def main(args):
# 加载图片
image, image_meta, _, _ = image_utils.load_image_gt(
np.random.randint(10), args.image_path, config.IMAGE_SHAPE[0], None)
# 加载模型
config.IMAGES_PER_GPU = 1
m = models.ctpn_net(config, 'test')
if args.weight_path is not None:
m.load_weights(args.weight_path)
else:
m.load_weights(config.WEIGHT_PATH)
# m.summary()
# 模型预测
text_boxes, text_scores, _ = m.predict(np.array([image]))
text_boxes = np_utils.remove_pad(text_boxes[0])
text_scores = np_utils.remove_pad(text_scores[0])[:, 0]
# 文本行检测器
image_meta = image_utils.parse_image_meta(image_meta)
detector = TextDetector(config)
text_lines = detector.detect(text_boxes, text_scores, config.IMAGE_SHAPE,
image_meta['window'])
# print("text_lines:{}".format(text_lines))
boxes_num = 15
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(1, 1, 1)
visualize.display_polygons(image,
text_lines[:boxes_num, :8],
text_lines[:boxes_num, 8],
ax=ax)
fig.savefig('examples.{}.png'.format(np.random.randint(10)))
def main(args):
# 覆盖参数 rewrite the parameters
config.USE_SIDE_REFINE = bool(args.use_side_refine)
if args.weight_path is not None:
config.WEIGHT_PATH = args.weight_path
config.IMAGES_PER_GPU = 1
config.IMAGE_SHAPE = (1024, 1024, 3)
# 加载图片 load images
image, image_meta, _, _ = image_utils.load_image_gt(np.random.randint(10),
args.image_path,
config.IMAGE_SHAPE[0],
None)
# 加载模型 load the model
m = models.ctpn_net(config, 'test')
m.load_weights(config.WEIGHT_PATH, by_name=True)
# m.summary()
# 模型预测 prediction
text_boxes, text_scores, _ = m.predict([np.array([image]), np.array([image_meta])])
text_boxes = np_utils.remove_pad(text_boxes[0])
text_scores = np_utils.remove_pad(text_scores[0])[:, 0]
# 文本行检测器 text detector
image_meta = image_utils.parse_image_meta(image_meta)
detector = TextDetector(config)
text_lines = detector.detect(text_boxes, text_scores, config.IMAGE_SHAPE, image_meta['window'])
# 可视化保存图像 saving the images
boxes_num = 30
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(1, 1, 1)
visualize.display_polygons(image, text_lines[:boxes_num, :8], text_lines[:boxes_num, 8],
ax=ax)
image_name = os.path.basename(args.image_path)
fig.savefig('{}.{}.jpg'.format(os.path.splitext(image_name)[0], int(config.USE_SIDE_REFINE)))
def generator(image_path_list, image_shape):
for i, image_path in enumerate(image_path_list):
image, image_meta, _, _ = image_utils.load_image_gt(
np.random.randint(10), image_path, image_shape[0])
print("开始评估第 {} 张图像".format(i))
yield {
"input_image": np.asarray([image]),
"input_image_meta": np.asarray([image_meta])
}
def generator(image_path_list, image_shape):
for i, image_path in enumerate(image_path_list):
image, image_meta, _, _ = image_utils.load_image_gt(
np.random.randint(10), image_path, image_shape[0])
if i % 200 == 0:
print("Evaluating No. {} ".format(i))
yield {
"input_image": np.asarray([image]),
"input_image_meta": np.asarray([image_meta])
}
def get_text_lines(self,
img: np.ndarray,
interactive=False) -> List[ctpn_coordinate_pair]:
# 加载图片
image, image_meta, _, _ = image_utils.load_image_gt(
np.random.randint(10), img, config.IMAGE_SHAPE[0], None)
# 加载模型
# 模型预测
text_boxes, text_scores, _ = self.m.predict(
[np.array([image]), np.array([image_meta])])
text_boxes = np_utils.remove_pad(text_boxes[0])
text_scores = np_utils.remove_pad(text_scores[0])[:, 0]
# 文本行检测器
image_meta = image_utils.parse_image_meta(image_meta)
detector = TextDetector(config)
text_lines = detector.detect(text_boxes, text_scores,
config.IMAGE_SHAPE, image_meta['window'])
# 可视化保存图像
boxes_num = 30
fig = plt.figure(figsize=(16, 16))
ax = fig.add_subplot(1, 1, 1)
visualize.display_polygons(image,
text_lines[:boxes_num, :8],
text_lines[:boxes_num, 8],
ax=ax)
lines = list(map(lambda x: ctpn_coordinate_pair(x, x[4]), text_lines))
for r in text_boxes:
# (y1,x1,y2,x2)
cv2.rectangle(image, (r[1], r[0]), (r[3], r[2]), (0, 255, 0), 2)
for r in lines:
# (y1,x1,y2,x2)
cv2.line(image, (r.x1, r.y1), (r.x2, r.y2), (255, 0, 0), 2)
#image = rotate_image(image, get_rotation_angle(lines))
if interactive:
cv2.imshow('img', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
return lines