def general_ocr_port(img,det_ip_port,rec_ip_port,MyList):
det_client = Client()
det_client.load_client_config("./general_ocr_config/det_infer_client/serving_client_conf.prototxt")
det_client.connect(det_ip_port)
#start rec Client
rec_client = Client()
rec_client.load_client_config("./general_ocr_config/rec_infer_client/serving_client_conf.prototxt")
rec_client.connect(rec_ip_port)
#前处理
feed, fetch, tmp_args = det_preprocess(img)
#推理
fetch_map = det_client.predict(feed, fetch)
outputs = [fetch_map[x] for x in fetch]
#后处理
dt_boxes = det_postprocess(outputs, tmp_args)
# print(dt_boxes.shape)
#裁剪出框
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = get_rotate_crop_image(img, tmp_box)
img_crop_list.append(img_crop)
#以batch为30开始识别
batch_size = 8
batch_num = len(img_crop_list) // batch_size + 1
batch_num = len(img_crop_list)
text_list = []
score_list =[]
for i in range(batch_num):
if i == (batch_num-1):
img_batch = img_crop_list[i*batch_size:]
else :
img_batch = img_crop_list[i*batch_size:(i+1)*batch_size]
if(len(img_batch)==0):
continue
feed, fetch, tmp_args = rec_preprocess(img_batch)
#推理
fetch_map = rec_client.predict(feed, fetch)
# print(fetch_map)
outputs = [fetch_map[x] for x in fetch]
for x in fetch_map.keys():
if ".lod" in x:
# print(x),fetch_map[x]
tmp_args[x] = fetch_map[x]
#后处理
rec_res = rec_postprocess(outputs, tmp_args)
for x in rec_res:
text_list.append(x[0])
score_list.append(x[1])
MyList.append(text_list)
det_client.release()
rec_client.release()
return
def general_ocr_port(img, det_ip_port, rec_ip_port):
'''
General_ocr_rec function
Args:
img: Input image,opencv BGR888
det_ip_port: The det serving port to listen
rec_ip_port: The rec serving port to listen
return:
dt_boxes:detection boxes list
text_list: Boxes recognition result
score_list: The confidence of boxes recognition result
'''
#开始检测
#前处理
#初始化检测和识别
det_client = Client()
det_client.load_client_config(
"./general_ocr_config/det_infer_client/serving_client_conf.prototxt")
det_client.connect(det_ip_port)
feed, fetch, tmp_args = det_preprocess(img)
#推理
fetch_map = det_client.predict(feed, fetch)
outputs = [fetch_map[x] for x in fetch]
#后处理
dt_boxes = det_postprocess(outputs, tmp_args)
# print(dt_boxes.shape)
#裁剪出框
img_crop_list = []
dt_boxes = sorted_boxes(dt_boxes)
for bno in range(len(dt_boxes)):
tmp_box = copy.deepcopy(dt_boxes[bno])
img_crop = get_rotate_crop_image(img, tmp_box)
img_crop_list.append(img_crop)
#以batch为30开始识别
batch_size = 8
batch_num = len(img_crop_list) // batch_size + 1
text_list = []
score_list = []
if len(img_crop_list) == 0:
return text_list, score_list
#start rec Client
rec_client = Client()
rec_client.load_client_config(
"./general_ocr_config/rec_infer_client/serving_client_conf.prototxt")
rec_client.connect(rec_ip_port)
feed, fetch, tmp_args = det_preprocess(img)
for i in range(batch_num):
if i == (batch_num - 1):
img_batch = img_crop_list[i * batch_size:]
else:
img_batch = img_crop_list[i * batch_size:(i + 1) * batch_size]
if (len(img_batch) == 0):
continue
feed, fetch, tmp_args = rec_preprocess(img_batch)
#推理
fetch_map = rec_client.predict(feed, fetch)
# print(fetch_map)
outputs = [fetch_map[x] for x in fetch]
for x in fetch_map.keys():
if ".lod" in x:
# print(x),fetch_map[x]
tmp_args[x] = fetch_map[x]
#后处理
rec_res = rec_postprocess(outputs, tmp_args)
for x in rec_res:
text_list.append(x[0])
score_list.append(x[1])
rec_client.release()
det_client.release()
return dt_boxes, text_list, score_list