Merge branch 'develop' into develop

ppocr/utils/utility.py

@@ -14,6 +14,9 @@
 
 import logging
 import os
+import imghdr
+import cv2
+from paddle import fluid
 
 
 def initial_logger():
@@ -61,19 +64,31 @@ def get_image_file_list(img_file):
     if img_file is None or not os.path.exists(img_file):
         raise Exception("not found any img file in {}".format(img_file))
 
-    img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
-    if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
+    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
+    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
         imgs_lists.append(img_file)
     elif os.path.isdir(img_file):
         for single_file in os.listdir(img_file):
-            if single_file.split('.')[-1] in img_end:
-                imgs_lists.append(os.path.join(img_file, single_file))
+            file_path = os.path.join(img_file, single_file)
+            if imghdr.what(file_path) in img_end:
+                imgs_lists.append(file_path)
     if len(imgs_lists) == 0:
         raise Exception("not found any img file in {}".format(img_file))
     return imgs_lists
 
 
-from paddle import fluid
+def check_and_read_gif(img_path):
+    if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
+        gif = cv2.VideoCapture(img_path)
+        ret, frame = gif.read()
+        if not ret:
+            logging.info("Cannot read {}. This gif image maybe corrupted.")
+            return None, False
+        if len(frame.shape) == 2 or frame.shape[-1] == 1:
+            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+        imgvalue = frame[:, :, ::-1]
+        return imgvalue, True
+    return None, False
 
 
 def create_multi_devices_program(program, loss_var_name):

tools/export_model.py

@@ -41,27 +41,11 @@ from paddle import fluid
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.utils.save_load import init_model
-from ppocr.utils.character import CharacterOps
-from ppocr.utils.utility import create_module
 
 
-def main():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
-    logger.info(config)
-
-    # check if set use_gpu=True in paddlepaddle cpu version
-    use_gpu = config['Global']['use_gpu']
-    program.check_gpu(use_gpu)
 
-    alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
-    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
-        config['Global']['char_ops'] = CharacterOps(config['Global'])
-
-    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
-    startup_prog = fluid.Program()
-    eval_program = fluid.Program()
+def main():
+    startup_prog, eval_program, place, config, _ = program.preprocess()
 
     feeded_var_names, target_vars, fetches_var_name = program.build_export(
         config, eval_program, startup_prog)
@@ -88,6 +72,4 @@ def main():
 
 
 if __name__ == '__main__':
-    parser = program.ArgsParser()
-    FLAGS = parser.parse_args()
     main()

tools/infer/predict_det.py

@@ -20,7 +20,7 @@ sys.path.append(os.path.join(__dir__, '../..'))
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 import cv2
 from ppocr.data.det.east_process import EASTProcessTest
 from ppocr.data.det.db_process import DBProcessTest
@@ -135,8 +135,13 @@ if __name__ == "__main__":
     text_detector = TextDetector(args)
     count = 0
     total_time = 0
+    draw_img_save = "./inference_results"
+    if not os.path.exists(draw_img_save):
+        os.makedirs(draw_img_save)
     for image_file in image_file_list:
-        img = cv2.imread(image_file)
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
             continue
@@ -147,6 +152,7 @@ if __name__ == "__main__":
         print("Predict time of %s:" % image_file, elapse)
         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)
+        cv2.imwrite(
+            os.path.join(draw_img_save, "det_res_%s" % img_name_pure), src_im)
     if count > 1:
         print("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -20,7 +20,7 @@ sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
 import tools.infer.utility as utility
 from ppocr.utils.utility import initial_logger
 logger = initial_logger()
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 import cv2
 import copy
 import numpy as np
@@ -122,9 +122,9 @@ class TextRecognizer(object):
                     ind = np.argmax(probs, axis=1)
                     blank = probs.shape[1]
                     valid_ind = np.where(ind != (blank - 1))[0]
-                    score = np.mean(probs[valid_ind, ind[valid_ind]])
                     if len(valid_ind) == 0:
                         continue
+                    score = np.mean(probs[valid_ind, ind[valid_ind]])
                     # rec_res.append([preds_text, score])
                     rec_res[indices[beg_img_no + rno]] = [preds_text, score]
             else:
@@ -153,7 +153,9 @@ def main(args):
     valid_image_file_list = []
     img_list = []
     for image_file in image_file_list:
-        img = cv2.imread(image_file, cv2.IMREAD_COLOR)
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
             continue

tools/infer/predict_system.py

@@ -27,7 +27,7 @@ import copy
 import numpy as np
 import math
 import time
-from ppocr.utils.utility import get_image_file_list
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from PIL import Image
 from tools.infer.utility import draw_ocr
 from tools.infer.utility import draw_ocr_box_txt
@@ -49,18 +49,23 @@ class TextSystem(object):
         points[:, 0] = points[:, 0] - left
         points[:, 1] = points[:, 1] - top
         '''
-        img_crop_width = int(max(np.linalg.norm(points[0] - points[1]),
-                                 np.linalg.norm(points[2] - points[3])))
-        img_crop_height = int(max(np.linalg.norm(points[0] - points[3]),
-                                  np.linalg.norm(points[1] - points[2])))
-        pts_std = np.float32([[0, 0],
-                              [img_crop_width, 0],
+        img_crop_width = int(
+            max(
+                np.linalg.norm(points[0] - points[1]),
+                np.linalg.norm(points[2] - points[3])))
+        img_crop_height = int(
+            max(
+                np.linalg.norm(points[0] - points[3]),
+                np.linalg.norm(points[1] - points[2])))
+        pts_std = np.float32([[0, 0], [img_crop_width, 0],
                               [img_crop_width, img_crop_height],
                               [0, img_crop_height]])
         M = cv2.getPerspectiveTransform(points, pts_std)
-        dst_img = cv2.warpPerspective(img, M, (img_crop_width, img_crop_height),
-                                      borderMode=cv2.BORDER_REPLICATE,
-                                      flags=cv2.INTER_CUBIC)
+        dst_img = cv2.warpPerspective(
+            img,
+            M, (img_crop_width, img_crop_height),
+            borderMode=cv2.BORDER_REPLICATE,
+            flags=cv2.INTER_CUBIC)
         dst_img_height, dst_img_width = dst_img.shape[0:2]
         if dst_img_height * 1.0 / dst_img_width >= 1.5:
             dst_img = np.rot90(dst_img)
@@ -119,25 +124,27 @@ def main(args):
     is_visualize = True
     tackle_img_num = 0
     for image_file in image_file_list:
-        img = cv2.imread(image_file)
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = cv2.imread(image_file)
         if img is None:
             logger.info("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
-        tackle_img_num += 1	
-        if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:	
+        tackle_img_num += 1
+        if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
             text_sys = TextSystem(args)
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
         print("Predict time of %s: %.3fs" % (image_file, elapse))
+
+        drop_score = 0.5
         dt_num = len(dt_boxes)
-        dt_boxes_final = []
         for dno in range(dt_num):
             text, score = rec_res[dno]
-            if score >= 0.5:
+            if score >= drop_score:
                 text_str = "%s, %.3f" % (text, score)
                 print(text_str)
-                dt_boxes_final.append(dt_boxes[dno])
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -146,7 +153,12 @@ def main(args):
             scores = [rec_res[i][1] for i in range(len(rec_res))]
 
             draw_img = draw_ocr(
-                image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
+                image,
+                boxes,
+                txts,
+                scores,
+                draw_txt=True,
+                drop_score=drop_score)
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)

tools/infer/utility.py

@@ -95,7 +95,7 @@ def create_predictor(args, mode):
         config.set_cpu_math_library_num_threads(6)
         if args.enable_mkldnn:
             config.enable_mkldnn()
-            
+
     #config.enable_memory_optim()
     config.disable_glog_info()
 
@@ -169,7 +169,7 @@ def draw_ocr_box_txt(image, boxes, txts):
     img_right = Image.new('RGB', (w, h), (255, 255, 255))
 
     import random
-    # 每次使用相同的随机种子 ，可以保证两次颜色一致
+
     random.seed(0)
     draw_left = ImageDraw.Draw(img_left)
     draw_right = ImageDraw.Draw(img_right)

tools/train.py

@@ -42,27 +42,10 @@ from ppocr.utils.utility import initial_logger
 logger = initial_logger()
 from ppocr.data.reader_main import reader_main
 from ppocr.utils.save_load import init_model
-from ppocr.utils.character import CharacterOps
 from paddle.fluid.contrib.model_stat import summary
 
 
 def main():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
-    logger.info(config)
-
-    # check if set use_gpu=True in paddlepaddle cpu version
-    use_gpu = config['Global']['use_gpu']
-    program.check_gpu(use_gpu)
-
-    alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
-    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
-        config['Global']['char_ops'] = CharacterOps(config['Global'])
-
-    place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
-    startup_program = fluid.Program()
-    train_program = fluid.Program()
     train_build_outputs = program.build(
         config, train_program, startup_program, mode='train')
     train_loader = train_build_outputs[0]
@@ -91,7 +74,7 @@ def main():
 
     # dump mode structure
     if config['Global']['debug']:
-        if 'attention' in config['Global']['loss_type']:
+        if train_alg_type == 'rec' and 'attention' in config['Global']['loss_type']:
             logger.warning('Does not suport dump attention...')
         else:
             summary(train_program)
@@ -109,15 +92,13 @@ def main():
         'fetch_name_list':eval_fetch_name_list,\
         'fetch_varname_list':eval_fetch_varname_list}
 
-    if alg in ['EAST', 'DB']:
+    if train_alg_type == 'det':
         program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
     else:
         program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
 
 
 def test_reader():
-    config = program.load_config(FLAGS.config)
-    program.merge_config(FLAGS.opt)
     logger.info(config)
     train_reader = reader_main(config=config, mode="train")
     import time
@@ -136,7 +117,6 @@ def test_reader():
 
 
 if __name__ == '__main__':
-    parser = program.ArgsParser()
-    FLAGS = parser.parse_args()
+    startup_program, train_program, place, config, train_alg_type = program.preprocess()
     main()
 #     test_reader()