def main(config):
rec_predictor = RecPredictor(config)
image_list = get_image_list(config["Global"]["infer_imgs"])
batch_imgs = []
batch_names = []
cnt = 0
for idx, img_path in enumerate(image_list):
img = cv2.imread(img_path)
if img is None:
logger.warning(
"Image file failed to read and has been skipped. The path: {}".
format(img_path))
else:
img = img[:, :, ::-1]
batch_imgs.append(img)
img_name = os.path.basename(img_path)
batch_names.append(img_name)
cnt += 1
if cnt % config["Global"]["batch_size"] == 0 or (idx +
1) == len(image_list):
if len(batch_imgs) == 0:
continue
batch_results = rec_predictor.predict(batch_imgs)
for number, result_dict in enumerate(batch_results):
filename = batch_names[number]
print("{}:\t{}".format(filename, result_dict))
batch_imgs = []
batch_names = []
return
def main(config):
system_predictor = SystemPredictor(config)
image_list = get_image_list(config["Global"]["infer_imgs"])
assert config["Global"]["batch_size"] == 1
for idx, image_file in enumerate(image_list):
img = cv2.imread(image_file)[:, :, ::-1]
output = system_predictor.predict(img)
draw_bbox_results(img, output, image_file)
print(output)
return
def main(config):
det_predictor = DetPredictor(config)
image_list = get_image_list(config["Global"]["infer_imgs"])
assert config["Global"]["batch_size"] == 1
for idx, image_file in enumerate(image_list):
img = cv2.imread(image_file)[:, :, ::-1]
output = det_predictor.predict(img)
print(output)
return
def main(config):
cls_predictor = ClsPredictor(config)
image_list = get_image_list(config["Global"]["infer_imgs"])
batch_imgs = []
batch_names = []
cnt = 0
for idx, img_path in enumerate(image_list):
img = cv2.imread(img_path)
if img is None:
logger.warning(
"Image file failed to read and has been skipped. The path: {}".
format(img_path))
else:
img = img[:, :, ::-1]
batch_imgs.append(img)
img_name = os.path.basename(img_path)
batch_names.append(img_name)
cnt += 1
if cnt % config["Global"]["batch_size"] == 0 or (idx +
1) == len(image_list):
if len(batch_imgs) == 0:
continue
batch_results = cls_predictor.predict(batch_imgs)
for number, result_dict in enumerate(batch_results):
filename = batch_names[number]
clas_ids = result_dict["class_ids"]
scores_str = "[{}]".format(", ".join(
"{:.2f}".format(r) for r in result_dict["scores"]))
label_names = result_dict["label_names"]
print("{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
format(filename, clas_ids, scores_str, label_names))
batch_imgs = []
batch_names = []
if cls_predictor.benchmark:
cls_predictor.auto_logger.report()
return
def main(args):
image_path_list = get_image_list(args.image_file)
headers = {"Content-type": "application/json"}
preprocess_ops = create_operators(
PreprocessConfig(args.resize_short, args.crop_size, args.normalize,
args.to_chw)())
cnt = 0
predict_time = 0
all_score = 0.0
start_time = time.time()
img_data_list = []
img_name_list = []
cnt = 0
for idx, img_path in enumerate(image_path_list):
img = cv2.imread(img_path)
if img is None:
logger.warning(
f"Image file failed to read and has been skipped. The path: {img_path}"
)
continue
else:
img = img[:, :, ::-1]
for ops in preprocess_ops:
img = ops(img)
img = np.array(img)
img_data_list.append(img)
img_name = img_path.split('/')[-1]
img_name_list.append(img_name)
cnt += 1
if cnt % args.batch_size == 0 or (idx + 1) == len(image_path_list):
inputs = np.array(img_data_list)
b64str, revert_shape = np_to_b64(inputs)
data = {
"images": b64str,
"revert_params": {
"shape": revert_shape,
"dtype": str(inputs.dtype)
}
}
try:
r = requests.post(
url=args.server_url,
headers=headers,
data=json.dumps(data))
r.raise_for_status
if r.json()["status"] != "000":
msg = r.json()["msg"]
raise Exception(msg)
except Exception as e:
logger.error(f"{e}, in file(s): {img_name_list[0]} etc.")
continue
else:
results = r.json()["results"]
preds = results["prediction"]
elapse = results["elapse"]
cnt += len(preds)
predict_time += elapse
for number, result_list in enumerate(preds):
all_score += result_list["scores"][0]
pred_str = ", ".join(
[f"{k}: {result_list[k]}" for k in result_list])
logger.info(
f"File:{img_name_list[number]}, The result(s): {pred_str}"
)
finally:
img_data_list = []
img_name_list = []
total_time = time.time() - start_time
logger.info("The average time of prediction cost: {:.3f} s/image".format(
predict_time / cnt))
logger.info("The average time cost: {:.3f} s/image".format(total_time /
cnt))
logger.info("The average top-1 score: {:.3f}".format(all_score / cnt))
