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Merge pull request #998 from myhloli/dev 

test(unitest): Restore unit test cases
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tests/unittest/test_metascan_classify/test_classify.py 0 → 100644
View file @ 7b197fe2
import os
import pytest
from magic_pdf.filter.pdf_classify_by_type import classify_by_area, classify_by_text_len, classify_by_avg_words, \
classify_by_img_num, classify_by_text_layout, classify_by_img_narrow_strips
from magic_pdf.filter.pdf_meta_scan import get_pdf_page_size_pts, get_pdf_textlen_per_page, get_imgs_per_page
from tests.test_commons import get_docs_from_test_pdf, get_test_json_data
# 获取当前目录
current_directory = os.path.dirname(os.path.abspath(__file__))
'''
根据图片尺寸占页面面积的比例，判断是否为扫描版
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_area",
[
("the_eye/the_eye_cdn_00391653", True), # 特殊文字版1.每页存储所有图片，特点是图片占页面比例不大，每页展示可能为0也可能不止1张
("scihub/scihub_08400000/libgen.scimag08489000-08489999.zip_10.1016/0370-1573(90)90070-i", False), # 特殊扫描版2，每页存储的扫描页图片数量递增，特点是图占比大，每页展示1张
("zlib/zlib_17216416", False), # 特殊扫描版3，有的页面是一整张大图，有的页面是通过一条条小图拼起来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_wtl_00023799", False), # 特殊扫描版4，每一页都是一张张小图拼出来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_cdn_00328381", False), # 特殊扫描版5，每一页都是一张张小图拼出来的，存在多个小图多次重复使用情况，检测图片占比之前需要先按规则把小图拼成大图
("scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", False), # 特殊扫描版6，只有三页，其中两页是扫描版
("scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", False), # 特殊扫描版7，只有一页且由小图拼成大图
("scanned_detection/llm-raw-scihub-o.O-bf01427123", False), # 特殊扫描版8，只有3页且全是大图扫描版
("scihub/scihub_41200000/libgen.scimag41253000-41253999.zip_10.1080/00222938709460256", False), # 特殊扫描版12，头两页文字版且有一页没图片，后面扫描版11页
("scihub/scihub_37000000/libgen.scimag37068000-37068999.zip_10.1080/0015587X.1936.9718622", False) # 特殊扫描版13，头两页文字版且有一页没图片，后面扫描版3页
])
def test_classify_by_area(book_name, expected_bool_classify_by_area):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
page_width = int(median_width)
page_height = int(median_height)
img_sz_list = test_data[book_name]["expected_image_info"]
total_page = len(docs)
text_len_list = get_pdf_textlen_per_page(docs)
bool_classify_by_area = classify_by_area(total_page, page_width, page_height, img_sz_list, text_len_list)
assert bool_classify_by_area == expected_bool_classify_by_area
'''
广义上的文字版检测，任何一页大于100字，都认为为文字版
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_text_len",
[
("scihub/scihub_67200000/libgen.scimag67237000-67237999.zip_10.1515/crpm-2017-0020", True), # 文字版，少于50页
("scihub/scihub_83300000/libgen.scimag83306000-83306999.zip_10.1007/978-3-658-30153-8", True), # 文字版，多于50页
("zhongwenzaixian/zhongwenzaixian_65771414", False), # 完全无字的宣传册
])
def test_classify_by_text_len(book_name, expected_bool_classify_by_text_len):
docs = get_docs_from_test_pdf(book_name)
text_len_list = get_pdf_textlen_per_page(docs)
total_page = len(docs)
bool_classify_by_text_len = classify_by_text_len(text_len_list, total_page)
assert bool_classify_by_text_len == expected_bool_classify_by_text_len
'''
狭义上的文字版检测，需要平均每页字数大于200字
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_avg_words",
[
("zlib/zlib_21207669", False), # 扫描版，书末尾几页有大纲文字
("zlib/zlib_19012845", False), # 扫描版，好几本扫描书的集合，每本书末尾有一页文字页
("scihub/scihub_67200000/libgen.scimag67237000-67237999.zip_10.1515/crpm-2017-0020", True),# 正常文字版
("zhongwenzaixian/zhongwenzaixian_65771414", False), # 宣传册
("zhongwenzaixian/zhongwenzaixian_351879", False), # 图解书/无字or少字
("zhongwenzaixian/zhongwenzaixian_61357496_pdfvector", False), # 书法集
("zhongwenzaixian/zhongwenzaixian_63684541", False), # 设计图
("zhongwenzaixian/zhongwenzaixian_61525978", False), # 绘本
("zhongwenzaixian/zhongwenzaixian_63679729", False), # 摄影集
])
def test_classify_by_avg_words(book_name, expected_bool_classify_by_avg_words):
docs = get_docs_from_test_pdf(book_name)
text_len_list = get_pdf_textlen_per_page(docs)
bool_classify_by_avg_words = classify_by_avg_words(text_len_list)
assert bool_classify_by_avg_words == expected_bool_classify_by_avg_words
'''
这个规则只针对特殊扫描版1，因为扫描版1的图片信息都由于junk_list的原因被舍弃了，只能通过图片数量来判断
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_img_num",
[
("zlib/zlib_21370453", False), # 特殊扫描版1，每页都有所有扫描页图片，特点是图占比大，每页展示1至n张
("zlib/zlib_22115997", False), # 特殊扫描版2，类似特1，但是每页数量不完全相等
("zlib/zlib_21814957", False), # 特殊扫描版3，类似特1，但是每页数量不完全相等
("zlib/zlib_21814955", False), # 特殊扫描版4，类似特1，但是每页数量不完全相等
])
def test_classify_by_img_num(book_name, expected_bool_classify_by_img_num):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
img_num_list = get_imgs_per_page(docs)
img_sz_list = test_data[book_name]["expected_image_info"]
bool_classify_by_img_num = classify_by_img_num(img_sz_list, img_num_list)
assert bool_classify_by_img_num == expected_bool_classify_by_img_num
'''
排除纵向排版的pdf
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_text_layout",
[
("vertical_detection/三国演义_繁体竖排版", False), # 竖排版本1
("vertical_detection/净空法师_大乘无量寿", False), # 竖排版本2
("vertical_detection/om3006239", True), # 横排版本1
("vertical_detection/isit.2006.261791", True), # 横排版本2
])
def test_classify_by_text_layout(book_name, expected_bool_classify_by_text_layout):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
text_layout_per_page = test_data[book_name]["expected_text_layout"]
bool_classify_by_text_layout = classify_by_text_layout(text_layout_per_page)
assert bool_classify_by_text_layout == expected_bool_classify_by_text_layout
'''
通过检测页面是否由多个窄长条图像组成，来过滤特殊的扫描版
这个规则只对窄长条组成的pdf进行识别，而不会识别常规的大图扫描pdf
'''
@pytest.mark.parametrize("book_name, expected_bool_classify_by_img_narrow_strips",
[
("scihub/scihub_25900000/libgen.scimag25991000-25991999.zip_10.2307/40066695", False), # 特殊扫描版
("the_eye/the_eye_wtl_00023799", False), # 特殊扫描版4，每一页都是一张张小图拼出来的，检测图片占比之前需要先按规则把小图拼成大图
("the_eye/the_eye_cdn_00328381", False), # 特殊扫描版5，每一页都是一张张小图拼出来的，存在多个小图多次重复使用情况，检测图片占比之前需要先按规则把小图拼成大图
("scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", False), # 特殊扫描版7，只有一页且由小图拼成大图
("scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", True), # 特殊扫描版6，只有三页，其中两页是扫描版
("scanned_detection/llm-raw-scihub-o.O-bf01427123", True), # 特殊扫描版8，只有3页且全是大图扫描版
("scihub/scihub_53700000/libgen.scimag53724000-53724999.zip_10.1097/00129191-200509000-00018", True), # 特殊文本版，有一长条，但是只有一条
])
def test_classify_by_img_narrow_strips(book_name, expected_bool_classify_by_img_narrow_strips):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
img_sz_list = test_data[book_name]["expected_image_info"]
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
page_width = int(median_width)
page_height = int(median_height)
bool_classify_by_img_narrow_strips = classify_by_img_narrow_strips(page_width, page_height, img_sz_list)
assert bool_classify_by_img_narrow_strips == expected_bool_classify_by_img_narrow_strips
\ No newline at end of file
tests/unittest/test_metascan_classify/test_meta_scan.py 0 → 100644
View file @ 7b197fe2
import os
import pytest
from magic_pdf.filter.pdf_meta_scan import get_pdf_page_size_pts, get_image_info, get_pdf_text_layout_per_page, get_language
from tests.test_commons import get_docs_from_test_pdf, get_test_json_data
# 获取当前目录
current_directory = os.path.dirname(os.path.abspath(__file__))
'''
获取pdf的宽与高，宽和高各用一个list，分别取中位数
'''
@pytest.mark.parametrize("book_name, expected_width, expected_height",
[
("zlib/zlib_17058115", 795, 1002), # pdf中最大页与最小页差异极大个例
("the_eye/the_eye_wtl_00023799", 616, 785) # 采样的前50页存在中位数大小页面横竖旋转情况
])
def test_get_pdf_page_size_pts(book_name, expected_width, expected_height):
docs = get_docs_from_test_pdf(book_name)
median_width, median_height = get_pdf_page_size_pts(docs)
assert int(median_width) == expected_width
assert int(median_height) == expected_height
'''
获取pdf前50页的图片信息，为了提速，对特殊扫描版1的情况做了过滤，其余情况都正常取图片信息
'''
@pytest.mark.parametrize("book_name",
[
"zlib/zlib_21370453", # 特殊扫描版1，每页都有所有扫描页图片，特点是图占比大，每页展示1至n张
"the_eye/the_eye_cdn_00391653", # 特殊文字版1.每页存储所有图片，特点是图片占页面比例不大，每页展示可能为0也可能不止1张，这种pdf需要拿前10页抽样检测img大小和个数，如果符合需要清空junklist
"scihub/scihub_08400000/libgen.scimag08489000-08489999.zip_10.1016/0370-1573(90)90070-i", # 扫描版2，每页存储的扫描页图片数量递增，特点是图占比大，每页展示1张，需要清空junklist跑前50页图片信息用于分类判断
"zlib/zlib_17216416", # 特殊扫描版3，有的页面是一整张大图，有的页面是通过一条条小图拼起来的
"the_eye/the_eye_wtl_00023799", # 特殊扫描版4，每一页都是一张张小图拼出来的
"the_eye/the_eye_cdn_00328381", # 特殊扫描版5，每一页都是一张张小图拼出来的，但是存在多个小图多次重复使用情况
"scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991", # 特殊扫描版6，只有3页且其中两页是扫描页
"scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f", # 特殊扫描版7，只有一页，且是一张张小图拼出来的
"scanned_detection/llm-raw-scihub-o.O-bf01427123", # 特殊扫描版8，只有3页且全是大图扫描版
"zlib/zlib_22115997", # 特殊扫描版9，类似特1，但是每页数量不完全相等
"zlib/zlib_21814957", # 特殊扫描版10，类似特1，但是每页数量不完全相等
"zlib/zlib_21814955", # 特殊扫描版11，类似特1，但是每页数量不完全相等
"scihub/scihub_41200000/libgen.scimag41253000-41253999.zip_10.1080/00222938709460256", # 特殊扫描版12，头两页文字版且有一页没图片，后面扫描版11页
"scihub/scihub_37000000/libgen.scimag37068000-37068999.zip_10.1080/0015587X.1936.9718622" # 特殊扫描版13，头两页文字版且有一页没图片，后面扫描版3页
])
def test_get_image_info(book_name):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
page_width_pts, page_height_pts = get_pdf_page_size_pts(docs)
image_info, junk_img_bojids = get_image_info(docs, page_width_pts, page_height_pts)
assert image_info == test_data[book_name]["expected_image_info"]
assert junk_img_bojids == test_data[book_name]["expected_junk_img_bojids"]
'''
获取pdf前50页的文本布局信息，输出list，每个元素为一个页面的横竖排信息
'''
@pytest.mark.parametrize("book_name",
[
"vertical_detection/三国演义_繁体竖排版", # 竖排版本1
"vertical_detection/净空法师_大乘无量寿", # 竖排版本2
"vertical_detection/om3006239", # 横排版本1
"vertical_detection/isit.2006.261791" # 横排版本2
])
def test_get_text_layout_info(book_name):
test_data = get_test_json_data(current_directory, "test_metascan_classify_data.json")
docs = get_docs_from_test_pdf(book_name)
text_layout_info = get_pdf_text_layout_per_page(docs)
assert text_layout_info == test_data[book_name]["expected_text_layout"]
'''
获取pdf的语言信息
'''
@pytest.mark.parametrize("book_name, expected_language",
[
("scihub/scihub_05000000/libgen.scimag05023000-05023999.zip_10.1034/j.1601-0825.2003.02933.x", "en"), # 英文论文
])
def test_get_text_language_info(book_name, expected_language):
docs = get_docs_from_test_pdf(book_name)
text_language = get_language(docs)
assert text_language == expected_language
tests/unittest/test_metascan_classify/test_metascan_classify_data.json 0 → 100644
View file @ 7b197fe2
{
"zlib/zlib_21370453": {
"expected_image_info": [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []],
"expected_junk_img_bojids": [3259, 3268, 3358, 3359, 3360, 3361, 3362, 3363, 3364, 3365, 3366, 3367, 3269, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3376, 3377, 3270, 3378, 3379, 3380, 3381, 3382, 3383, 3384, 3385, 3386, 3387, 3271, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3396, 3397, 3272, 3398, 3399, 3400, 3401, 3402, 3403, 3404, 3405, 3406, 3407, 3273, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3274, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3275, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3276, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3447, 3277, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3260, 3278, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3279, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3280, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3281, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3282, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3283, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3284, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3285, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3286, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3546, 3547, 3287, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3261, 3288, 3558, 3559, 3560, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3262, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3263, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3264, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3265, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3266, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3267, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356, 3357, 3561, 3563, 3565, 3567, 3569, 3571, 3573, 3575, 3577, 3579, 3581, 3583, 3585, 3587, 3589, 3591, 3593, 3595, 3597, 3599, 3601, 3603, 3605, 3607, 3609, 3611, 3613, 3615, 3617, 3619, 3621, 3623, 3625, 3627, 3629, 3631, 3633, 3635, 3637, 3639, 3641, 3643, 3645, 3647, 3649, 3651, 3653, 3655, 3657, 3659, 3661, 3663, 3665, 3667, 3669, 3671, 3673, 3675, 3677, 3679, 3681, 3683, 3685, 3687, 3689, 3691, 3693, 3695, 3697, 3699, 3701, 3703, 3705, 3707, 3709, 3711, 3713, 3715, 3717, 3719, 3721, 3723, 3725, 3727, 3729, 3731, 3733, 3735, 3737, 3739, 3741, 3743, 3745, 3747, 3749, 3751, 3753, 3755, 3757, 3759, 3761, 3763, 3765, 3767, 3769, 3771, 3773, 3775, 3777, 3779, 3781, 3783, 3785, 3787, 3789, 3791, 3793, 3795, 3797, 3799, 3801, 3803, 3805, 3807, 3809, 3811, 3813, 3815, 3817, 3819, 3821, 3823, 3825, 3827, 3829, 3831, 3833, 3835, 3837, 3839, 3841, 3843, 3845, 3847, 3849, 3851, 3853, 3855, 3857, 3859, 3861, 3863, 3865, 3867, 3869, 3871, 3873, 3875, 3877, 3879, 3881, 3883, 3885, 3887, 3889, 3891, 3893, 3895, 3897, 3899, 3901, 3903, 3905, 3907, 3909, 3911, 3913, 3915, 3917, 3919, 3921, 3923, 3925, 3927, 3929, 3931, 3933, 3935, 3937, 3939, 3941, 3943, 3945, 3947, 3949, 3951, 3953, 3955, 3957, 3959, 3961, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3977, 3979, 3981, 3983, 3985, 3987, 3989, 3991, 3993, 3995, 3997, 3999, 4001, 4003, 4005, 4007, 4009, 4011, 4013, 4015, 4017, 4019, 4021, 4023, 4025, 4027, 4029, 4031, 4033, 4035, 4037, 4039, 4041, 4043, 4045, 4047, 4049, 4051, 4053, 4055, 4057, 4059, 4061, 4063, 4065, 4067, 4069, 4071, 4073, 4075, 4077, 4079, 4081, 4083, 4085, 4087, 4089, 4091, 4093, 4095, 4097, 4099, 4101, 4103, 4105, 4107, 4109, 4111, 4113, 4115, 4117, 4119, 4121, 4123, 4125, 4127, 4129]
},
"the_eye/the_eye_cdn_00391653" : {
"expected_image_info": [[[54, 233, 558, 670, 3]], [[442, 104, 535, 268, 7]], [], [], [], [], [], [], [], [[324, 128, 557, 476, 25]], [[54, 129, 288, 313, 29], [54, 476, 287, 630, 30], [324, 266, 557, 403, 31]], [[54, 54, 287, 374, 37], [324, 53, 557, 288, 38]], [[324, 66, 557, 447, 43]], [[54, 78, 288, 306, 47], [54, 331, 287, 648, 48], [324, 179, 557, 300, 49], [324, 387, 557, 580, 50]], [[54, 254, 557, 672, 57]], [[54, 53, 287, 211, 61], [54, 386, 287, 688, 62]], [[63, 54, 548, 360, 67]], [[62, 54, 549, 489, 71]], [[324, 53, 557, 399, 75]], [[54, 117, 557, 323, 79]], [[68, 60, 275, 272, 83], [60, 347, 280, 617, 84], [340, 354, 541, 633, 85]], [], [[54, 442, 557, 687, 93]], [[49, 66, 282, 439, 97], [324, 441, 558, 658, 98]], [[54, 241, 287, 315, 103], [66, 516, 545, 690, 104]], [[54, 53, 557, 655, 109]], [[54, 50, 557, 689, 113]], [[324, 79, 557, 656, 117]], [[109, 54, 502, 685, 121]], [[54, 379, 288, 642, 125]], [], [[156, 49, 455, 617, 131]], [[54, 40, 557, 679, 135]], [[54, 166, 557, 665, 139]], [[67, 45, 544, 691, 143]], [[54, 43, 557, 694, 147]], [[54, 53, 557, 372, 151]], [], [], [], [], [[54, 153, 557, 659, 163]], [[54, 53, 287, 223, 167], [324, 273, 557, 422, 168], [324, 430, 557, 554, 169]], [[324, 466, 557, 641, 175]], [[49, 279, 283, 390, 179], [324, 54, 558, 568, 180]], [[54, 66, 287, 477, 185]], [[54, 87, 557, 459, 189]], [[173, 47, 438, 693, 193]], [[54, 79, 557, 571, 197]], [[54, 54, 557, 679, 201]]],
"expected_junk_img_bojids": []
},
"scihub/scihub_08400000/libgen.scimag08489000-08489999.zip_10.1016/0370-1573(90)90070-i" : {
"expected_image_info": [[[-10, -10, 590, 729, 1273]], [[-10, -10, 590, 729, 1277]], [[-10, -10, 590, 729, 807]], [[-10, -10, 590, 729, 809]], [[-10, -10, 590, 729, 811]], [[-10, -10, 590, 729, 813]], [[-10, -10, 590, 729, 815]], [[-10, -10, 590, 729, 817]], [[-10, -10, 590, 729, 819]], [[-10, -10, 590, 729, 821]], [[-10, -10, 590, 729, 823]], [[-10, -10, 590, 729, 825]], [[-10, -10, 590, 729, 827]], [[-10, -10, 590, 729, 829]], [[-10, -10, 590, 729, 831]], [[-10, -10, 590, 729, 833]], [[-10, -10, 590, 729, 835]], [[-10, -10, 590, 729, 837]], [[-10, -10, 590, 729, 839]], [[-10, -10, 590, 729, 841]], [[-10, -10, 590, 729, 843]], [[-10, -10, 590, 729, 845]], [[-10, -10, 590, 729, 847]], [[-10, -10, 590, 729, 849]], [[-10, -10, 590, 729, 851]], [[-10, -10, 590, 729, 853]], [[-10, -10, 590, 729, 855]], [[-10, -10, 590, 729, 857]], [[-10, -10, 590, 729, 859]], [[-10, -10, 590, 729, 861]], [[-10, -10, 590, 729, 863]], [[-10, -10, 590, 729, 865]], [[-10, -10, 590, 729, 867]], [[-10, -10, 590, 729, 869]], [[-10, -10, 590, 729, 871]], [[-10, -10, 590, 729, 873]], [[-10, -10, 590, 729, 875]], [[-10, -10, 590, 729, 877]], [[-10, -10, 590, 729, 879]], [[-10, -10, 590, 729, 881]], [[-10, -10, 590, 729, 883]], [[-10, -10, 590, 729, 885]], [[-10, -10, 590, 729, 887]], [[-10, -10, 590, 729, 889]], [[-10, -10, 590, 729, 891]], [[-10, -10, 590, 729, 893]], [[-10, -10, 590, 729, 895]], [[-10, -10, 590, 729, 897]], [[-10, -10, 590, 729, 899]], [[-10, -10, 590, 729, 901]]],
"expected_junk_img_bojids": []
},
"zlib/zlib_17216416" : {
"expected_image_info": [[[0, 0, 531, 720, 36742]], [[0, 0, 529, 28, 59], [0, 28, 529, 57, 61], [0, 57, 529, 86, 63], [0, 86, 529, 115, 65], [0, 114, 529, 143, 67], [0, 143, 529, 172, 69], [0, 172, 529, 201, 71], [0, 201, 529, 230, 73], [0, 230, 529, 259, 75], [0, 259, 529, 287, 77], [0, 287, 529, 316, 79], [0, 345, 529, 374, 83], [0, 403, 529, 431, 87], [0, 431, 529, 460, 89], [0, 547, 529, 575, 97], [0, 575, 529, 604, 99], [0, 604, 529, 633, 101], [0, 633, 529, 662, 103], [0, 662, 529, 691, 105], [0, 691, 529, 719, 107], [0, 719, 529, 737, 109]], [[0, 0, 519, 28, 131], [0, 28, 519, 57, 133], [0, 57, 519, 86, 135], [0, 86, 519, 115, 137], [0, 115, 519, 144, 139], [0, 143, 519, 172, 141], [0, 172, 519, 201, 143], [0, 201, 519, 230, 145], [0, 230, 519, 259, 147], [0, 288, 519, 316, 151], [0, 316, 519, 345, 153], [0, 345, 519, 374, 155], [0, 374, 519, 403, 157], [0, 403, 519, 432, 159], [0, 460, 519, 489, 163], [0, 489, 519, 518, 165], [0, 518, 519, 547, 167], [0, 547, 519, 576, 169], [0, 576, 519, 604, 171], [0, 604, 519, 633, 173], [0, 633, 519, 662, 175], [0, 662, 519, 691, 177], [0, 691, 519, 720, 179]], [[0, 0, 528, 28, 201], [0, 28, 528, 57, 203], [0, 57, 528, 86, 205], [0, 85, 528, 114, 207], [0, 114, 528, 143, 209], [0, 143, 528, 172, 211], [0, 172, 528, 201, 213], [0, 201, 528, 230, 215], [0, 229, 528, 258, 217], [0, 258, 528, 287, 219], [0, 287, 528, 316, 221], [0, 316, 528, 345, 223], [0, 345, 528, 374, 225], [0, 374, 528, 402, 227], [0, 402, 528, 431, 229], [0, 431, 528, 460, 231], [0, 460, 528, 489, 233], [0, 489, 528, 518, 235], [0, 518, 528, 546, 237], [0, 546, 528, 575, 239], [0, 575, 528, 604, 241], [0, 604, 528, 633, 243], [0, 633, 528, 662, 245], [0, 662, 528, 690, 247], [0, 690, 528, 719, 249], [0, 719, 528, 737, 251]], [[0, 0, 528, 29, 273], [0, 57, 528, 86, 277], [0, 86, 528, 115, 279], [0, 115, 528, 144, 281], [0, 144, 528, 173, 283], [0, 173, 528, 202, 285], [0, 201, 528, 230, 287], [0, 230, 528, 259, 289], [0, 259, 528, 288, 291], [0, 288, 528, 317, 293], [0, 317, 528, 346, 295], [0, 346, 528, 374, 297], [0, 374, 528, 403, 299], [0, 403, 528, 432, 301], [0, 432, 528, 461, 303], [0, 576, 528, 605, 313], [0, 662, 528, 691, 319], [0, 691, 528, 720, 321], [0, 720, 528, 736, 323]], [[0, 0, 529, 29, 345], [0, 29, 529, 58, 347], [0, 57, 529, 86, 349], [0, 86, 529, 115, 351], [0, 115, 529, 144, 353], [0, 144, 529, 173, 355], [0, 173, 529, 202, 357], [0, 201, 529, 230, 359], [0, 230, 529, 259, 361], [0, 259, 529, 288, 363], [0, 288, 529, 317, 365], [0, 317, 529, 346, 367], [0, 346, 529, 374, 369], [0, 374, 529, 403, 371], [0, 403, 529, 432, 373], [0, 432, 529, 461, 375], [0, 461, 529, 490, 377], [0, 490, 529, 518, 379], [0, 518, 529, 547, 381], [0, 547, 529, 576, 383], [0, 576, 529, 605, 385], [0, 605, 529, 634, 387], [0, 634, 529, 662, 389], [0, 662, 529, 691, 391], [0, 691, 529, 720, 393], [0, 720, 529, 740, 395]], [[0, 0, 519, 29, 417], [0, 29, 519, 58, 419], [0, 58, 519, 88, 421], [0, 88, 519, 117, 423], [0, 117, 519, 147, 425], [0, 147, 519, 176, 427], [0, 176, 519, 205, 429], [0, 205, 519, 235, 431], [0, 235, 519, 264, 433], [0, 264, 519, 294, 435], [0, 294, 519, 323, 437], [0, 323, 519, 352, 439], [0, 352, 519, 382, 441], [0, 382, 519, 411, 443], [0, 411, 519, 441, 445], [0, 441, 519, 470, 447], [0, 470, 519, 499, 449], [0, 499, 519, 529, 451], [0, 529, 519, 558, 453], [0, 558, 519, 588, 455], [0, 588, 519, 617, 457], [0, 646, 519, 676, 461], [0, 705, 519, 734, 465]], [[0, 0, 529, 28, 487], [0, 28, 529, 57, 489], [0, 57, 529, 86, 491], [0, 86, 529, 115, 493], [0, 115, 529, 144, 495], [0, 143, 529, 172, 497], [0, 172, 529, 201, 499], [0, 201, 529, 230, 501], [0, 230, 529, 259, 503], [0, 259, 529, 288, 505], [0, 288, 529, 316, 507], [0, 316, 529, 345, 509], [0, 345, 529, 374, 511], [0, 374, 529, 403, 513], [0, 403, 529, 432, 515], [0, 432, 529, 460, 517], [0, 460, 529, 489, 519], [0, 489, 529, 518, 521], [0, 518, 529, 547, 523], [0, 547, 529, 576, 525], [0, 576, 529, 604, 527], [0, 604, 529, 633, 529], [0, 633, 529, 662, 531], [0, 662, 529, 691, 533], [0, 691, 529, 720, 535], [0, 720, 529, 739, 537]], [[0, 0, 519, 29, 559], [0, 29, 519, 58, 561], [0, 57, 519, 86, 563], [0, 86, 519, 115, 565], [0, 115, 519, 144, 567], [0, 144, 519, 173, 569], [0, 173, 519, 202, 571], [0, 201, 519, 230, 573], [0, 230, 519, 259, 575], [0, 259, 519, 288, 577], [0, 288, 519, 317, 579], [0, 317, 519, 346, 581], [0, 346, 519, 374, 583], [0, 374, 519, 403, 585], [0, 403, 519, 432, 587], [0, 432, 519, 461, 589], [0, 461, 519, 490, 591], [0, 490, 519, 518, 593], [0, 518, 519, 547, 595], [0, 547, 519, 576, 597], [0, 576, 519, 605, 599], [0, 605, 519, 634, 601], [0, 634, 519, 662, 603], [0, 691, 519, 720, 607]], [[0, 0, 519, 28, 629], [0, 28, 519, 57, 631], [0, 57, 519, 86, 633], [0, 86, 519, 115, 635], [0, 114, 519, 143, 637], [0, 143, 519, 172, 639], [0, 172, 519, 201, 641], [0, 201, 519, 230, 643], [0, 230, 519, 259, 645], [0, 259, 519, 287, 647], [0, 287, 519, 316, 649], [0, 316, 519, 345, 651], [0, 345, 519, 374, 653], [0, 374, 519, 403, 655], [0, 403, 519, 431, 657], [0, 431, 519, 460, 659], [0, 460, 519, 489, 661], [0, 489, 519, 518, 663], [0, 518, 519, 547, 665], [0, 547, 519, 575, 667], [0, 575, 519, 604, 669], [0, 604, 519, 633, 671], [0, 633, 519, 662, 673], [0, 662, 519, 691, 675], [0, 691, 519, 719, 677]], [[0, 0, 519, 29, 699], [0, 29, 519, 58, 701], [0, 58, 519, 88, 703], [0, 88, 519, 117, 705], [0, 117, 519, 147, 707], [0, 147, 519, 176, 709], [0, 176, 519, 205, 711], [0, 205, 519, 235, 713], [0, 235, 519, 264, 715], [0, 264, 519, 294, 717], [0, 294, 519, 323, 719], [0, 323, 519, 352, 721], [0, 352, 519, 382, 723], [0, 382, 519, 411, 725], [0, 411, 519, 441, 727], [0, 441, 519, 470, 729], [0, 470, 519, 499, 731], [0, 499, 519, 529, 733], [0, 529, 519, 558, 735], [0, 558, 519, 588, 737], [0, 588, 519, 617, 739], [0, 617, 519, 646, 741], [0, 646, 519, 676, 743], [0, 705, 519, 734, 747]], [[0, 0, 519, 28, 769], [0, 28, 519, 57, 771], [0, 57, 519, 86, 773], [0, 86, 519, 115, 775], [0, 114, 519, 143, 777], [0, 143, 519, 172, 779], [0, 172, 519, 201, 781], [0, 201, 519, 230, 783], [0, 230, 519, 259, 785], [0, 259, 519, 287, 787], [0, 287, 519, 316, 789], [0, 316, 519, 345, 791], [0, 345, 519, 374, 793], [0, 374, 519, 403, 795], [0, 403, 519, 431, 797], [0, 431, 519, 460, 799], [0, 460, 519, 489, 801], [0, 489, 519, 518, 803], [0, 518, 519, 547, 805], [0, 547, 519, 575, 807], [0, 575, 519, 604, 809], [0, 604, 519, 633, 811], [0, 633, 519, 662, 813], [0, 691, 519, 719, 817]], [[0, 0, 523, 28, 839], [0, 28, 523, 57, 841], [0, 57, 523, 86, 843], [0, 86, 523, 115, 845], [0, 115, 523, 144, 847], [0, 143, 523, 172, 849], [0, 172, 523, 201, 851], [0, 201, 523, 230, 853], [0, 230, 523, 259, 855], [0, 259, 523, 288, 857], [0, 288, 523, 316, 859], [0, 316, 523, 345, 861], [0, 345, 523, 374, 863], [0, 374, 523, 403, 865], [0, 403, 523, 432, 867], [0, 432, 523, 460, 869], [0, 460, 523, 489, 871], [0, 489, 523, 518, 873], [0, 518, 523, 547, 875], [0, 547, 523, 576, 877], [0, 576, 523, 604, 879], [0, 604, 523, 633, 881], [0, 633, 523, 662, 883], [0, 662, 523, 691, 885], [0, 720, 523, 734, 889]], [[0, 0, 525, 28, 911], [0, 57, 525, 86, 915], [0, 85, 525, 114, 917], [0, 114, 525, 143, 919], [0, 143, 525, 172, 921], [0, 172, 525, 201, 923], [0, 201, 525, 230, 925], [0, 229, 525, 258, 927], [0, 258, 525, 287, 929], [0, 287, 525, 316, 931], [0, 316, 525, 345, 933], [0, 345, 525, 374, 935], [0, 374, 525, 402, 937], [0, 431, 525, 460, 941], [0, 460, 525, 489, 943], [0, 489, 525, 518, 945], [0, 518, 525, 546, 947], [0, 546, 525, 575, 949], [0, 575, 525, 604, 951], [0, 662, 525, 690, 957], [0, 719, 525, 735, 961]], [[0, 0, 519, 28, 983], [0, 28, 519, 57, 985], [0, 57, 519, 86, 987], [0, 86, 519, 115, 989], [0, 115, 519, 144, 991], [0, 143, 519, 172, 993], [0, 172, 519, 201, 995], [0, 201, 519, 230, 997], [0, 230, 519, 259, 999], [0, 259, 519, 288, 1001], [0, 288, 519, 316, 1003], [0, 316, 519, 345, 1005], [0, 345, 519, 374, 1007], [0, 374, 519, 403, 1009], [0, 403, 519, 432, 1011], [0, 432, 519, 460, 1013], [0, 460, 519, 489, 1015], [0, 489, 519, 518, 1017], [0, 518, 519, 547, 1019], [0, 576, 519, 604, 1023], [0, 633, 519, 662, 1027], [0, 691, 519, 720, 1031]], [[0, 0, 525, 29, 1053], [0, 57, 525, 86, 1057], [0, 173, 525, 202, 1065], [0, 201, 525, 230, 1067], [0, 230, 525, 259, 1069], [0, 259, 525, 288, 1071], [0, 288, 525, 317, 1073], [0, 317, 525, 346, 1075], [0, 346, 525, 374, 1077], [0, 374, 525, 403, 1079], [0, 403, 525, 432, 1081], [0, 432, 525, 461, 1083], [0, 461, 525, 490, 1085], [0, 490, 525, 518, 1087], [0, 518, 525, 547, 1089], [0, 547, 525, 576, 1091], [0, 576, 525, 605, 1093], [0, 605, 525, 634, 1095], [0, 634, 525, 662, 1097], [0, 662, 525, 691, 1099], [0, 691, 525, 720, 1101], [0, 720, 525, 736, 1103]], [[0, 0, 519, 28, 1125], [0, 28, 519, 57, 1127], [0, 57, 519, 86, 1129], [0, 86, 519, 115, 1131], [0, 114, 519, 143, 1133], [0, 143, 519, 172, 1135], [0, 172, 519, 201, 1137], [0, 201, 519, 230, 1139], [0, 230, 519, 259, 1141], [0, 259, 519, 287, 1143], [0, 287, 519, 316, 1145], [0, 316, 519, 345, 1147], [0, 345, 519, 374, 1149], [0, 374, 519, 403, 1151], [0, 403, 519, 431, 1153], [0, 431, 519, 460, 1155], [0, 460, 519, 489, 1157], [0, 489, 519, 518, 1159], [0, 518, 519, 547, 1161], [0, 547, 519, 575, 1163], [0, 575, 519, 604, 1165], [0, 604, 519, 633, 1167], [0, 633, 519, 662, 1169], [0, 662, 519, 691, 1171], [0, 691, 519, 719, 1173]], [[0, 0, 525, 29, 1233], [0, 57, 525, 86, 1237], [0, 86, 525, 115, 1239], [0, 115, 525, 144, 1241], [0, 144, 525, 173, 1243], [0, 172, 525, 201, 1245], [0, 201, 525, 230, 1247], [0, 230, 525, 259, 1249], [0, 259, 525, 288, 1251], [0, 288, 525, 317, 1253], [0, 317, 525, 345, 1255], [0, 345, 525, 374, 1257], [0, 374, 525, 403, 1259], [0, 403, 525, 432, 1261], [0, 432, 525, 461, 1263], [0, 461, 525, 489, 1265], [0, 489, 525, 518, 1267], [0, 518, 525, 547, 1269], [0, 547, 525, 576, 1271], [0, 576, 525, 605, 1273], [0, 605, 525, 633, 1275], [0, 633, 525, 662, 1277], [0, 662, 525, 691, 1279], [0, 720, 525, 736, 1283]], [[0, 0, 523, 29, 1305], [0, 57, 523, 86, 1309], [0, 86, 523, 115, 1311], [0, 115, 523, 144, 1313], [0, 144, 523, 173, 1315], [0, 173, 523, 202, 1317], [0, 201, 523, 230, 1319], [0, 230, 523, 259, 1321], [0, 259, 523, 288, 1323], [0, 288, 523, 317, 1325], [0, 317, 523, 346, 1327], [0, 346, 523, 374, 1329], [0, 374, 523, 403, 1331], [0, 403, 523, 432, 1333], [0, 432, 523, 461, 1335], [0, 461, 523, 490, 1337], [0, 490, 523, 518, 1339], [0, 518, 523, 547, 1341], [0, 547, 523, 576, 1343], [0, 576, 523, 605, 1345], [0, 605, 523, 634, 1347], [0, 634, 523, 662, 1349], [0, 662, 523, 691, 1351], [0, 720, 523, 736, 1355]], [[0, 0, 528, 28, 1377], [0, 57, 528, 86, 1381], [0, 85, 528, 114, 1383], [0, 114, 528, 143, 1385], [0, 143, 528, 172, 1387], [0, 172, 528, 201, 1389], [0, 201, 528, 230, 1391], [0, 229, 528, 258, 1393], [0, 258, 528, 287, 1395], [0, 287, 528, 316, 1397], [0, 316, 528, 345, 1399], [0, 345, 528, 374, 1401], [0, 374, 528, 402, 1403], [0, 402, 528, 431, 1405], [0, 431, 528, 460, 1407], [0, 460, 528, 489, 1409], [0, 489, 528, 518, 1411], [0, 518, 528, 546, 1413], [0, 546, 528, 575, 1415], [0, 575, 528, 604, 1417], [0, 604, 528, 633, 1419], [0, 633, 528, 662, 1421], [0, 662, 528, 690, 1423], [0, 719, 528, 737, 1427]], [[0, 0, 519, 28, 1449], [0, 28, 519, 57, 1451], [0, 57, 519, 86, 1453], [0, 86, 519, 115, 1455], [0, 114, 519, 143, 1457], [0, 143, 519, 172, 1459], [0, 172, 519, 201, 1461], [0, 201, 519, 230, 1463], [0, 230, 519, 259, 1465], [0, 259, 519, 287, 1467], [0, 287, 519, 316, 1469], [0, 316, 519, 345, 1471], [0, 345, 519, 374, 1473], [0, 374, 519, 403, 1475], [0, 403, 519, 431, 1477], [0, 431, 519, 460, 1479], [0, 460, 519, 489, 1481], [0, 489, 519, 518, 1483], [0, 518, 519, 547, 1485], [0, 547, 519, 575, 1487], [0, 575, 519, 604, 1489], [0, 604, 519, 633, 1491], [0, 633, 519, 662, 1493], [0, 662, 519, 691, 1495], [0, 691, 519, 719, 1497]], [[0, 0, 529, 28, 1519], [0, 57, 529, 86, 1523], [0, 86, 529, 115, 1525], [0, 115, 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352, 3535], [0, 352, 519, 382, 3537], [0, 382, 519, 411, 3539], [0, 411, 519, 441, 3541], [0, 441, 519, 470, 3543], [0, 470, 519, 499, 3545], [0, 499, 519, 529, 3547], [0, 529, 519, 558, 3549], [0, 558, 519, 588, 3551], [0, 588, 519, 617, 3553], [0, 617, 519, 646, 3555], [0, 646, 519, 676, 3557], [0, 676, 519, 705, 3559], [0, 705, 519, 734, 3561]], [[0, 0, 527, 29, 3583], [0, 29, 527, 58, 3585], [0, 57, 527, 86, 3587], [0, 86, 527, 115, 3589], [0, 115, 527, 144, 3591], [0, 144, 527, 173, 3593], [0, 173, 527, 202, 3595], [0, 201, 527, 230, 3597], [0, 230, 527, 259, 3599], [0, 259, 527, 288, 3601], [0, 288, 527, 317, 3603], [0, 317, 527, 346, 3605], [0, 346, 527, 374, 3607], [0, 374, 527, 403, 3609], [0, 403, 527, 432, 3611], [0, 432, 527, 461, 3613], [0, 461, 527, 490, 3615], [0, 490, 527, 518, 3617], [0, 518, 527, 547, 3619], [0, 576, 527, 605, 3623], [0, 605, 527, 634, 3625], [0, 634, 527, 662, 3627], [0, 662, 527, 691, 3629], [0, 720, 527, 738, 3633]]],
"expected_junk_img_bojids": []
},
"the_eye/the_eye_wtl_00023799" : {
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619, 544, 120], [0, 544, 619, 599, 122], [0, 599, 619, 653, 124], [0, 653, 619, 708, 126], [0, 708, 619, 762, 128], [0, 762, 619, 791, 130]], [[0, 0, 616, 54, 167], [0, 54, 616, 108, 169], [0, 108, 616, 163, 171], [0, 163, 616, 217, 173], [0, 217, 616, 272, 175], [0, 272, 616, 326, 177], [0, 326, 616, 381, 179], [0, 381, 616, 435, 181], [0, 435, 616, 490, 183], [0, 490, 616, 544, 185], [0, 544, 616, 599, 187], [0, 599, 616, 653, 189], [0, 653, 616, 708, 191], [0, 708, 616, 762, 193], [0, 762, 616, 787, 195]], [[0, 0, 610, 55, 232], [0, 55, 610, 111, 234], [0, 111, 610, 166, 236], [0, 166, 610, 222, 238], [0, 222, 610, 278, 240], [0, 278, 610, 333, 242], [0, 333, 610, 389, 244], [0, 389, 610, 445, 246], [0, 445, 610, 501, 248], [0, 501, 610, 556, 250], [0, 556, 610, 612, 252], [0, 612, 610, 668, 254], [0, 668, 610, 723, 256], [0, 723, 610, 779, 258], [0, 779, 610, 786, 260]], [[0, 0, 610, 55, 297], [0, 55, 610, 111, 299], [0, 111, 610, 166, 301], [0, 166, 610, 221, 303], [0, 221, 610, 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"expected_junk_img_bojids": []
},
"vertical_detection/三国演义_繁体竖排版" : {
"expected_text_layout": [
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]
},
"vertical_detection/净空法师_大乘无量寿" : {
"expected_text_layout" : [
"vertical", "unknow", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical", "vertical"
]
},
"vertical_detection/om3006239" : {
"expected_text_layout" : [
"horizontal", "horizontal", "horizontal", "horizontal", "horizontal", "horizontal", "horizontal", "horizontal", "horizontal", "horizontal"
]
},
"vertical_detection/isit.2006.261791" : {
"expected_text_layout" : [
"horizontal", "horizontal", "horizontal", "horizontal", "horizontal"
]
},
"the_eye/the_eye_cdn_00328381" : {
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"expected_junk_img_bojids": []
},
"scihub/scihub_25800000/libgen.scimag25889000-25889999.zip_10.2307/4153991" : {
"expected_image_info": [[[50,726,110,792,21],[50,80,202,230,22]],[[0,0,468,720,17]],[[0,0,468,720,31]]],
"expected_junk_img_bojids": []
},
"scanned_detection/llm-raw-scihub-o.O-0584-8539%2891%2980165-f" : {
"expected_image_info": [[[0, 0, 504, 29, 33], [0, 29, 504, 59, 35], [0, 59, 504, 89, 37], [0, 89, 504, 119, 39], [0, 119, 504, 148, 41], [0, 148, 504, 178, 43], [0, 178, 504, 208, 45], [0, 208, 504, 238, 47], [0, 238, 504, 267, 49], [0, 267, 504, 297, 51], [0, 297, 504, 327, 53], [0, 327, 504, 357, 55], [0, 357, 504, 386, 57], [0, 386, 504, 416, 59], [0, 416, 504, 446, 61], [0, 446, 504, 476, 63], [0, 476, 504, 505, 65], [0, 505, 504, 535, 67], [0, 535, 504, 565, 69], [0, 565, 504, 595, 71], [0, 595, 504, 624, 73], [0, 624, 504, 654, 75], [0, 654, 504, 684, 77], [0, 684, 504, 714, 79], [0, 714, 504, 744, 81], [0, 773, 504, 792, 83]]],
"expected_junk_img_bojids": []
},
"scanned_detection/llm-raw-scihub-o.O-bf01427123" : {
"expected_image_info": [[[0, 0, 532, 698, 76]], [[0, 0, 532, 698, 14]], [[0, 0, 532, 698, 30]]],
"expected_junk_img_bojids": []
},
"scihub/scihub_25900000/libgen.scimag25991000-25991999.zip_10.2307/40066695" : {
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},
"scihub/scihub_53700000/libgen.scimag53724000-53724999.zip_10.1097/00129191-200509000-00018": {
"expected_image_info": [[[-2, 0, 603, 80, 24]], [[-3, 0, 602, 80, 24]]]
},
"zlib/zlib_22115997": {
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"expected_junk_img_bojids": [3830, 3831, 3832, 3837, 3833, 3838, 3842, 3834, 3835, 3836, 3839, 3840, 3841, 3872, 3854, 3868, 3850, 3869, 3847, 3873, 3866, 3870, 3857, 3858, 3871, 3855, 3849, 3874, 3843, 3859, 3844, 3853, 3860, 3856, 3851, 3861, 3862, 3845, 3846, 3863, 3848, 3864, 3865, 3867, 3852, 3899, 3906, 3894, 3903, 3902, 3901, 3895, 3905, 3884, 3879, 3904, 3875, 3876, 3889, 3888, 3877, 3882, 3885, 3878, 3896, 3880, 3881, 3890, 3900, 3883, 3891, 3893, 3886, 3887, 3892, 3897, 3898, 3918, 3922, 3936, 3913, 3938, 3920, 3908, 3937, 3909, 3926, 3923, 3919, 3933, 3907, 3934, 3914, 3921, 3931, 3915, 3916, 3924, 3925, 3910, 3927, 3935, 3928, 3929, 3930, 3932, 3911, 3912, 3917, 3965, 3960, 3942, 3951, 3958, 3946, 3952, 3947, 3939, 3963, 3943, 3968, 3969, 3950, 3964, 3961, 3962, 3944, 3966, 3970, 3959, 3967, 3940, 3941, 3945, 3948, 3953, 3949, 3954, 3955, 3956, 3957, 3982, 3991, 3998, 3984, 3975, 4002, 3974, 3999, 3976, 4000, 3985, 3992, 4001, 3971, 3989, 3988, 3983, 3977, 3980, 3997, 3986, 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},
"zlib/zlib_21814957": {
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tests/unittest/test_model/assets/test_01.model.json 0 → 100644
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"text": "normal tissue ; M : PBR322/Hae II Marker ; ssDNA"
},
{
"category_id": 15,
"poly": [
1207.0,
1979.0,
1422.0,
1979.0,
1422.0,
2004.0,
1207.0,
2004.0
],
"score": 0.97,
"text": "Single-stranded DNA ; ds-"
},
{
"category_id": 15,
"poly": [
755.0,
2015.0,
797.0,
2015.0,
797.0,
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755.0,
2038.0
],
"score": 1.0,
"text": "DNA"
},
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"category_id": 15,
"poly": [
814.0,
2015.0,
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2015.0,
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2038.0
],
"score": 0.98,
"text": "Double-stranded DNA"
},
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"poly": [
71.0,
1880.0,
698.0,
1880.0,
698.0,
1902.0,
71.0,
1902.0
],
"score": 0.96,
"text": "Fig 2Alterations of PCR amplified products of FHIT exon 3,4,5 and"
},
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"poly": [
28.0,
1916.0,
399.0,
1916.0,
399.0,
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],
"score": 0.98,
"text": "microsatellite marker D3S1300、D3S1312.A"
},
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"poly": [
417.0,
1916.0,
701.0,
1916.0,
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],
"score": 0.9,
"text": "Deletion of exon5(arrows）;B :"
},
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"poly": [
29.0,
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1953.0,
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],
"score": 0.95,
"text": "Deletion of exon 3 A( arrows）;C : Deletion of microsatellite marker D3S1300,"
},
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"poly": [
28.0,
1989.0,
134.0,
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2014.0
],
"score": 1.0,
"text": "D3S1312.T"
},
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"category_id": 15,
"poly": [
149.0,
1989.0,
696.0,
1989.0,
696.0,
2014.0,
149.0,
2014.0
],
"score": 0.96,
"text": "Tumor ; N : Corresponding normal tissue ; L : Corresponding lymph"
},
{
"category_id": 15,
"poly": [
30.0,
2027.0,
634.0,
2027.0,
634.0,
2047.0,
30.0,
2047.0
],
"score": 0.94,
"text": "node tissue;M :DL2000 DNA marker;L1:Lewis ;A :A549;S SPAC-1"
},
{
"category_id": 15,
"poly": [
801.0,
1259.0,
1427.0,
1259.0,
1427.0,
1280.0,
801.0,
1280.0
],
"score": 0.94,
"text": "Fig 3SSCP analysis of FHIT exon 3.The arrow indicateda deletion of"
},
{
"category_id": 15,
"poly": [
757.0,
1294.0,
1424.0,
1294.0,
1424.0,
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757.0,
1318.0
],
"score": 0.96,
"text": "exon 3 of 41T. T : Tumor tissue ; N : Corresponding normal tissue ; M PBR322/"
},
{
"category_id": 15,
"poly": [
755.0,
1329.0,
1218.0,
1329.0,
1218.0,
1355.0,
755.0,
1355.0
],
"score": 0.95,
"text": "Hae Il Marker / ssDNA : Single-stranded DNA ; dsDNA"
},
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"category_id": 15,
"poly": [
1236.0,
1329.0,
1418.0,
1329.0,
1418.0,
1355.0,
1236.0,
1355.0
],
"score": 1.0,
"text": "Double-strandedDNA"
},
{
"category_id": 15,
"poly": [
910.0,
1217.0,
1269.0,
1217.0,
1269.0,
1241.0,
910.0,
1241.0
],
"score": 1.0,
"text": "图3FHIT基因外显子3的SSCP分析"
},
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"category_id": 15,
"poly": [
909.0,
1904.0,
1269.0,
1904.0,
1269.0,
1927.0,
909.0,
1927.0
],
"score": 1.0,
"text": "图4FHIT基因外显子4的SSCP分析"
},
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"category_id": 15,
"poly": [
374.0,
563.0,
1077.0,
563.0,
1077.0,
583.0,
374.0,
583.0
],
"score": 0.99,
"text": "图1FHIT基因外显子3、4、5、8和微卫星灶的PCR扩增产物琼脂糖电泳图"
},
{
"category_id": 15,
"poly": [
1351.0,
81.0,
1376.0,
81.0,
1376.0,
102.0,
1351.0,
102.0
],
"score": 1.0,
"text": "13"
},
{
"category_id": 15,
"poly": [
207.0,
600.0,
1245.0,
600.0,
1245.0,
624.0,
207.0,
624.0
],
"score": 0.96,
"text": "Fig 1 Agarose electrophoresis of PCR products of exor( A)3 ,4 ,5 ,8 and three microsatellite markers( B）of FHIT gene"
},
{
"category_id": 15,
"poly": [
309.0,
634.0,
1142.0,
634.0,
1142.0,
662.0,
309.0,
662.0
],
"score": 0.97,
"text": "M1 :DL2000 DNA marker ; M2 PBR322/Hae Il marker ; T :Tumor ; N :Corresponding normal tissue"
},
{
"category_id": 15,
"poly": [
73.0,
1840.0,
651.0,
1840.0,
651.0,
1864.0,
73.0,
1864.0
],
"score": 1.0,
"text": "图2FHIT基因外显子和微卫星灶PCR扩增产物缺失电泳图"
},
{
"category_id": 15,
"poly": [
207.0,
600.0,
1245.0,
600.0,
1245.0,
625.0,
207.0,
625.0
],
"score": 0.96,
"text": "Fig 1 Agarose electrophoresis of PCR products of exor A)3 ,4 ,5 ,8 and three microsatellite markers( B)of FHIT gene"
},
{
"category_id": 15,
"poly": [
309.0,
635.0,
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635.0,
1142.0,
661.0,
309.0,
661.0
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"score": 0.97,
"text": "M1 :DL2000 DNA marker ; M2 PBR322/Hae Il marker ; T Tumor ; N :Corresponding normal tissue"
}
],
"page_info": {
"page_no": 0,
"height": 2080,
"width": 1472
}
}
]
tests/unittest/test_model/assets/test_02.model.json 0 → 100644
View file @ 7b197fe2
This source diff could not be displayed because it is too large. You can view the blob instead.
tests/unittest/test_model/test_magic_model.py 0 → 100644
View file @ 7b197fe2
import json
from magic_pdf.data.read_api import read_local_pdfs
from magic_pdf.model.magic_model import MagicModel
def test_magic_model_image_v2():
datasets = read_local_pdfs('tests/test_model/assets/test_01.pdf')
with open('tests/test_model/assets/test_01.model.json') as f:
model_json = json.load(f)
magic_model = MagicModel(model_json, datasets[0])
imgs = magic_model.get_imgs_v2(0)
print(imgs)
tables = magic_model.get_tables_v2(0)
print(tables)
def test_magic_model_table_v2():
datasets = read_local_pdfs('tests/test_model/assets/test_02.pdf')
with open('tests/test_model/assets/test_02.model.json') as f:
model_json = json.load(f)
magic_model = MagicModel(model_json, datasets[0])
tables = magic_model.get_tables_v2(5)
print(tables)
tables = magic_model.get_tables_v2(8)
print(tables)
tests/test_table/test_tablemaster.py → tests/unittest/test_table/test_tablemaster.py
View file @ 7b197fe2
......@@ -7,7 +7,7 @@ from magic_pdf.model.sub_modules.table.tablemaster.tablemaster_paddle import Tab
class TestppTableModel(unittest.TestCase):
def test_image2html(self):
img = Image.open("tests/test_table/assets/table.jpg")
img = Image.open("tests/unittest/test_table/assets/table.jpg")
# 修改table模型路径
config = {"device": "cuda",
"model_dir": "/home/quyuan/.cache/modelscope/hub/opendatalab/PDF-Extract-Kit/models/TabRec/TableMaster"}
......
tests/unittest/test_tools/assets/cli_dev/cli_test_01.jsonl 0 → 100644
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tests/unittest/test_tools/assets/cli_dev/cli_test_01.model.json 0 → 100644
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\ No newline at end of file
tests/unittest/test_tools/test_cli.py 0 → 100644
View file @ 7b197fe2
import tempfile
import os
import shutil
from click.testing import CliRunner
from magic_pdf.tools.cli import cli
def test_cli_pdf():
# setup
unitest_dir = "/tmp/magic_pdf/unittest/tools"
filename = "cli_test_01"
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir="/tmp/magic_pdf/unittest/tools")
# run
runner = CliRunner()
result = runner.invoke(
cli,
[
"-p",
"tests/test_tools/assets/cli/pdf/cli_test_01.pdf",
"-o",
temp_output_dir,
],
)
# check
assert result.exit_code == 0
base_output_dir = os.path.join(temp_output_dir, "cli_test_01/auto")
r = os.stat(os.path.join(base_output_dir, f"{filename}.md"))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, "middle.json"))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, "model.json"))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, "origin.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "layout.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "spans.pdf"))
assert r.st_size > 500000
assert os.path.exists(os.path.join(base_output_dir, "images")) is True
assert os.path.isdir(os.path.join(base_output_dir, "images")) is True
assert os.path.exists(os.path.join(base_output_dir, "content_list.json")) is False
# teardown
shutil.rmtree(temp_output_dir)
def test_cli_path():
# setup
unitest_dir = "/tmp/magic_pdf/unittest/tools"
os.makedirs(unitest_dir, exist_ok=True)
temp_output_dir = tempfile.mkdtemp(dir="/tmp/magic_pdf/unittest/tools")
# run
runner = CliRunner()
result = runner.invoke(
cli, ["-p", "tests/test_tools/assets/cli/path", "-o", temp_output_dir]
)
# check
assert result.exit_code == 0
filename = "cli_test_01"
base_output_dir = os.path.join(temp_output_dir, "cli_test_01/auto")
r = os.stat(os.path.join(base_output_dir, f"{filename}.md"))
assert r.st_size > 7000
r = os.stat(os.path.join(base_output_dir, "middle.json"))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, "model.json"))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, "origin.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "layout.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "spans.pdf"))
assert r.st_size > 500000
assert os.path.exists(os.path.join(base_output_dir, "images")) is True
assert os.path.isdir(os.path.join(base_output_dir, "images")) is True
assert os.path.exists(os.path.join(base_output_dir, "content_list.json")) is False
base_output_dir = os.path.join(temp_output_dir, "cli_test_02/auto")
filename = "cli_test_02"
r = os.stat(os.path.join(base_output_dir, f"{filename}.md"))
assert r.st_size > 5000
r = os.stat(os.path.join(base_output_dir, "middle.json"))
assert r.st_size > 200000
r = os.stat(os.path.join(base_output_dir, "model.json"))
assert r.st_size > 15000
r = os.stat(os.path.join(base_output_dir, "origin.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "layout.pdf"))
assert r.st_size > 500000
r = os.stat(os.path.join(base_output_dir, "spans.pdf"))
assert r.st_size > 500000
assert os.path.exists(os.path.join(base_output_dir, "images")) is True
assert os.path.isdir(os.path.join(base_output_dir, "images")) is True
assert os.path.exists(os.path.join(base_output_dir, "content_list.json")) is False
# teardown
shutil.rmtree(temp_output_dir)
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