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# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
import argparse
import numpy as np
import cv2 as cv
# Check OpenCV version
opencv_python_version = lambda str_version: tuple(map(int, (str_version.split("."))))
assert opencv_python_version(cv.__version__) >= opencv_python_version("4.10.0"), \
"Please install latest opencv-python for benchmark: python3 -m pip install --upgrade opencv-python"
from wechatqrcode import WeChatQRCode
# Valid combinations of backends and targets
backend_target_pairs = [
[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA],
[cv.dnn.DNN_BACKEND_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16],
[cv.dnn.DNN_BACKEND_TIMVX, cv.dnn.DNN_TARGET_NPU],
[cv.dnn.DNN_BACKEND_CANN, cv.dnn.DNN_TARGET_NPU]
]
parser = argparse.ArgumentParser(
description="WeChat QR code detector for detecting and parsing QR code (https://github.com/opencv/opencv_contrib/tree/master/modules/wechat_qrcode)")
parser.add_argument('--input', '-i', type=str,
help='Usage: Set path to the input image. Omit for using default camera.')
parser.add_argument('--detect_prototxt_path', type=str, default='detect_2021nov.prototxt',
help='Usage: Set path to detect.prototxt.')
parser.add_argument('--detect_model_path', type=str, default='detect_2021nov.caffemodel',
help='Usage: Set path to detect.caffemodel.')
parser.add_argument('--sr_prototxt_path', type=str, default='sr_2021nov.prototxt',
help='Usage: Set path to sr.prototxt.')
parser.add_argument('--sr_model_path', type=str, default='sr_2021nov.caffemodel',
help='Usage: Set path to sr.caffemodel.')
parser.add_argument('--backend_target', '-bt', type=int, default=0,
help='''Choose one of the backend-target pair to run this demo:
{:d}: (default) OpenCV implementation + CPU,
{:d}: CUDA + GPU (CUDA),
{:d}: CUDA + GPU (CUDA FP16),
{:d}: TIM-VX + NPU,
{:d}: CANN + NPU
'''.format(*[x for x in range(len(backend_target_pairs))]))
parser.add_argument('--save', '-s', action='store_true',
help='Usage: Specify to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input.')
parser.add_argument('--vis', '-v', action='store_true',
help='Usage: Specify to open a new window to show results. Invalid in case of camera input.')
args = parser.parse_args()
def visualize(image, res, points, points_color=(0, 255, 0), text_color=(0, 255, 0), fps=None):
output = image.copy()
h, w, _ = output.shape
if fps is not None:
cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, text_color)
fontScale = 0.5
fontSize = 1
for r, p in zip(res, points):
p = p.astype(np.int32)
for _p in p:
cv.circle(output, _p, 10, points_color, -1)
qrcode_center_x = int((p[0][0] + p[2][0]) / 2)
qrcode_center_y = int((p[0][1] + p[2][1]) / 2)
text_size, baseline = cv.getTextSize(r, cv.FONT_HERSHEY_DUPLEX, fontScale, fontSize)
text_x = qrcode_center_x - int(text_size[0] / 2)
text_y = qrcode_center_y - int(text_size[1] / 2)
cv.putText(output, '{}'.format(r), (text_x, text_y), cv.FONT_HERSHEY_DUPLEX, fontScale, text_color, fontSize)
return output
if __name__ == '__main__':
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
# Instantiate WeChatQRCode
model = WeChatQRCode(args.detect_prototxt_path,
args.detect_model_path,
args.sr_prototxt_path,
args.sr_model_path,
backendId=backend_id,
targetId=target_id)
# If input is an image:
if args.input is not None:
image = cv.imread(args.input)
res, points = model.infer(image)
# Print results:
print(res)
print(points)
# Draw results on the input image
image = visualize(image, res, points)
# Save results if save is true
if args.save:
print('Results saved to result.jpg\n')
cv.imwrite('result.jpg', image)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, image)
cv.waitKey(0)
else: # Omit input to call default camera
deviceId = 0
cap = cv.VideoCapture(deviceId)
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
# Inference
tm.start()
res, points = model.infer(frame)
tm.stop()
fps = tm.getFPS()
# Draw results on the input image
frame = visualize(frame, res, points, fps=fps)
# Visualize results in a new window
cv.imshow('WeChatQRCode Demo', frame)
tm.reset()
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