Upload gradio-filtreleme-huggıng-face.py

gradio-filtreleme-huggıng-face.py

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+import cv2
+import numpy as np
+import gradio as gr
+
+# Farklı filtre fonksiyonları
+def apply_gaussian_blur(frame):
+    return cv2.GaussianBlur(frame, (15, 15), 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=50):
+    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_image = cv2.transform(frame, sepia_filter)
+    return np.clip(sepia_image, 0, 255).astype(np.uint8)
+
+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_image = cv2.transform(frame, fall_filter)
+    return np.clip(fall_image, 0, 255).astype(np.uint8)
+
+def apply_emboss_filter(frame):
+    emboss_filter = np.array([[-2, -1, 0],
+                              [-1, 1, 1],
+                              [0, 1, 2]])
+    embossed_image = cv2.filter2D(frame, -1, emboss_filter)
+    return np.clip(embossed_image + 128, 0, 255).astype(np.uint8)
+
+
+def Engin_deneme_fitresi(frame):
+    deneme=np.array([[0.202, 0.504, 0.101],
+                             [0.309, 0.606, 0.118], 
+                             [0.393, 0.709, 0.109]])
+    return frame
+
+
+def karnel_sharpening(frame):
+    karnel=np.array(1/9*[[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
+    return frame
+
+
+
+
+
+# Filtre uygulama fonksiyonu
+def apply_filter(filter_type, input_image=None):
+    if input_image is None:
+        return "Görüntü yüklenmedi."
+    
+    frame = input_image
+
+    # Filtre türüne göre işlemi seç
+    if filter_type == "Gaussian Blur":
+        return apply_gaussian_blur(frame)
+    elif filter_type == "Sharpen":
+        return apply_sharpening_filter(frame)
+    elif filter_type == "Edge Detection":
+        return apply_edge_detection(frame)
+    elif filter_type == "Invert":
+        return apply_invert_filter(frame)
+    elif filter_type == "Brightness":
+        return adjust_brightness_contrast(frame, alpha=1.0, beta=50)
+    elif filter_type == "Grayscale":
+        return apply_grayscale_filter(frame)
+    elif filter_type == "Sepia":
+        return apply_sepia_filter(frame)
+    elif filter_type == "Sonbahar":
+        return apply_fall_filter(frame)
+    elif filter_type == "Emboss Filter":
+        return apply_emboss_filter(frame)
+    elif filter_type == "Engin Deneme":
+        return Engin_deneme_fitresi(frame)
+    elif filter_type == "Karnel Sharpening":
+        return karnel_sharpening(frame)
+
+    else:
+        return frame
+
+# Gradio arayüzü
+with gr.Blocks() as demo:
+    gr.Markdown("# Web Kameradan Canlı Filtreleme")
+
+    # Filtre seçenekleri
+    filter_type = gr.Dropdown(
+        label="Filtre Seçin",
+        choices=["Gaussian Blur", "Sharpen", "Edge Detection", "Invert", "Brightness", "Grayscale", "Sepia", "Sonbahar", "Emboss Filter", "Engin Deneme", "Karnel Sharpening"],
+        value="Gaussian Blur"
+    )
+
+    # Görüntü yükleme alanı
+    input_image = gr.Image(label="Resim Yükle", type="numpy")
+
+    # Çıktı için görüntü
+    output_image = gr.Image(label="Filtre Uygulandı")
+
+    # Filtre uygula butonu
+    apply_button = gr.Button("Filtreyi Uygula")
+
+    # Butona tıklanınca filtre uygulama fonksiyonu
+    apply_button.click(fn=apply_filter, inputs=[filter_type, input_image], outputs=output_image)
+
+# Gradio arayüzünü başlat
+demo.launch()