Spaces:
Sleeping
Sleeping
File size: 3,592 Bytes
d2fc863 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import gradio as gr
import cv2
import numpy as np
# Load Haar Cascade for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
def process_image(image, operation, canny_threshold1=100, canny_threshold2=200, blur_kernel=5):
# Convert Gradio image (PIL) to OpenCV format (BGR)
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Initialize output dictionary
outputs = {}
# Perform selected operation
if operation == "Grayscale":
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
outputs["Grayscale Image"] = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
elif operation == "Canny Edge Detection":
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, canny_threshold1, canny_threshold2)
outputs["Edges"] = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
elif operation == "Gaussian Blur":
if blur_kernel % 2 == 0:
blur_kernel += 1 # Kernel size must be odd
blurred = cv2.GaussianBlur(image, (blur_kernel, blur_kernel), 0)
outputs["Blurred Image"] = blurred
elif operation == "Face Detection":
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
output_image = image.copy()
for (x, y, w, h) in faces:
cv2.rectangle(output_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
outputs["Faces Detected"] = output_image
# Convert back to RGB for Gradio display
for key in outputs:
outputs[key] = cv2.cvtColor(outputs[key], cv2.COLOR_BGR2RGB)
return outputs
# Define Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# OpenCV Feature Demo")
gr.Markdown("Upload an image and select an OpenCV operation to apply.")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload Image", type="pil")
operation = gr.Dropdown(
choices=["Grayscale", "Canny Edge Detection", "Gaussian Blur", "Face Detection"],
label="Select Operation",
value="Grayscale"
)
canny_threshold1 = gr.Slider(0, 500, value=100, step=10, label="Canny Threshold 1", visible=False)
canny_threshold2 = gr.Slider(0, 500, value=200, step=10, label="Canny Threshold 2", visible=False)
blur_kernel = gr.Slider(3, 21, value=5, step=2, label="Blur Kernel Size", visible=False)
# Show/hide sliders based on operation
def update_sliders(op):
if op == "Canny Edge Detection":
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False)
elif op == "Gaussian Blur":
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
else:
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
operation.change(update_sliders, inputs=operation, outputs=[canny_threshold1, canny_threshold2, blur_kernel])
with gr.Column():
output = gr.Gallery(label="Processed Image")
submit_button = gr.Button("Process Image")
submit_button.click(
fn=process_image,
inputs=[image_input, operation, canny_threshold1, canny_threshold2, blur_kernel],
outputs=output
)
if __name__ == "__main__":
demo.launch() |