vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
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"""
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This file is part of OpenCV project.
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It is subject to the license terms in the LICENSE file found in the top-level directory
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of this distribution and at http://opencv.org/license.html.
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Copyright (C) 2025, Bigvision LLC.
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This sample demonstrates super-resolution using the SeeMoreDetails model.
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The model upscales images by 4x while enhancing details and reducing noise.
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Supports image inputs only.
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SeeMoreDetails Repo: https://github.com/eduardzamfir/seemoredetails
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"""
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import cv2 as cv
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import argparse
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import numpy as np
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import os
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from common import *
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def get_args_parser(func_args):
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backends = ("default", "openvino", "opencv", "vkcom", "cuda")
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targets = (
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"cpu",
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"opencl",
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"opencl_fp16",
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"ncs2_vpu",
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"hddl_vpu",
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"vulkan",
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"cuda",
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"cuda_fp16",
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)
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parser = argparse.ArgumentParser(add_help=False)
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parser.add_argument(
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"--zoo",
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default=os.path.join(os.path.dirname(os.path.abspath(__file__)), "models.yml"),
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help="An optional path to file with preprocessing parameters.",
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)
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parser.add_argument(
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"--input", help="Path to input image file.", default="chicky_512.png", required=False
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)
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parser.add_argument(
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"--backend",
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default="default",
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type=str,
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choices=backends,
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help="Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN",
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)
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parser.add_argument(
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"--target",
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default="cpu",
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type=str,
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choices=targets,
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help="Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"ncs2_vpu: NCS2 VPU, "
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"hddl_vpu: HDDL VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float preprocess)",
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)
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args, _ = parser.parse_known_args()
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model_name = "seemoredetails"
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add_preproc_args(args.zoo, parser, "super_resolution", model_name)
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parser = argparse.ArgumentParser(
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parents=[parser],
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description="""
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To run:
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Default image:
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python super_resolution.py
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Image processing:
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python super_resolution.py --input=path/to/your/input/image.jpg
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The model performs 4x super-resolution on input images.
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""",
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formatter_class=argparse.RawTextHelpFormatter,
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)
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return parser.parse_args(func_args)
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def load_model(args):
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"""Load the super-resolution model"""
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try:
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model_path = findModel(args.model, args.sha1)
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net = cv.dnn.readNetFromONNX(model_path)
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net.setPreferableBackend(get_backend_id(args.backend))
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net.setPreferableTarget(get_target_id(args.target))
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return net
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except Exception as e:
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print(f"Error loading model: {e}")
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return None
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def postprocess_output(output, args, original_shape=None):
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"""Postprocess model output to displayable image"""
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output = np.squeeze(output, axis=0)
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output = np.clip(output, 0, 1)
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output = np.transpose(output, (1, 2, 0))
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output = (output * 255).astype(np.uint8)
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output = cv.cvtColor(output, cv.COLOR_RGB2BGR)
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if original_shape is not None:
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target_height, target_width = original_shape
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upscaled_height, upscaled_width = target_height * 4, target_width * 4
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output = cv.resize(output, (upscaled_width, upscaled_height))
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return output
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def apply_super_resolution(net, image, args):
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"""Apply super-resolution to a single image"""
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original_shape = image.shape[:2]
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blob = cv.dnn.blobFromImage(
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image,
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scalefactor=args.scale,
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size=(args.width, args.height),
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mean=args.mean,
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swapRB=args.rgb,
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crop=False,
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)
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net.setInput(blob)
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t0 = cv.getTickCount()
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output = net.forward()
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t = (cv.getTickCount() - t0) / cv.getTickFrequency()
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result = postprocess_output(output, args, original_shape)
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label = "Inference time: %.2f ms" % (t * 1000.0)
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cv.putText(result, label, (10, 30), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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return result
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def main(func_args=None):
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args = get_args_parser(func_args)
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net = load_model(args)
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if net is None:
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print("Failed to load model.")
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return -1
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input_path = cv.samples.findFile(args.input)
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image = cv.imread(input_path)
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if image is None:
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print(f"Cannot load image: {input_path}")
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return -1
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print(f"Processing image: {input_path}")
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result = apply_super_resolution(net, image, args)
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cv.namedWindow("Input", cv.WINDOW_NORMAL)
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cv.namedWindow("Super-Resolution Result", cv.WINDOW_NORMAL)
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cv.imshow("Input", image)
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cv.imshow("Super-Resolution Result", result)
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print("Press 'q' to quit...")
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while True:
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key = cv.waitKey(0) & 0xFF
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if key == ord("q"):
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break
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cv.destroyAllWindows()
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return 0
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if __name__ == "__main__":
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main()
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