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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 ppocr_det import PPOCRDet
# 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='PP-OCR Text Detection (https://arxiv.org/abs/2206.03001).')
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='./text_detection_en_ppocrv3_2023may.onnx',
help='Usage: Set model path, defaults to text_detection_en_ppocrv3_2023may.onnx.')
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('--width', type=int, default=736,
help='Usage: Resize input image to certain width, default = 736. It should be multiple by 32.')
parser.add_argument('--height', type=int, default=736,
help='Usage: Resize input image to certain height, default = 736. It should be multiple by 32.')
parser.add_argument('--binary_threshold', type=float, default=0.3,
help='Usage: Threshold of the binary map, default = 0.3.')
parser.add_argument('--polygon_threshold', type=float, default=0.5,
help='Usage: Threshold of polygons, default = 0.5.')
parser.add_argument('--max_candidates', type=int, default=200,
help='Usage: Set maximum number of polygon candidates, default = 200.')
parser.add_argument('--unclip_ratio', type=np.float64, default=2.0,
help=' Usage: The unclip ratio of the detected text region, which determines the output size, default = 2.0.')
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, results, box_color=(0, 255, 0), text_color=(0, 0, 255), isClosed=True, thickness=2, 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)
pts = np.array(results[0])
output = cv.polylines(output, pts, isClosed, box_color, thickness)
return output
if __name__ == '__main__':
backend_id = backend_target_pairs[args.backend_target][0]
target_id = backend_target_pairs[args.backend_target][1]
# Instantiate model
model = PPOCRDet(modelPath=args.model,
inputSize=[args.width, args.height],
binaryThreshold=args.binary_threshold,
polygonThreshold=args.polygon_threshold,
maxCandidates=args.max_candidates,
unclipRatio=args.unclip_ratio,
backendId=backend_id,
targetId=target_id)
# If input is an image
if args.input is not None:
original_image = cv.imread(args.input)
original_w = original_image.shape[1]
original_h = original_image.shape[0]
scaleHeight = original_h / args.height
scaleWidth = original_w / args.width
image = cv.resize(original_image, [args.width, args.height])
# Inference
results = model.infer(image)
# Scale the results bounding box
for i in range(len(results[0])):
for j in range(4):
box = results[0][i][j]
results[0][i][j][0] = box[0] * scaleWidth
results[0][i][j][1] = box[1] * scaleHeight
# Print results
print('{} texts detected.'.format(len(results[0])))
for idx, (bbox, score) in enumerate(zip(results[0], results[1])):
print('{}: {} {} {} {}, {:.2f}'.format(idx, bbox[0], bbox[1], bbox[2], bbox[3], score))
# Draw results on the input image
original_image = visualize(original_image, results)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg\n')
cv.imwrite('result.jpg', original_image)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, original_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, original_image = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
original_w = original_image.shape[1]
original_h = original_image.shape[0]
scaleHeight = original_h / args.height
scaleWidth = original_w / args.width
frame = cv.resize(original_image, [args.width, args.height])
# Inference
tm.start()
results = model.infer(frame) # results is a tuple
tm.stop()
# Scale the results bounding box
for i in range(len(results[0])):
for j in range(4):
box = results[0][i][j]
results[0][i][j][0] = box[0] * scaleWidth
results[0][i][j][1] = box[1] * scaleHeight
# Draw results on the input image
original_image = visualize(original_image, results, fps=tm.getFPS())
# Visualize results in a new Window
cv.imshow('{} Demo'.format(model.name), original_image)
tm.reset()
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