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app.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+
+def apply_gaussian_blur(frame, intensity):
+    ksize = int(intensity) * 2 + 1
+    return cv2.GaussianBlur(frame, (ksize, ksize), 0)
+
+def apply_sharpening_filter(frame):
+    kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+    return cv2.filter2D(frame, -1, kernel)
+
+def apply_edge_detection(frame):
+    return cv2.Canny(frame, 100, 200)
+
+def apply_invert_filter(frame):
+    return cv2.bitwise_not(frame)
+
+def adjust_brightness_contrast(frame, alpha=1.0, beta=0):
+    return cv2.convertScaleAbs(frame, alpha=alpha, beta=beta)
+
+def apply_grayscale_filter(frame):
+    return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+def apply_sepia_filter(frame):
+    sepia_filter = np.array([[0.272, 0.534, 0.131],
+                             [0.349, 0.686, 0.168],
+                             [0.393, 0.769, 0.189]])
+    sepia_frame = cv2.transform(frame, sepia_filter)
+    sepia_frame = np.clip(sepia_frame, 0, 255)
+    return sepia_frame
+
+def apply_fall_filter(frame):
+    fall_filter = np.array([[0.393, 0.769, 0.189],
+                            [0.349, 0.686, 0.168],
+                            [0.272, 0.534, 0.131]])
+    fall_frame = cv2.transform(frame, fall_filter)
+    fall_frame = np.clip(fall_frame, 0, 255)
+    return fall_frame
+
+def apply_filter(filter_types, input_image, blur_intensity=1, brightness=1.0, contrast=50):
+    frame = input_image.copy()
+   
+    for filter_type in filter_types:
+        if filter_type == "Gaussian Blur":
+            frame = apply_gaussian_blur(frame, blur_intensity)
+        elif filter_type == "Sharpen":
+            frame = apply_sharpening_filter(frame)
+        elif filter_type == "Edge Detection":
+            frame = apply_edge_detection(frame)
+        elif filter_type == "Invert":
+            frame = apply_invert_filter(frame)
+        elif filter_type == "Brightness/Contrast":
+            frame = adjust_brightness_contrast(frame, alpha=brightness, beta=contrast)
+        elif filter_type == "Grayscale":
+            frame = apply_grayscale_filter(frame)
+        elif filter_type == "Sepia":
+            frame = apply_sepia_filter(frame)
+        elif filter_type == "Sonbahar":
+            frame = apply_fall_filter(frame)
+    
+    return frame
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Gelişmiş Web Kameradan Canlı Filtreleme")
+
+    
+    filter_types = gr.CheckboxGroup(
+        label="Filtre Seçin",
+        choices=["Gaussian Blur", "Sharpen", "Edge Detection", "Invert", "Brightness/Contrast", "Grayscale", "Sepia", "Sonbahar"],
+        value=["Gaussian Blur"]
+    )
+
+    blur_intensity = gr.Slider(label="Gaussian Blur Yoğunluğu", minimum=1, maximum=10, step=1, value=1)
+    brightness = gr.Slider(label="Parlaklık", minimum=0.5, maximum=2.0, step=0.1, value=1.0)
+    contrast = gr.Slider(label="Kontrast", minimum=0, maximum=100, step=10, value=50)
+
+    input_image = gr.Image(label="Resim Yükle", type="numpy")
+
+    output_image = gr.Image(label="Filtre Uygulandı")
+
+    apply_button = gr.Button("Filtreyi Uygula")
+
+    apply_button.click(fn=apply_filter, inputs=[filter_types, input_image, blur_intensity, brightness, contrast], outputs=output_image)
+
+
+demo.launch()

requirements.txt

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+opencv-python
+numpy
+gradio