昆虫的分"},{"category_id":15,"poly":[317,3872,2801,3872,2801,3954,317,3954],"score":1,"text":"泌物经过人的手变成丝绸，或者通过金属原子的排列来制造金属，也是价值（指使用"},{"category_id":15,"poly":[317,3976,2801,3976,2801,4055,317,4055],"score":0.99,"text":"价值，尽管维里在这里同重农学派论战时自己也不清楚说的是哪一种价值）和财富的"},{"category_id":15,"poly":[317,4074,2801,4074,2801,4156,317,4156],"score":0.99,"text":"再生产。”（彼得罗·维里《政治经济学研究》1773年初版，载于库斯托第编《意大利政"},{"category_id":15,"poly":[319,4178,1731,4178,1731,4254,319,4254],"score":0.99,"text":"治经济学名家文集》现代部分，第15卷第22页）"},{"category_id":15,"poly":[2670,464,2754,464,2754,536,2670,536],"score":1,"text":"19"}],"page_info":{"page_no":7,"height":5000,"width":3405}}]
\ No newline at end of file
magic_pdf/data/data_reader_writer/filebase.py
View file @ 4dcf31b6
...@@ -55,5 +55,8 @@ class FileBasedDataWriter(DataWriter): ...@@ -55,5 +55,8 @@ class FileBasedDataWriter(DataWriter):
if not os.path.isabs(fn_path) and len(self._parent_dir) > 0: if not os.path.isabs(fn_path) and len(self._parent_dir) > 0:
fn_path = os.path.join(self._parent_dir, path) fn_path = os.path.join(self._parent_dir, path)
if not os.path.exists(os.path.dirname(fn_path)):
os.makedirs(os.path.dirname(fn_path), exist_ok=True)
with open(fn_path, 'wb') as f: with open(fn_path, 'wb') as f:
f.write(data) f.write(data)
projects/web_api/app.py
View file @ 4dcf31b6
...@@ -3,75 +3,79 @@ import json ...@@ -3,75 +3,79 @@ import json
import os import os
from tempfile import NamedTemporaryFile from tempfile import NamedTemporaryFile
import magic_pdf.model as model_config
import uvicorn import uvicorn
from fastapi import FastAPI, File, UploadFile, Form from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse from fastapi.responses import JSONResponse
from loguru import logger from loguru import logger
import magic_pdf.model as model_config
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.pipe.OCRPipe import OCRPipe from magic_pdf.pipe.OCRPipe import OCRPipe
from magic_pdf.pipe.TXTPipe import TXTPipe from magic_pdf.pipe.TXTPipe import TXTPipe
from magic_pdf.pipe.UNIPipe import UNIPipe from magic_pdf.pipe.UNIPipe import UNIPipe
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
model_config.__use_inside_model__ = True model_config.__use_inside_model__ = True
app = FastAPI() app = FastAPI()
def json_md_dump( def json_md_dump(
pipe, pipe,
md_writer, md_writer,
pdf_name, pdf_name,
content_list, content_list,
md_content, md_content,
): ):
# Write model results to model.json # Write model results to model.json
orig_model_list = copy.deepcopy(pipe.model_list) orig_model_list = copy.deepcopy(pipe.model_list)
md_writer.write( md_writer.write_string(
content=json.dumps(orig_model_list, ensure_ascii=False, indent=4), f'{pdf_name}_model.json',
path=f"{pdf_name}_model.json" json.dumps(orig_model_list, ensure_ascii=False, indent=4),
) )
# Write intermediate results to middle.json # Write intermediate results to middle.json
md_writer.write( md_writer.write_string(
content=json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4), f'{pdf_name}_middle.json',
path=f"{pdf_name}_middle.json" json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4),
) )
# Write text content results to content_list.json # Write text content results to content_list.json
md_writer.write( md_writer.write_string(
content=json.dumps(content_list, ensure_ascii=False, indent=4), f'{pdf_name}_content_list.json',
path=f"{pdf_name}_content_list.json" json.dumps(content_list, ensure_ascii=False, indent=4),
) )
# Write results to .md file # Write results to .md file
md_writer.write( md_writer.write_string(
content=md_content, f'{pdf_name}.md',
path=f"{pdf_name}.md" md_content,
) )
@app.post("/pdf_parse", tags=["projects"], summary="Parse PDF file")
@app.post('/pdf_parse', tags=['projects'], summary='Parse PDF file')
async def pdf_parse_main( async def pdf_parse_main(
pdf_file: UploadFile = File(...), pdf_file: UploadFile = File(...),
parse_method: str = 'auto', parse_method: str = 'auto',
model_json_path: str = None, model_json_path: str = None,
is_json_md_dump: bool = True, is_json_md_dump: bool = True,
output_dir: str = "output" output_dir: str = 'output',
): ):
""" """Execute the process of converting PDF to JSON and MD, outputting MD and
Execute the process of converting PDF to JSON and MD, outputting MD and JSON files to the specified directory JSON files to the specified directory.
:param pdf_file: The PDF file to be parsed :param pdf_file: The PDF file to be parsed
:param parse_method: Parsing method, can be auto, ocr, or txt. Default is auto. If results are not satisfactory, try ocr :param parse_method: Parsing method, can be auto, ocr, or txt. Default is auto. If results are not satisfactory, try ocr
:param model_json_path: Path to existing model data file. If empty, use built-in model. PDF and model_json must correspond :param model_json_path: Path to existing model data file. If empty, use built-in model. PDF and model_json must correspond
:param is_json_md_dump: Whether to write parsed data to .json and .md files. Default is True. Different stages of data will be written to different .json files (3 in total), md content will be saved to .md file :param is_json_md_dump: Whether to write parsed data to .json and .md files. Default is True. Different stages of data will be written to different .json files (3 in total), md content will be saved to .md file # noqa E501
:param output_dir: Output directory for results. A folder named after the PDF file will be created to store all results :param output_dir: Output directory for results. A folder named after the PDF file will be created to store all results
""" """
try: try:
# Create a temporary file to store the uploaded PDF # Create a temporary file to store the uploaded PDF
with NamedTemporaryFile(delete=False, suffix=".pdf") as temp_pdf: with NamedTemporaryFile(delete=False, suffix='.pdf') as temp_pdf:
temp_pdf.write(await pdf_file.read()) temp_pdf.write(await pdf_file.read())
temp_pdf_path = temp_pdf.name temp_pdf_path = temp_pdf.name
pdf_name = os.path.basename(pdf_file.filename).split(".")[0] pdf_name = os.path.basename(pdf_file.filename).split('.')[0]
if output_dir: if output_dir:
output_path = os.path.join(output_dir, pdf_name) output_path = os.path.join(output_dir, pdf_name)
...@@ -83,28 +87,32 @@ async def pdf_parse_main( ...@@ -83,28 +87,32 @@ async def pdf_parse_main(
# Get parent path of images for relative path in .md and content_list.json # Get parent path of images for relative path in .md and content_list.json
image_path_parent = os.path.basename(output_image_path) image_path_parent = os.path.basename(output_image_path)
pdf_bytes = open(temp_pdf_path, "rb").read() # Read binary data of PDF file pdf_bytes = open(temp_pdf_path, 'rb').read() # Read binary data of PDF file
if model_json_path: if model_json_path:
# Read original JSON data of PDF file parsed by model, list type # Read original JSON data of PDF file parsed by model, list type
model_json = json.loads(open(model_json_path, "r", encoding="utf-8").read()) model_json = json.loads(open(model_json_path, 'r', encoding='utf-8').read())
else: else:
model_json = [] model_json = []
# Execute parsing steps # Execute parsing steps
image_writer, md_writer = DiskReaderWriter(output_image_path), DiskReaderWriter(output_path) image_writer, md_writer = FileBasedDataWriter(
output_image_path
), FileBasedDataWriter(output_path)
# Choose parsing method # Choose parsing method
if parse_method == "auto": if parse_method == 'auto':
jso_useful_key = {"_pdf_type": "", "model_list": model_json} jso_useful_key = {'_pdf_type': '', 'model_list': model_json}
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
elif parse_method == "txt": elif parse_method == 'txt':
pipe = TXTPipe(pdf_bytes, model_json, image_writer) pipe = TXTPipe(pdf_bytes, model_json, image_writer)
elif parse_method == "ocr": elif parse_method == 'ocr':
pipe = OCRPipe(pdf_bytes, model_json, image_writer) pipe = OCRPipe(pdf_bytes, model_json, image_writer)
else: else:
logger.error("Unknown parse method, only auto, ocr, txt allowed") logger.error('Unknown parse method, only auto, ocr, txt allowed')
return JSONResponse(content={"error": "Invalid parse method"}, status_code=400) return JSONResponse(
content={'error': 'Invalid parse method'}, status_code=400
)
# Execute classification # Execute classification
pipe.pipe_classify() pipe.pipe_classify()
...@@ -114,28 +122,36 @@ async def pdf_parse_main( ...@@ -114,28 +122,36 @@ async def pdf_parse_main(
if model_config.__use_inside_model__: if model_config.__use_inside_model__:
pipe.pipe_analyze() # Parse pipe.pipe_analyze() # Parse
else: else:
logger.error("Need model list input") logger.error('Need model list input')
return JSONResponse(content={"error": "Model list input required"}, status_code=400) return JSONResponse(
content={'error': 'Model list input required'}, status_code=400
)
# Execute parsing # Execute parsing
pipe.pipe_parse() pipe.pipe_parse()
# Save results in text and md format # Save results in text and md format
content_list = pipe.pipe_mk_uni_format(image_path_parent, drop_mode="none") content_list = pipe.pipe_mk_uni_format(image_path_parent, drop_mode='none')
md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode="none") md_content = pipe.pipe_mk_markdown(image_path_parent, drop_mode='none')
if is_json_md_dump: if is_json_md_dump:
json_md_dump(pipe, md_writer, pdf_name, content_list, md_content) json_md_dump(pipe, md_writer, pdf_name, content_list, md_content)
data = {"layout": copy.deepcopy(pipe.model_list), "info": pipe.pdf_mid_data, "content_list": content_list,'md_content':md_content} data = {
'layout': copy.deepcopy(pipe.model_list),
'info': pipe.pdf_mid_data,
'content_list': content_list,
'md_content': md_content,
}
return JSONResponse(data, status_code=200) return JSONResponse(data, status_code=200)
except Exception as e: except Exception as e:
logger.exception(e) logger.exception(e)
return JSONResponse(content={"error": str(e)}, status_code=500) return JSONResponse(content={'error': str(e)}, status_code=500)
finally: finally:
# Clean up the temporary file # Clean up the temporary file
if 'temp_pdf_path' in locals(): if 'temp_pdf_path' in locals():
os.unlink(temp_pdf_path) os.unlink(temp_pdf_path)
# if __name__ == '__main__':
# uvicorn.run(app, host="0.0.0.0", port=8888) if __name__ == '__main__':
\ No newline at end of file uvicorn.run(app, host='0.0.0.0', port=8888)
projects/web_demo/web_demo/api/analysis/pdf_ext.py
View file @ 4dcf31b6
import json import json
import re
import os import os
import shutil import shutil
import traceback import traceback
from pathlib import Path from pathlib import Path
from common.error_types import ApiException
from common.mk_markdown.mk_markdown import \
ocr_mk_mm_markdown_with_para_and_pagination
from flask import current_app, url_for from flask import current_app, url_for
from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter from loguru import logger
from magic_pdf.pipe.UNIPipe import UNIPipe
import magic_pdf.model as model_config import magic_pdf.model as model_config
from magic_pdf.data.data_reader_writer import FileBasedDataWriter
from magic_pdf.libs.json_compressor import JsonCompressor from magic_pdf.libs.json_compressor import JsonCompressor
from common.mk_markdown.mk_markdown import ocr_mk_mm_markdown_with_para_and_pagination from magic_pdf.pipe.UNIPipe import UNIPipe
from ..extensions import app, db
from .ext import find_file from .ext import find_file
from ..extentions import app, db
from .models import AnalysisPdf, AnalysisTask from .models import AnalysisPdf, AnalysisTask
from common.error_types import ApiException
from loguru import logger
model_config.__use_inside_model__ = True model_config.__use_inside_model__ = True
...@@ -22,51 +25,51 @@ model_config.__use_inside_model__ = True ...@@ -22,51 +25,51 @@ model_config.__use_inside_model__ = True
def analysis_pdf(image_url_prefix, image_dir, pdf_bytes, is_ocr=False): def analysis_pdf(image_url_prefix, image_dir, pdf_bytes, is_ocr=False):
try: try:
model_json = [] # model_json传空list使用内置模型解析 model_json = [] # model_json传空list使用内置模型解析
logger.info(f"is_ocr: {is_ocr}") logger.info(f'is_ocr: {is_ocr}')
if not is_ocr: if not is_ocr:
jso_useful_key = {"_pdf_type": "", "model_list": model_json} jso_useful_key = {'_pdf_type': '', 'model_list': model_json}
image_writer = DiskReaderWriter(image_dir) image_writer = FileBasedDataWriter(image_dir)
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True)
pipe.pipe_classify() pipe.pipe_classify()
else: else:
jso_useful_key = {"_pdf_type": "ocr", "model_list": model_json} jso_useful_key = {'_pdf_type': 'ocr', 'model_list': model_json}
image_writer = DiskReaderWriter(image_dir) image_writer = FileBasedDataWriter(image_dir)
pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True) pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True)
"""如果没有传入有效的模型数据，则使用内置model解析""" """如果没有传入有效的模型数据，则使用内置model解析"""
if len(model_json) == 0: if len(model_json) == 0:
if model_config.__use_inside_model__: if model_config.__use_inside_model__:
pipe.pipe_analyze() pipe.pipe_analyze()
else: else:
logger.error("need model list input") logger.error('need model list input')
exit(1) exit(1)
pipe.pipe_parse() pipe.pipe_parse()
pdf_mid_data = JsonCompressor.decompress_json(pipe.get_compress_pdf_mid_data()) pdf_mid_data = JsonCompressor.decompress_json(pipe.get_compress_pdf_mid_data())
pdf_info_list = pdf_mid_data["pdf_info"] pdf_info_list = pdf_mid_data['pdf_info']
md_content = json.dumps(ocr_mk_mm_markdown_with_para_and_pagination(pdf_info_list, image_url_prefix), md_content = json.dumps(ocr_mk_mm_markdown_with_para_and_pagination(pdf_info_list, image_url_prefix),
ensure_ascii=False) ensure_ascii=False)
bbox_info = get_bbox_info(pdf_info_list) bbox_info = get_bbox_info(pdf_info_list)
return md_content, bbox_info return md_content, bbox_info
except Exception as e: except Exception as e: # noqa: F841
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
def get_bbox_info(data): def get_bbox_info(data):
bbox_info = [] bbox_info = []
for page in data: for page in data:
preproc_blocks = page.get("preproc_blocks", []) preproc_blocks = page.get('preproc_blocks', [])
discarded_blocks = page.get("discarded_blocks", []) discarded_blocks = page.get('discarded_blocks', [])
bbox_info.append({ bbox_info.append({
"preproc_blocks": preproc_blocks, 'preproc_blocks': preproc_blocks,
"page_idx": page.get("page_idx"), 'page_idx': page.get('page_idx'),
"page_size": page.get("page_size"), 'page_size': page.get('page_size'),
"discarded_blocks": discarded_blocks, 'discarded_blocks': discarded_blocks,
}) })
return bbox_info return bbox_info
def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
""" """解析pdf.
解析pdf
:param pdf_dir: pdf解析目录 :param pdf_dir: pdf解析目录
:param image_dir: 图片目录 :param image_dir: 图片目录
:param pdf_path: pdf路径 :param pdf_path: pdf路径
...@@ -75,8 +78,8 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -75,8 +78,8 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
:return: :return:
""" """
try: try:
logger.info(f"start task: {pdf_path}") logger.info(f'start task: {pdf_path}')
logger.info(f"image_dir: {image_dir}") logger.info(f'image_dir: {image_dir}')
if not Path(image_dir).exists(): if not Path(image_dir).exists():
Path(image_dir).mkdir(parents=True, exist_ok=True) Path(image_dir).mkdir(parents=True, exist_ok=True)
else: else:
...@@ -96,26 +99,26 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -96,26 +99,26 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
# ############ markdown ############# # ############ markdown #############
pdf_name = Path(pdf_path).name pdf_name = Path(pdf_path).name
full_md_content = "" full_md_content = ''
for item in json.loads(md_content): for item in json.loads(md_content):
full_md_content += item["md_content"] + "\n" full_md_content += item['md_content'] + '\n'
full_md_name = "full.md" full_md_name = 'full.md'
with open(f"{pdf_dir}/{full_md_name}", "w", encoding="utf-8") as file: with open(f'{pdf_dir}/{full_md_name}', 'w', encoding='utf-8') as file:
file.write(full_md_content) file.write(full_md_content)
with app.app_context(): with app.app_context():
full_md_link = url_for('analysis.mdview', filename=full_md_name, as_attachment=False) full_md_link = url_for('analysis.mdview', filename=full_md_name, as_attachment=False)
full_md_link = f"{full_md_link}&pdf={pdf_name}" full_md_link = f'{full_md_link}&pdf={pdf_name}'
md_link_list = [] md_link_list = []
with app.app_context(): with app.app_context():
for n, md in enumerate(json.loads(md_content)): for n, md in enumerate(json.loads(md_content)):
md_content = md["md_content"] md_content = md['md_content']
md_name = f"{md.get('page_no', n)}.md" md_name = f"{md.get('page_no', n)}.md"
with open(f"{pdf_dir}/{md_name}", "w", encoding="utf-8") as file: with open(f'{pdf_dir}/{md_name}', 'w', encoding='utf-8') as file:
file.write(md_content) file.write(md_content)
md_url = url_for('analysis.mdview', filename=md_name, as_attachment=False) md_url = url_for('analysis.mdview', filename=md_name, as_attachment=False)
md_link_list.append(f"{md_url}&pdf={pdf_name}") md_link_list.append(f'{md_url}&pdf={pdf_name}')
with app.app_context(): with app.app_context():
with db.auto_commit(): with db.auto_commit():
...@@ -129,8 +132,8 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -129,8 +132,8 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
analysis_task_object = AnalysisTask.query.filter_by(analysis_pdf_id=analysis_pdf_id).first() analysis_task_object = AnalysisTask.query.filter_by(analysis_pdf_id=analysis_pdf_id).first()
analysis_task_object.status = 1 analysis_task_object.status = 1
db.session.add(analysis_task_object) db.session.add(analysis_task_object)
logger.info(f"finished!") logger.info('finished!')
except Exception as e: except Exception as e: # noqa: F841
logger.error(traceback.format_exc()) logger.error(traceback.format_exc())
with app.app_context(): with app.app_context():
with db.auto_commit(): with db.auto_commit():
...@@ -141,7 +144,7 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -141,7 +144,7 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
analysis_task_object = AnalysisTask.query.filter_by(analysis_pdf_id=analysis_pdf_id).first() analysis_task_object = AnalysisTask.query.filter_by(analysis_pdf_id=analysis_pdf_id).first()
analysis_task_object.status = 1 analysis_task_object.status = 1
db.session.add(analysis_task_object) db.session.add(analysis_task_object)
raise ApiException(code=500, msg="PDF parsing failed", msgZH="pdf解析失败") raise ApiException(code=500, msg='PDF parsing failed', msgZH='pdf解析失败')
finally: finally:
# 执行pending # 执行pending
with app.app_context(): with app.app_context():
...@@ -149,12 +152,12 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -149,12 +152,12 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
AnalysisTask.update_date.asc()).first() AnalysisTask.update_date.asc()).first()
if analysis_task_object: if analysis_task_object:
pdf_upload_folder = current_app.config['PDF_UPLOAD_FOLDER'] pdf_upload_folder = current_app.config['PDF_UPLOAD_FOLDER']
upload_dir = f"{current_app.static_folder}/{pdf_upload_folder}" upload_dir = f'{current_app.static_folder}/{pdf_upload_folder}'
file_path = find_file(analysis_task_object.file_key, upload_dir) file_path = find_file(analysis_task_object.file_key, upload_dir)
file_stem = Path(file_path).stem file_stem = Path(file_path).stem
pdf_analysis_folder = current_app.config['PDF_ANALYSIS_FOLDER'] pdf_analysis_folder = current_app.config['PDF_ANALYSIS_FOLDER']
pdf_dir = f"{current_app.static_folder}/{pdf_analysis_folder}/{file_stem}" pdf_dir = f'{current_app.static_folder}/{pdf_analysis_folder}/{file_stem}'
image_dir = f"{pdf_dir}/images" image_dir = f'{pdf_dir}/images'
with db.auto_commit(): with db.auto_commit():
analysis_pdf_object = AnalysisPdf.query.filter_by(id=analysis_task_object.analysis_pdf_id).first() analysis_pdf_object = AnalysisPdf.query.filter_by(id=analysis_task_object.analysis_pdf_id).first()
analysis_pdf_object.status = 0 analysis_pdf_object.status = 0
...@@ -164,4 +167,4 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id): ...@@ -164,4 +167,4 @@ def analysis_pdf_task(pdf_dir, image_dir, pdf_path, is_ocr, analysis_pdf_id):
db.session.add(analysis_task_object) db.session.add(analysis_task_object)
analysis_pdf_task(pdf_dir, image_dir, file_path, analysis_task_object.is_ocr, analysis_task_object.analysis_pdf_id) analysis_pdf_task(pdf_dir, image_dir, file_path, analysis_task_object.is_ocr, analysis_task_object.analysis_pdf_id)
else: else:
logger.info(f"all task finished!") logger.info('all task finished!')
projects/web_demo/web_demo/api/extentions.py → projects/web_demo/web_demo/api/extensions.py
View file @ 4dcf31b6
from contextlib import contextmanager
from common.error_types import ApiException
from flask import Flask, jsonify from flask import Flask, jsonify
from flask_restful import Api as _Api
from flask_cors import CORS from flask_cors import CORS
from flask_sqlalchemy import SQLAlchemy as _SQLAlchemy
from flask_migrate import Migrate
from contextlib import contextmanager
from flask_jwt_extended import JWTManager from flask_jwt_extended import JWTManager
from flask_marshmallow import Marshmallow from flask_marshmallow import Marshmallow
from common.error_types import ApiException from flask_migrate import Migrate
from werkzeug.exceptions import HTTPException from flask_restful import Api as _Api
from flask_sqlalchemy import SQLAlchemy as _SQLAlchemy
from loguru import logger from loguru import logger
from werkzeug.exceptions import HTTPException
class Api(_Api): class Api(_Api):
...@@ -21,23 +22,23 @@ class Api(_Api): ...@@ -21,23 +22,23 @@ class Api(_Api):
elif isinstance(e, HTTPException): elif isinstance(e, HTTPException):
code = e.code code = e.code
msg = e.description msg = e.description
msgZH = "服务异常，详细信息请查看日志" msgZH = '服务异常，详细信息请查看日志'
error_code = e.code error_code = e.code
else: else:
code = 500 code = 500
msg = str(e) msg = str(e)
error_code = 500 error_code = 500
msgZH = "服务异常，详细信息请查看日志" msgZH = '服务异常，详细信息请查看日志'
# 使用 loguru 记录异常信息 # 使用 loguru 记录异常信息
logger.opt(exception=e).error(f"An error occurred: {msg}") logger.opt(exception=e).error(f'An error occurred: {msg}')
return jsonify({ return jsonify({
"error": "Internal Server Error" if code == 500 else e.name, 'error': 'Internal Server Error' if code == 500 else e.name,
"msg": msg, 'msg': msg,
"msgZH": msgZH, 'msgZH': msgZH,
"code": code, 'code': code,
"error_code": error_code 'error_code': error_code
}), code }), code
...@@ -59,4 +60,4 @@ db = SQLAlchemy() ...@@ -59,4 +60,4 @@ db = SQLAlchemy()
migrate = Migrate() migrate = Migrate()
jwt = JWTManager() jwt = JWTManager()
ma = Marshmallow() ma = Marshmallow()
folder = app.config.get("REACT_APP_DIST") folder = app.config.get('REACT_APP_DIST')
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