Merge pull request #42 from dyning/develop

updata doc of infer

tools/infer/predict_det.py

@@ -15,19 +15,17 @@
 import utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
+from ppocr.utils.utility import get_image_file_list
 import cv2
 from ppocr.data.det.east_process import EASTProcessTest
 from ppocr.data.det.db_process import DBProcessTest
 from ppocr.postprocess.db_postprocess import DBPostProcess
 from ppocr.postprocess.east_postprocess import EASTPostPocess
-from ppocr.utils.utility import get_image_file_list
-from tools.infer.utility import draw_ocr
 import copy
 import numpy as np
 import math
 import time
 import sys
-import os
 
 
 class TextDetector(object):
@@ -79,27 +77,10 @@ class TextDetector(object):
         rect = np.array([tl, tr, br, bl], dtype="float32")
         return rect
 
-    def expand_det_res(self, points, bbox_height, bbox_width, img_height,
-                       img_width):
-        if bbox_height * 1.0 / bbox_width >= 2.0:
-            expand_w = bbox_width * 0.20
-            expand_h = bbox_width * 0.20
-        elif bbox_width * 1.0 / bbox_height >= 3.0:
-            expand_w = bbox_height * 0.20
-            expand_h = bbox_height * 0.20
-        else:
-            expand_w = bbox_height * 0.1
-            expand_h = bbox_height * 0.1
-
-        points[0, 0] = int(max((points[0, 0] - expand_w), 0))
-        points[1, 0] = int(min((points[1, 0] + expand_w), img_width))
-        points[3, 0] = int(max((points[3, 0] - expand_w), 0))
-        points[2, 0] = int(min((points[2, 0] + expand_w), img_width))
-
-        points[0, 1] = int(max((points[0, 1] - expand_h), 0))
-        points[1, 1] = int(max((points[1, 1] - expand_h), 0))
-        points[3, 1] = int(min((points[3, 1] + expand_h), img_height))
-        points[2, 1] = int(min((points[2, 1] + expand_h), img_height))
+    def clip_det_res(self, points, img_height, img_width):
+        for pno in range(4):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
         return points
 
     def filter_tag_det_res(self, dt_boxes, image_shape):
@@ -107,22 +88,11 @@ class TextDetector(object):
         dt_boxes_new = []
         for box in dt_boxes:
             box = self.order_points_clockwise(box)
-            left = int(np.min(box[:, 0]))
-            right = int(np.max(box[:, 0]))
-            top = int(np.min(box[:, 1]))
-            bottom = int(np.max(box[:, 1]))
-            bbox_height = bottom - top
-            bbox_width = right - left
-            diffh = math.fabs(box[0, 1] - box[1, 1])
-            diffw = math.fabs(box[0, 0] - box[3, 0])
+            box = self.clip_det_res(box, img_height, img_width)
             rect_width = int(np.linalg.norm(box[0] - box[1]))
             rect_height = int(np.linalg.norm(box[0] - box[3]))
             if rect_width <= 10 or rect_height <= 10:
                 continue
-            # if diffh <= 10 and diffw <= 10:
-            #     box = self.expand_det_res(
-            #         copy.deepcopy(box), bbox_height, bbox_width, img_height,
-            #         img_width)
             dt_boxes_new.append(box)
         dt_boxes = np.array(dt_boxes_new)
         return dt_boxes
@@ -153,8 +123,6 @@ class TextDetector(object):
         return dt_boxes, elapse
 
 
-from tools.infer.utility import draw_text_det_res
-
 if __name__ == "__main__":
     args = utility.parse_args()
     image_file_list = get_image_file_list(args.image_dir)
@@ -171,9 +139,8 @@ if __name__ == "__main__":
             total_time += elapse
         count += 1
         print("Predict time of %s:" % image_file, elapse)
-        img_draw = draw_text_det_res(dt_boxes, image_file, return_img=True)
-        save_path = os.path.join("./inference_det/",
-                                 os.path.basename(image_file))
-        print("The visualized image saved in {}".format(save_path))
-
+        src_im = utility.draw_text_det_res(dt_boxes, image_file)
+        img_name_pure = image_file.split("/")[-1]
+        cv2.imwrite("./inference_results/det_res_%s" % img_name_pure, src_im)
+    if count > 1: 
     	print("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -15,8 +15,8 @@
 import utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
+from ppocr.utils.utility import get_image_file_list
 import cv2
-
 import copy
 import numpy as np
 import math
@@ -30,6 +30,7 @@ class TextRecognizer(object):
             utility.create_predictor(args, mode="rec")
         image_shape = [int(v) for v in args.rec_image_shape.split(",")]
         self.rec_image_shape = image_shape
+        self.character_type = args.rec_char_type
         char_ops_params = {}
         char_ops_params["character_type"] = args.rec_char_type
         char_ops_params["character_dict_path"] = args.rec_char_dict_path
@@ -38,6 +39,7 @@ class TextRecognizer(object):
 
     def resize_norm_img(self, img, max_wh_ratio):
         imgC, imgH, imgW = self.rec_image_shape
+        if self.character_type == "ch":
             imgW = int(32 * max_wh_ratio)
         h = img.shape[0]
         w = img.shape[1]
@@ -102,7 +104,7 @@ class TextRecognizer(object):
 
 if __name__ == "__main__":
     args = utility.parse_args()
-    image_file_list = utility.get_image_file_list(args.image_dir)
+    image_file_list = get_image_file_list(args.image_dir)
     text_recognizer = TextRecognizer(args)
     valid_image_file_list = []
     img_list = []
@@ -114,6 +116,7 @@ if __name__ == "__main__":
         valid_image_file_list.append(image_file)
         img_list.append(img)
     rec_res, predict_time = text_recognizer(img_list)
+    rec_res, predict_time = text_recognizer(img_list)
     for ino in range(len(img_list)):
         print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
     print("Total predict time for %d images:%.3f" %

tools/program.py

@@ -191,8 +191,8 @@ def build_export(config, main_prog, startup_prog):
             func_infor = config['Architecture']['function']
             model = create_module(func_infor)(params=config)
             image, outputs = model(mode='export')
-            fetches_var = sorted([outputs[name] for name in outputs])
-            fetches_var_name = [name for name in fetches_var]
+            fetches_var_name = sorted([name for name in outputs])
+            fetches_var = [outputs[name] for name in fetches_var_name]
     feeded_var_names = [image.name]
     target_vars = fetches_var
     return feeded_var_names, target_vars, fetches_var_name