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 of this distribution and at http://opencv.org/license.html.
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Copyright (C) 2025, Bigvision LLC.
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MODNet Alpha Matting with OpenCV DNN
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This sample demonstrates human portrait alpha matting using MODNet model.
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MODNet is a trimap-free portrait matting method that can produce high-quality
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alpha mattes for portrait images in real-time.
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Reference:
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Github: https://github.com/ZHKKKe/MODNet
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To download the MODNet model, run:
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python download_models.py modnet
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Usage:
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python alpha_matting.py --input=image.jpg
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"""
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import cv2 as cv
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import numpy as np
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import argparse
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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",
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default="messi5.jpg",
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help="Path to input image or video file. Defaults to messi5.jpg in samples/data.",
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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, "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA",
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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 precision)",
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)
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args, _ = parser.parse_known_args()
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add_preproc_args(args.zoo, parser, "alpha_matting", "modnet")
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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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python alpha_matting.py --input=path/to/your/input/image
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Model path can also be specified using --model argument
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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 postprocess_output(image, alpha_output):
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"""Process model output to create alpha mask."""
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h, w = image.shape[:2]
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alpha = alpha_output[0, 0] if alpha_output.ndim == 4 else alpha_output[0]
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alpha = cv.resize(alpha, (w, h))
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alpha = np.clip(alpha, 0, 1)
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alpha_mask = (alpha * 255).astype(np.uint8)
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return alpha_mask
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def loadModel(args, engine):
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net = cv.dnn.readNetFromONNX(args.model, engine)
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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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def draw_label(img, text, color):
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h, w = img.shape[:2]
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font_scale = max(h, w) / 1000.0
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thickness = 1
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text_size, _ = cv.getTextSize(text, cv.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
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x = 10
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y = text_size[1] + 10
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cv.putText(img, text, (x, y), cv.FONT_HERSHEY_SIMPLEX, font_scale, color, thickness)
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def apply_modnet(args, model, image):
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inp = cv.dnn.blobFromImage(
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image, args.scale, (args.width, args.height), args.mean, swapRB=args.rgb
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)
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model.setInput(inp)
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t0 = cv.getTickCount()
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out = model.forward()
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t = (cv.getTickCount() - t0) / cv.getTickFrequency()
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alpha_mask = postprocess_output(image, out)
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alpha_3ch = cv.merge([alpha_mask / 255.0, alpha_mask / 255.0, alpha_mask / 255.0])
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composite = (image.astype(np.float32) * alpha_3ch).astype(np.uint8)
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return alpha_mask, composite, t
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def main(func_args=None):
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args = get_args_parser(func_args)
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engine = cv.dnn.ENGINE_AUTO
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if args.backend != "default" or args.target != "cpu":
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engine = cv.dnn.ENGINE_CLASSIC
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image = cv.imread(cv.samples.findFile(args.input))
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if image is None:
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print("Failed to load the input image")
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exit(-1)
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cv.namedWindow("Input", cv.WINDOW_AUTOSIZE)
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cv.namedWindow("Alpha Mask", cv.WINDOW_AUTOSIZE)
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cv.namedWindow("Composite", cv.WINDOW_AUTOSIZE)
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cv.moveWindow("Alpha Mask", 200, 50)
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cv.moveWindow("Composite", 400, 50)
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args.model = findModel(args.model, args.sha1)
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net = loadModel(args, engine)
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alpha_mask, composite, t = apply_modnet(args, net, image)
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label = "Inference time: %.2f ms" % (t * 1000.0)
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draw_label(image, label, (0, 255, 0))
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draw_label(alpha_mask, label, (255, 255, 255))
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draw_label(composite, label, (0, 255, 0))
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cv.imshow("Input", image)
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cv.imshow("Alpha Mask", alpha_mask)
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cv.imshow("Composite", composite)
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print("Press any key to exit")
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cv.waitKey(0)
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cv.destroyAllWindows()
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if __name__ == "__main__":
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main()
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