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import gradio as gr
import cv2
import numpy as np
import cupy as cp
# CUDA kernel for custom filter (example sharpening)
custom_filter_kernel = cp.array([[0, -1, 0],
[-1, 5, -1],
[0, -1, 0]], dtype=cp.float32)
def edge_detection_cuda(image):
img_gpu = cp.asarray(image, dtype=cp.uint8)
gray_gpu = cp.asarray(cv2.cvtColor(image, cv2.COLOR_RGB2GRAY))
edges_gpu = cv2.Canny(cp.asnumpy(gray_gpu), 100, 200)
return cv2.cvtColor(edges_gpu, cv2.COLOR_GRAY2RGB)
def gaussian_blur_cuda(image):
img_gpu = cp.asarray(image, dtype=cp.float32)
blurred_gpu = cv2.GaussianBlur(cp.asnumpy(img_gpu), (15, 15), 0)
return blurred_gpu.astype(np.uint8)
def custom_filter_cuda(image):
img_gpu = cp.asarray(image, dtype=cp.float32)
img_gpu = cv2.filter2D(cp.asnumpy(img_gpu), -1, cp.asnumpy(custom_filter_kernel))
return img_gpu.astype(np.uint8)
def process_image(image, operation):
if image is None:
return None, None, None
edge_result = None
blur_result = None
custom_result = None
if operation == "All":
# Run operations in parallel using CUDA Streams
stream1 = cp.cuda.Stream()
stream2 = cp.cuda.Stream()
stream3 = cp.cuda.Stream()
with stream1:
edge_result = edge_detection_cuda(image)
with stream2:
blur_result = gaussian_blur_cuda(image)
with stream3:
custom_result = custom_filter_cuda(image)
stream1.synchronize()
stream2.synchronize()
stream3.synchronize()
elif operation == "Edge Detection":
edge_result = edge_detection_cuda(image)
elif operation == "Gaussian Blur":
blur_result = gaussian_blur_cuda(image)
elif operation == "Custom Filter":
custom_result = custom_filter_cuda(image)
return edge_result, blur_result, custom_result
with gr.Blocks() as demo:
gr.Markdown("## 🖼️ Parallel Image Processing with CUDA (GPU)")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="numpy", label="Upload Image")
process_btn = gr.Button("Process Image", variant="primary")
with gr.Column():
operation = gr.Radio(["All", "Edge Detection", "Gaussian Blur", "Custom Filter"],
label="Choose Operation", value="All")
with gr.Row():
edge_output = gr.Image(label="Edge Detection Result")
blur_output = gr.Image(label="Gaussian Blur Result")
custom_output = gr.Image(label="Custom Filter Result")
process_btn.click(process_image, inputs=[image_input, operation],
outputs=[edge_output, blur_output, custom_output])
# Launch app
demo.launch()
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