Create refine.py

refine.py

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+import numpy as np
+import cv2
+import gradio as gr
+from PIL import Image
+
+# Function to apply image enhancement filters
+def apply_filters(image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe):
+    img = np.array(image)
+
+    # Auto Enhancement
+    if auto:
+        lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
+        l, a, b = cv2.split(lab)
+        clahe_filter = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+        l = clahe_filter.apply(l)
+        img = cv2.merge((l, a, b))
+        img = cv2.cvtColor(img, cv2.COLOR_LAB2RGB)
+
+    # Scaling
+    if scale != 1.0:
+        height, width = img.shape[:2]
+        img = cv2.resize(img, (int(width * scale), int(height * scale)))
+    
+    # Noise Reduction (Bilateral Filtering)
+    if noise:
+        img = cv2.bilateralFilter(img, 9, 75, 75)
+
+    # Denoising (Non-Local Means)
+    if denoise:
+        img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
+    
+    # Sharpening
+    if sharpen:
+        kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+        img = cv2.filter2D(img, -1, kernel)
+    
+    # Grayscale Conversion
+    if grayscale or threshold or hist_eq or clahe:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+    # Histogram Equalization
+    if hist_eq:
+        img = cv2.equalizeHist(img)
+    
+    # CLAHE (Contrast Limited Adaptive Histogram Equalization)
+    if clahe:
+        clahe_filter = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+        img = clahe_filter.apply(img)
+    
+    # Adaptive Thresholding
+    if threshold:
+        img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+    
+    # Edge Detection
+    if edges:
+        img = cv2.Canny(img, 100, 200)
+    
+    # Invert Colors
+    if invert:
+        img = cv2.bitwise_not(img)
+    
+    # Gamma Correction
+    if gamma != 1.0:
+        inv_gamma = 1.0 / gamma
+        table = np.array([(i / 255.0) ** inv_gamma * 255 for i in np.arange(0, 256)]).astype("uint8")
+        img = cv2.LUT(img, table)
+
+    # Blur
+    if blur:
+        img = cv2.GaussianBlur(img, (2*blur + 1, 2*blur + 1), 0)
+    
+    # Contrast & Brightness Adjustment
+    if contrast != 1.0 or brightness != 0:
+        img = cv2.convertScaleAbs(img, alpha=contrast, beta=brightness)
+    
+    return img
+
+# Gradio interface to upload an image and display results
+def enhance_image(image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe):
+    refined_img = apply_filters(image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe)
+    return Image.fromarray(refined_img)
+
+# Setup Gradio app
+with gr.Blocks() as demo:
+    gr.Markdown("# 🖼️ Refine Image for License Plate Detection")
+    gr.Markdown("Enhance the license plate image for better recognition.")
+
+    # Image upload
+    uploaded_image = gr.Image(type="pil", label="Upload Image for License Plate Detection")
+
+    # Enhancement options sidebar
+    with gr.Column():
+        blur = gr.Slider(0, 10, label="🔹 Blur", step=1)
+        contrast = gr.Slider(0.5, 2.0, label="🔹 Contrast", step=0.1)
+        brightness = gr.Slider(0.5, 2.0, label="🔹 Brightness", step=0.1)
+        gamma = gr.Slider(0.1, 3.0, label="Gamma Correction", step=0.1)
+        scale = gr.Slider(1.0, 10.0, label="🔹 Scale", step=0.1)
+        noise = gr.Checkbox(label="Noise Reduction (Bilateral)")
+        denoise = gr.Checkbox(label="Denoise (Non-Local Means)")
+        sharpen = gr.Checkbox(label="Sharpening")
+        hist_eq = gr.Checkbox(label="Histogram Equalization")
+        clahe = gr.Checkbox(label="CLAHE (Advanced Contrast)")
+        grayscale = gr.Checkbox(label="Grayscale Conversion")
+        threshold = gr.Checkbox(label="Adaptive Thresholding")
+        edges = gr.Checkbox(label="Edge Detection")
+        invert = gr.Checkbox(label="Invert Colors")
+        auto = gr.Checkbox(label="Auto Enhancement")
+
+    # Output fields
+    image_output = gr.Image(label="Refined License Plate Image")
+    
+    # Button to submit the image for enhancement
+    submit_button = gr.Button("Submit Image")
+
+    # Submit button action
+    submit_button.click(enhance_image, inputs=[uploaded_image, noise, sharpen, grayscale, threshold, edges, invert, auto, blur, contrast, brightness, scale, denoise, hist_eq, gamma, clahe], outputs=image_output)
+
+demo.launch()