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import argparse
import numpy as np
import cv2 as cv
from lpd_yunet import LPD_YuNet
def str2bool(v):
if v.lower() in ['on', 'yes', 'true', 'y', 't']:
return True
elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
return False
else:
raise NotImplementedError
backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
try:
backends += [cv.dnn.DNN_BACKEND_TIMVX]
targets += [cv.dnn.DNN_TARGET_NPU]
help_msg_backends += "; {:d}: TIMVX"
help_msg_targets += "; {:d}: NPU"
except:
print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.')
parser = argparse.ArgumentParser(description='LPD-YuNet for License Plate Detection')
parser.add_argument('--input', '-i', type=str, help='Usage: Set path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='license_plate_detection_lpd_yunet_2022may.onnx', help='Usage: Set model path, defaults to license_plate_detection_lpd_yunet_2022may.onnx.')
parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
parser.add_argument('--conf_threshold', type=float, default=0.9, help='Usage: Set the minimum needed confidence for the model to identify a license plate, defaults to 0.9. Smaller values may result in faster detection, but will limit accuracy. Filter out faces of confidence < conf_threshold.')
parser.add_argument('--nms_threshold', type=float, default=0.3, help='Usage: Suppress bounding boxes of iou >= nms_threshold. Default = 0.3. Suppress bounding boxes of iou >= nms_threshold.')
parser.add_argument('--top_k', type=int, default=5000, help='Usage: Keep top_k bounding boxes before NMS.')
parser.add_argument('--keep_top_k', type=int, default=750, help='Usage: Keep keep_top_k bounding boxes after NMS.')
parser.add_argument('--save', '-s', type=str2bool, default=False, help='Usage: Set “True” to save file with results (i.e. bounding box, confidence level). Invalid in case of camera input. Default will be set to “False”.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Usage: Default will be set to “True” and will open a new window to show results. Set to “False” to stop visualizations from being shown. Invalid in case of camera input.')
args = parser.parse_args()
def visualize(image, dets, line_color=(0, 255, 0), text_color=(0, 0, 255), fps=None):
output = image.copy()
if fps is not None:
cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, text_color)
for det in dets:
bbox = det[:-1].astype(np.int32)
x1, y1, x2, y2, x3, y3, x4, y4 = bbox
# Draw the border of license plate
cv.line(output, (x1, y1), (x2, y2), line_color, 2)
cv.line(output, (x2, y2), (x3, y3), line_color, 2)
cv.line(output, (x3, y3), (x4, y4), line_color, 2)
cv.line(output, (x4, y4), (x1, y1), line_color, 2)
return output
if __name__ == '__main__':
# Instantiate LPD-YuNet
model = LPD_YuNet(modelPath=args.model,
confThreshold=args.conf_threshold,
nmsThreshold=args.nms_threshold,
topK=args.top_k,
keepTopK=args.keep_top_k,
backendId=args.backend,
targetId=args.target)
# If input is an image
if args.input is not None:
image = cv.imread(args.input)
h, w, _ = image.shape
# Inference
model.setInputSize([w, h])
results = model.infer(image)
# Print results
print('{} license plates detected.'.format(results.shape[0]))
# Draw results on the input image
image = visualize(image, results)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg')
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)
w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
model.setInputSize([w, h])
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
# Inference
tm.start()
results = model.infer(frame) # results is a tuple
tm.stop()
# Draw results on the input image
frame = visualize(frame, results, fps=tm.getFPS())
# Visualize results in a new Window
cv.imshow('LPD-YuNet Demo', frame)
tm.reset()
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