Detailed explanation of the code that implements GPU-accelerated image processing in Python

import torch
import  as nn
import cv2
import numpy as np

# Check if the GPU is availabledevice = ("cuda" if .is_available() else "cpu")
print(f"Using device: {device}")

# Read the image and convert it to PyTorch tensorimage = ("")  # Read BGR format imagesimage = (image, cv2.COLOR_BGR2RGB)  # Convert to RGBimage_tensor = torch.from_numpy(image).float().permute(2, 0, 1)  # HWC -> CHW
image_tensor = image_tensor.unsqueeze(0).to(device)  # Add batch dimension and move to GPU
# Define edge detection convolution kernel (Sobel operator)conv_layer = nn.Conv2d(
    in_channels=3,
    out_channels=3,
    kernel_size=3,
    bias=False,
    padding=1
).to(device)

# Set Sobel core weight manually (example, only for horizontal edges)sobel_kernel = ([
    [[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],  # Red Channel    [[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],  # Green Channel    [[[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]],  # Blue Channel], dtype=torch.float32).repeat(3, 1, 1, 1).to(device)

conv_layer. = sobel_kernel

# Perform convolution operations (GPU acceleration)with torch.no_grad():
    output_tensor = conv_layer(image_tensor)

# Convert the result back to numpy and saveoutput = output_tensor.squeeze(0).permute(1, 2, 0).cpu().numpy()
output = (output, 0, 255).astype(np.uint8)
("edge_detection_gpu.jpg", (output, cv2.COLOR_RGB2BGR))

import cv2
import time

# Check whether OpenCV supports CUDAprint("CUDA devices:", ())

# Read the image and upload it to the GPUimage = ("")
gpu_image = cv2.cuda_GpuMat()
gpu_image.upload(image)

# Create a GPU-accelerated Gaussian filtergaussian_filter = (
    cv2.CV_8UC3,  # Input type (8-bit unsigned, 3 channels)    cv2.CV_8UC3,  # Output Type    (15, 15),      # core size    0              # Sigma (automatic calculation))

# Perform filtering (repeat multiple test speed)start_time = ()
for _ in range(100):  # Repeat 100 times to simulate large data volume    gpu_blur = gaussian_filter.apply(gpu_image)
end_time = ()

# Download the results to the CPU and saveresult = gpu_blur.download()
print(f"GPU Time: {end_time - start_time:.4f} seconds")
("blur_gpu.jpg", result)

import cupy as cp
import cv2
import numpy as np
import time

# Read the image and turn it to grayscaleimage = ("", cv2.IMREAD_GRAYSCALE)

# Convert numpy array to CuPy array (upload to GPU)image_gpu = (image)

# Fast Fourier Transform (FFT) and Inverse Transform (IFFT)start_time = ()
fft_gpu = .fft2(image_gpu)
fft_shift = (fft_gpu)
magnitude_spectrum = ((fft_shift))
end_time = ()

# Turn the result back to the CPUmagnitude_cpu = (magnitude_spectrum)
print(f"GPU FFT Time: {end_time - start_time:.4f} seconds")

# Normalize and save the spectrummagnitude_cpu = (magnitude_cpu, None, 0, 255, cv2.NORM_MINMAX)
("fft_spectrum_gpu.jpg", magnitude_cpu.astype(np.uint8))

from numba import cuda
import numpy as np
import cv2
import time

# Read the imageimage = ("")
height, width, channels = 

# Define GPU kernel functions@
def invert_colors_kernel(image):
    x, y = (2)
    if x < [0] and y < [1]:
        for c in range(3):  # traverse RGB channels            image[x, y, c] = 255 - image[x, y, c]

# Upload the image to the GPUimage_gpu = cuda.to_device(image)

# Configure threads and blocksthreads_per_block = (16, 16)
blocks_per_grid_x = (height + threads_per_block[0] - 1) // threads_per_block[0]
blocks_per_grid_y = (width + threads_per_block[1] - 1) // threads_per_block[1]
blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y)

# Execute kernel functionsstart_time = ()
invert_colors_kernel[blocks_per_grid, threads_per_block](image_gpu)
()  # Wait for the GPU to completeend_time = ()

# Download the results and saveimage_cpu = image_gpu.copy_to_host()
print(f"GPU Invert Time: {end_time - start_time:.6f} seconds")
("inverted_gpu.jpg", image_cpu)

import torch
import  as models
from torchvision import transforms
from PIL import Image

# Load the pretrained model to the GPUdevice = ("cuda" if .is_available() else "cpu")
model = models.vgg19(pretrained=True).(device).eval()

# Image preprocessingpreprocess = ([
    (512),
    (),
    (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load content images and style imagescontent_image = ("")
style_image = ("")

# Convert the image to a tensor and move it to the GPUcontent_tensor = preprocess(content_image).unsqueeze(0).to(device)
style_tensor = preprocess(style_image).unsqueeze(0).to(device)

# Define style transfer function (example, complete loss calculation and optimization)def style_transfer(model, content_input, style_input, iterations=500):
    # Create optimized images    input_image = content_input.clone().requires_grad_(True)
    
    # Define an optimizer    optimizer = ([input_image])
    
    # Style Transfer Loop    for i in range(iterations):
        def closure():
            optimizer.zero_grad()
            # Extract features and calculate losses (requires specific details)            # ...
            return total_loss
        
        (closure)
    
    return input_image

# Perform style migration (requires complete code)output_image = style_transfer(model, content_tensor, style_tensor)

# Post-process and save the resultsoutput_image = output_image.squeeze().cpu().detach()
output_image = ()(output_image)
output_image.save("style_transfer_gpu.jpg")

pip install torch torchvision opencv-python-headless cupy numba