vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
This commit is contained in:
Gitea Mirror Bot
@@ -0,0 +1,199 @@
|
||||
#!/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 inpaints the masked area in the given image.
|
||||
|
||||
Copyright (C) 2025, Bigvision LLC.
|
||||
|
||||
How to use:
|
||||
Sample command to run:
|
||||
`python inpainting.py`
|
||||
The system will ask you to draw the mask to be inpainted
|
||||
|
||||
You can download lama inpainting model using
|
||||
`python download_models.py lama`
|
||||
|
||||
References:
|
||||
Github: https://github.com/advimman/lama
|
||||
ONNX model: https://huggingface.co/Carve/LaMa-ONNX/blob/main/lama_fp32.onnx
|
||||
|
||||
ONNX model was further quantized using block quantization from [opencv_zoo](https://github.com/opencv/opencv_zoo)
|
||||
|
||||
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 os.path
|
||||
import numpy as np
|
||||
import cv2 as cv
|
||||
from common import *
|
||||
|
||||
def help():
|
||||
print(
|
||||
'''
|
||||
Use this script for image inpainting using OpenCV.
|
||||
|
||||
Firstly, download required models i.e. lama 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 inpainting.py [--input=<image_name>]
|
||||
|
||||
Inpainting model path can also be specified using --model argument.
|
||||
'''
|
||||
)
|
||||
|
||||
def keyboard_shorcuts():
|
||||
print('''
|
||||
Keyboard Shorcuts:
|
||||
Press 'i' to increase brush size.
|
||||
Press 'd' to decrease brush size.
|
||||
Press 'r' to reset mask.
|
||||
Press ' ' (space bar) after selecting area to be inpainted.
|
||||
Press ESC to terminate the program.
|
||||
'''
|
||||
)
|
||||
|
||||
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="rubberwhale1.png", 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, 'inpainting', prefix="", alias="lama")
|
||||
parser = argparse.ArgumentParser(parents=[parser],
|
||||
description='Image inpainting using OpenCV.',
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
drawing = False
|
||||
mask_gray = None
|
||||
brush_size = 15
|
||||
|
||||
def draw_mask(event, x, y, flags, param):
|
||||
global drawing, mask_gray, brush_size
|
||||
if event == cv.EVENT_LBUTTONDOWN:
|
||||
drawing = True
|
||||
elif event == cv.EVENT_MOUSEMOVE:
|
||||
if drawing:
|
||||
cv.circle(mask_gray, (x, y), brush_size, (255), thickness=-1)
|
||||
elif event == cv.EVENT_LBUTTONUP:
|
||||
drawing = False
|
||||
|
||||
def main():
|
||||
global mask_gray, brush_size
|
||||
|
||||
print("Model loading...")
|
||||
|
||||
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))
|
||||
aspect_ratio = input_image.shape[0]/input_image.shape[1]
|
||||
height = int(args.width*aspect_ratio)
|
||||
|
||||
input_image = cv.resize(input_image, (args.width, height))
|
||||
image = input_image.copy()
|
||||
keyboard_shorcuts()
|
||||
|
||||
stdSize = 0.7
|
||||
stdWeight = 2
|
||||
stdImgSize = 512
|
||||
imgWidth = min(input_image.shape[:2])
|
||||
fontSize = min(1.5, (stdSize*imgWidth)/stdImgSize)
|
||||
fontThickness = max(1,(stdWeight*imgWidth)//stdImgSize)
|
||||
|
||||
label = "Press 'i' to increase, 'd' to decrease brush size. And 'r' to reset mask. "
|
||||
labelSize, _ = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, fontSize, fontThickness)
|
||||
alpha = 0.5
|
||||
# Setting up the window
|
||||
cv.namedWindow("Draw Mask")
|
||||
cv.setMouseCallback("Draw Mask", draw_mask)
|
||||
temp_image = input_image.copy()
|
||||
overlay = input_image.copy()
|
||||
cv.rectangle(overlay, (0, 0), (labelSize[0]+10, labelSize[1]+int(30*fontSize)), (255, 255, 255), cv.FILLED)
|
||||
cv.addWeighted(overlay, alpha, temp_image, 1 - alpha, 0, temp_image)
|
||||
cv.putText(temp_image, "Draw the mask on the image. Press space bar when done.", (10, int(25*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness)
|
||||
cv.putText(temp_image, label, (10, int(50*fontSize)), cv.FONT_HERSHEY_SIMPLEX, fontSize, (0, 0, 0), fontThickness)
|
||||
display_image = temp_image.copy()
|
||||
|
||||
while True:
|
||||
mask_gray = np.zeros((input_image.shape[0], input_image.shape[1]), dtype=np.uint8)
|
||||
display_image = temp_image.copy()
|
||||
while True:
|
||||
display_image[mask_gray > 0] = [255, 255, 255]
|
||||
cv.imshow("Draw Mask", display_image)
|
||||
key = cv.waitKey(30) & 0xFF
|
||||
if key == ord('i'): # Increase brush size
|
||||
brush_size += 1
|
||||
print(f"Brush size increased to {brush_size}")
|
||||
elif key == ord('d'): # Decrease brush size
|
||||
brush_size = max(1, brush_size - 1)
|
||||
print(f"Brush size decreased to {brush_size}")
|
||||
elif key == ord('r'): # clear the mask
|
||||
mask_gray = np.zeros((input_image.shape[0], input_image.shape[1]), dtype=np.uint8)
|
||||
display_image = temp_image.copy()
|
||||
print(f"Mask cleared")
|
||||
elif key == ord(' '): # Press space bar to finish drawing
|
||||
break
|
||||
elif key == 27:
|
||||
exit()
|
||||
|
||||
print("Processing image...")
|
||||
# Inference block
|
||||
image_blob = cv.dnn.blobFromImage(image, args.scale, (args.width, args.height), args.mean, args.rgb, False)
|
||||
mask_blob = cv.dnn.blobFromImage(mask_gray, scalefactor=1.0, size=(args.width, args.height), mean=(0,), swapRB=False, crop=False)
|
||||
mask_blob = (mask_blob > 0).astype(np.float32)
|
||||
|
||||
net.setInput(image_blob, "image")
|
||||
net.setInput(mask_blob, "mask")
|
||||
|
||||
output = net.forward()
|
||||
|
||||
# Postprocessing
|
||||
output_image = output[0]
|
||||
output_image = np.transpose(output_image, (1, 2, 0))
|
||||
output_image = (output_image).astype(np.uint8)
|
||||
output_image = cv.resize(output_image, (args.width, height))
|
||||
image = output_image
|
||||
|
||||
cv.imshow("Inpainted Output", output_image)
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = get_args_parser()
|
||||
main()
|
||||
Reference in New Issue
Block a user