''' This sample demonstrates edge detection with dexined and canny edge detection techniques. For switching between deep learning based model(dexined) and canny edge detector, press space bar in case of video. In case of image, pass the argument --method for switching between dexined and canny. ''' import cv2 as cv import argparse import numpy as np from common import * def get_args_parser(func_args): backends = ("default", "openvino", "opencv", "vkcom", "cuda") targets = ("cpu", "opencl", "opencl_fp16", "ncs2_vpu", "hddl_vpu", "vulkan", "cuda", "cuda_fp16") parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--zoo', default=os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models.yml'), help='An optional path to file with preprocessing parameters.') parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.', default=0, required=False) parser.add_argument('--method', help='choose method: dexined or canny', default='canny', required=False) parser.add_argument('--backend', default="default", type=str, choices=backends, help="Choose one of computation backends: " "default: automatically (by default), " "openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), " "opencv: OpenCV implementation, " "vkcom: VKCOM, " "cuda: CUDA, " "webnn: WebNN") parser.add_argument('--target', default="cpu", type=str, choices=targets, help="Choose one of target computation devices: " "cpu: CPU target (by default), " "opencl: OpenCL, " "opencl_fp16: OpenCL fp16 (half-float precision), " "ncs2_vpu: NCS2 VPU, " "hddl_vpu: HDDL VPU, " "vulkan: Vulkan, " "cuda: CUDA, " "cuda_fp16: CUDA fp16 (half-float preprocess)") args, _ = parser.parse_known_args() add_preproc_args(args.zoo, parser, 'edge_detection', 'dexined') parser = argparse.ArgumentParser(parents=[parser], description=''' To run: Canny: python edge_detection.py --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera) Dexined: python edge_detection.py dexined --input=path/to/your/input/image/or/video "In case of video input, for switching between deep learning based model (Dexined) and Canny edge detector, press space bar. Pass as argument in case of image input." Model path can also be specified using --model argument ''', formatter_class=argparse.RawTextHelpFormatter) return parser.parse_args(func_args) threshold1 = 0 threshold2 = 50 blur_amount = 5 gray = None def sigmoid(x): return 1.0 / (1.0 + np.exp(-x)) def post_processing(output, shape): h, w = shape preds = [] for p in output: img = sigmoid(p) img = np.squeeze(img) img = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U) img = cv.resize(img, (w, h)) preds.append(img) fuse = preds[-1] ave = np.array(preds, dtype=np.float32) ave = np.uint8(np.mean(ave, axis=0)) return fuse, ave def apply_canny(image): global threshold1, threshold2, blur_amount kernel_size = 2 * blur_amount + 1 blurred = cv.GaussianBlur(image, (kernel_size, kernel_size), 0) result = cv.Canny(blurred, threshold1, threshold2) cv.imshow('Output', result) def setupCannyWindow(image): global gray cv.destroyWindow('Output') cv.namedWindow('Output', cv.WINDOW_AUTOSIZE) cv.moveWindow('Output', 200, 50) gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) cv.createTrackbar('thrs1', 'Output', threshold1, 255, lambda value: [globals().__setitem__('threshold1', value), apply_canny(gray)]) cv.createTrackbar('thrs2', 'Output', threshold2, 255, lambda value: [globals().__setitem__('threshold2', value), apply_canny(gray)]) cv.createTrackbar('blur', 'Output', blur_amount, 20, lambda value: [globals().__setitem__('blur_amount', value), apply_canny(gray)]) def loadModel(args): net = cv.dnn.readNetFromONNX(args.model) net.setPreferableBackend(get_backend_id(args.backend)) net.setPreferableTarget(get_target_id(args.target)) return net def apply_dexined(model, image): out = model.forward() result,_ = post_processing(out, image.shape[:2]) t, _ = model.getPerfProfile() label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency()) cv.putText(image, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255)) cv.putText(result, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255)) cv.imshow("Output", result) def main(func_args=None): args = get_args_parser(func_args) cap = cv.VideoCapture(cv.samples.findFile(args.input) if args.input else 0) if not cap.isOpened(): print("Failed to open the input video") exit(-1) cv.namedWindow('Input', cv.WINDOW_AUTOSIZE) cv.namedWindow('Output', cv.WINDOW_AUTOSIZE) cv.moveWindow('Output', 200, 50) method = args.method if os.getenv('OPENCV_SAMPLES_DATA_PATH') is not None or hasattr(args, 'model'): try: args.model = findModel(args.model, args.sha1) method = 'dexined' except: print("[WARN] Model file not provided, using canny instead. Pass model using --model=/path/to/dexined.onnx to use dexined model.") method = 'canny' args.model = None else: print("[WARN] Model file not provided, using canny instead. Pass model using --model=/path/to/dexined.onnx to use dexined model.") method = 'canny' if method == 'canny': dummy = np.zeros((512, 512, 3), dtype="uint8") setupCannyWindow(dummy) net = None if method == "dexined": net = loadModel(args) while cv.waitKey(1) < 0: hasFrame, image = cap.read() if not hasFrame: print("Press any key to exit") cv.waitKey(0) break if method == "canny": global gray gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY) apply_canny(gray) elif method == "dexined": inp = cv.dnn.blobFromImage(image, args.scale, (args.width, args.height), args.mean, swapRB=args.rgb, crop=False) net.setInput(inp) apply_dexined(net, image) cv.imshow("Input", image) key = cv.waitKey(30) if key == ord(' ') and method == 'canny': if hasattr(args, 'model') and args.model is not None: print("model: ", args.model) method = "dexined" if net is None: net = loadModel(args) cv.destroyWindow('Output') cv.namedWindow('Output', cv.WINDOW_AUTOSIZE) cv.moveWindow('Output', 200, 50) else: print("[ERROR] Provide model file using --model to use dexined. Download model using python download_models.py dexined from dnn samples directory") elif key == ord(' ') and method=='dexined': method = "canny" setupCannyWindow(image) elif key == 27 or key == ord('q'): break cv.destroyAllWindows() if __name__ == '__main__': main()