Merge pull request #1299 from WenmuZhou/fix_predict_system

add predict_cls to predict_system

tools/infer/predict_cls.py

@@ -23,7 +23,7 @@ import copy
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -106,10 +106,10 @@ class TextClassifier(object):
                 norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
                 self.predictor.run([norm_img_batch])
             prob_out = self.output_tensors[0].copy_to_cpu()
-            cls_res = self.postprocess_op(prob_out)
+            cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
-            for rno in range(len(cls_res)):
-                label, score = cls_res[rno]
+            for rno in range(len(cls_result)):
+                label, score = cls_result[rno]
                 cls_res[indices[beg_img_no + rno]] = [label, score]
                 if '180' in label and score > self.cls_thresh:
                     img_list[indices[beg_img_no + rno]] = cv2.rotate(
@@ -133,8 +133,8 @@ def main(args):
         img_list.append(img)
     try:
         img_list, cls_res, predict_time = text_classifier(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -143,10 +143,10 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))
 
-    if __name__ == "__main__":
+if __name__ == "__main__":
     main(utility.parse_args())

tools/infer/predict_det.py

@@ -178,11 +178,12 @@ if __name__ == "__main__":
         if count > 0:
             total_time += elapse
         count += 1
-        print("Predict time of {}: {}".format(image_file, elapse))
+        logger.info("Predict time of {}: {}".format(image_file, elapse))
         src_im = utility.draw_text_det_res(dt_boxes, image_file)
         img_name_pure = os.path.split(image_file)[-1]
         img_path = os.path.join(draw_img_save,
                                 "det_res_{}".format(img_name_pure))
         cv2.imwrite(img_path, src_im)
+        logger.info("The visualized image saved in {}".format(img_path))
     if count > 1:
-        print("Avg Time:", total_time / (count - 1))
+        logger.info("Avg Time:", total_time / (count - 1))

tools/infer/predict_rec.py

@@ -22,7 +22,7 @@ import cv2
 import numpy as np
 import math
 import time
-
+import traceback
 import paddle.fluid as fluid
 
 import tools.infer.utility as utility
@@ -135,8 +135,8 @@ def main(args):
         img_list.append(img)
     try:
         rec_res, predict_time = text_recognizer(img_list)
-    except Exception as e:
-        print(e)
+    except:
+        logger.info(traceback.format_exc())
         logger.info(
             "ERROR!!!! \n"
             "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
@@ -145,9 +145,9 @@ def main(args):
             "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
         exit()
     for ino in range(len(img_list)):
-        print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
             ino]))
-    print("Total predict time for {} images, cost: {:.3f}".format(
+    logger.info("Total predict time for {} images, cost: {:.3f}".format(
         len(img_list), predict_time))
 
 

tools/infer/predict_system.py

@@ -23,17 +23,21 @@ import numpy as np
 import time
 from PIL import Image
 import tools.infer.utility as utility
-from tools.infer.utility import draw_ocr
 import tools.infer.predict_rec as predict_rec
 import tools.infer.predict_det as predict_det
+import tools.infer.predict_cls as predict_cls
 from ppocr.utils.utility import get_image_file_list, check_and_read_gif
 from ppocr.utils.logging import get_logger
+from tools.infer.utility import draw_ocr_box_txt
 
 
 class TextSystem(object):
     def __init__(self, args):
         self.text_detector = predict_det.TextDetector(args)
         self.text_recognizer = predict_rec.TextRecognizer(args)
+        self.use_angle_cls = args.use_angle_cls
+        if self.use_angle_cls:
+            self.text_classifier = predict_cls.TextClassifier(args)
 
     def get_rotate_crop_image(self, img, points):
         '''
@@ -72,12 +76,12 @@ class TextSystem(object):
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
             cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
-            print(bno, rec_res[bno])
+            logger.info(bno, rec_res[bno])
 
     def __call__(self, img):
         ori_im = img.copy()
         dt_boxes, elapse = self.text_detector(img)
-        print("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
+        logger.info("dt_boxes num : {}, elapse : {}".format(len(dt_boxes), elapse))
         if dt_boxes is None:
             return None, None
         img_crop_list = []
@@ -88,8 +92,14 @@ class TextSystem(object):
             tmp_box = copy.deepcopy(dt_boxes[bno])
             img_crop = self.get_rotate_crop_image(ori_im, tmp_box)
             img_crop_list.append(img_crop)
+        if self.use_angle_cls:
+            img_crop_list, angle_list, elapse = self.text_classifier(
+                img_crop_list)
+            logger.info("cls num  : {}, elapse : {}".format(
+                len(img_crop_list), elapse))
+
         rec_res, elapse = self.text_recognizer(img_crop_list)
-        print("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
+        logger.info("rec_res num  : {}, elapse : {}".format(len(rec_res), elapse))
         # self.print_draw_crop_rec_res(img_crop_list, rec_res)
         return dt_boxes, rec_res
 
@@ -119,7 +129,8 @@ def main(args):
     image_file_list = get_image_file_list(args.image_dir)
     text_sys = TextSystem(args)
     is_visualize = True
-    tackle_img_num = 0
+    font_path = args.vis_font_path
+    drop_score = args.drop_score
     for image_file in image_file_list:
         img, flag = check_and_read_gif(image_file)
         if not flag:
@@ -128,20 +139,16 @@ def main(args):
             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:
-            text_sys = TextSystem(args)
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
-        print("Predict time of %s: %.3fs" % (image_file, elapse))
+        logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
 
-        drop_score = 0.5
         dt_num = len(dt_boxes)
         for dno in range(dt_num):
             text, score = rec_res[dno]
             if score >= drop_score:
                 text_str = "%s, %.3f" % (text, score)
-                print(text_str)
+                logger.info(text_str)
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -149,15 +156,20 @@ def main(args):
             txts = [rec_res[i][0] for i in range(len(rec_res))]
             scores = [rec_res[i][1] for i in range(len(rec_res))]
 
-            draw_img = draw_ocr(
-                image, boxes, txts, scores, drop_score=drop_score)
+            draw_img = draw_ocr_box_txt(
+                image,
+                boxes,
+                txts,
+                scores,
+                drop_score=drop_score,
+                font_path=font_path)
             draw_img_save = "./inference_results/"
             if not os.path.exists(draw_img_save):
                 os.makedirs(draw_img_save)
             cv2.imwrite(
                 os.path.join(draw_img_save, os.path.basename(image_file)),
                 draw_img[:, :, ::-1])
-            print("The visualized image saved in {}".format(
+            logger.info("The visualized image saved in {}".format(
                 os.path.join(draw_img_save, os.path.basename(image_file))))
 
 

tools/infer/utility.py

@@ -71,6 +71,7 @@ def parse_args():
     parser.add_argument("--use_space_char", type=str2bool, default=True)
     parser.add_argument(
         "--vis_font_path", type=str, default="./doc/simfang.ttf")
+    parser.add_argument("--drop_score", type=float, default=0.5)
 
     # params for text classifier
     parser.add_argument("--use_angle_cls", type=str2bool, default=False)
@@ -202,7 +203,12 @@ def draw_ocr(image,
     return image
 
 
-def draw_ocr_box_txt(image, boxes, txts):
+def draw_ocr_box_txt(image,
+                     boxes,
+                     txts,
+                     scores=None,
+                     drop_score=0.5,
+                     font_path="./doc/simfang.ttf"):
     h, w = image.height, image.width
     img_left = image.copy()
     img_right = Image.new('RGB', (w, h), (255, 255, 255))
@@ -212,7 +218,9 @@ def draw_ocr_box_txt(image, boxes, txts):
     random.seed(0)
     draw_left = ImageDraw.Draw(img_left)
     draw_right = ImageDraw.Draw(img_right)
-    for (box, txt) in zip(boxes, txts):
+    for idx, (box, txt) in enumerate(zip(boxes, txts)):
+        if scores is not None and scores[idx] < drop_score:
+            continue
         color = (random.randint(0, 255), random.randint(0, 255),
                  random.randint(0, 255))
         draw_left.polygon(box, fill=color)
@@ -222,14 +230,13 @@ def draw_ocr_box_txt(image, boxes, txts):
                 box[2][1], box[3][0], box[3][1]
             ],
             outline=color)
-        box_height = math.sqrt((box[0][0] - box[3][0])**2 + (box[0][1] - box[3][
-            1])**2)
-        box_width = math.sqrt((box[0][0] - box[1][0])**2 + (box[0][1] - box[1][
-            1])**2)
+        box_height = math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][
+            1]) ** 2)
+        box_width = math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][
+            1]) ** 2)
         if box_height > 2 * box_width:
             font_size = max(int(box_width * 0.9), 10)
-            font = ImageFont.truetype(
-                "./doc/simfang.ttf", font_size, encoding="utf-8")
+            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
             cur_y = box[0][1]
             for c in txt:
                 char_size = font.getsize(c)
@@ -238,8 +245,7 @@ def draw_ocr_box_txt(image, boxes, txts):
                 cur_y += char_size[1]
         else:
             font_size = max(int(box_height * 0.8), 10)
-            font = ImageFont.truetype(
-                "./doc/simfang.ttf", font_size, encoding="utf-8")
+            font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
             draw_right.text(
                 [box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)
     img_left = Image.blend(image, img_left, 0.5)