# 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()