vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

2026-08-22 00:10:33 +08:00
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+#!/usr/bin/env python
+'''
+This file is part of OpenCV project.
+It is subject to the license terms in the LICENSE file found in the top-level directory
+of this distribution and at http://opencv.org/license.html.
+
+This sample deblurs the given blurry image.
+
+Copyright (C) 2025, Bigvision LLC.
+
+How to use:
+    Sample command to run:
+        `python deblurring.py`
+
+    You can download NAFNet deblurring model using
+        `python download_models.py NAFNet`
+
+    References:
+      Github: https://github.com/megvii-research/NAFNet
+      PyTorch model: https://drive.google.com/file/d/14D4V4raNYIOhETfcuuLI3bGLB-OYIv6X/view
+
+      PyTorch model was converted to ONNX and then ONNX model was further quantized using block quantization from [opencv_zoo](https://github.com/opencv/opencv_zoo/blob/main/tools/quantize/block_quantize.py)
+
+    Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to point to the directory where models are downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.
+'''
+
+import argparse
+import cv2 as cv
+import numpy as np
+from common import *
+
+def help():
+    print(
+        '''
+        Use this script for image deblurring using OpenCV.
+
+        Firstly, download required models i.e. NAFNet using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.
+
+        To run:
+        Example: python deblurring.py [--input=<image_name>]
+
+        Deblurring model path can also be specified using --model argument.
+        '''
+    )
+
+def get_args_parser():
+    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', '-i', default="licenseplate_motion.jpg", help='Path to image file.', 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, 'deblurring', prefix="", alias="NAFNet")
+    parser = argparse.ArgumentParser(parents=[parser],
+                                        description='Image deblurring using OpenCV.',
+                                        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    return parser.parse_args()
+
+def main():
+    if hasattr(args, 'help'):
+        help()
+        exit(1)
+
+    args.model = findModel(args.model, args.sha1)
+
+    engine = cv.dnn.ENGINE_AUTO
+
+    if args.backend != "default" or args.target != "cpu":
+        engine = cv.dnn.ENGINE_CLASSIC
+
+    net = cv.dnn.readNetFromONNX(args.model, engine)
+    net.setPreferableBackend(get_backend_id(args.backend))
+    net.setPreferableTarget(get_target_id(args.target))
+
+    input_image = cv.imread(findFile(args.input))
+    image = input_image.copy()
+    height, width = image.shape[:2]
+
+    image_blob = cv.dnn.blobFromImage(image, args.scale, (width, height), args.mean, args.rgb, False)
+    net.setInput(image_blob)
+    out = net.forward()
+
+    # Postprocessing
+    output = out[0]
+    output = np.transpose(output, (1, 2, 0))
+    output = np.clip(output * 255.0, 0, 255).astype(np.uint8)
+    out_image = cv.cvtColor(output, cv.COLOR_RGB2BGR)
+
+    cv.imshow("input image: ", input_image)
+    cv.imshow("output image: ", out_image)
+    cv.waitKey(0)
+
+if __name__ == '__main__':
+    args = get_args_parser()
+    main()