Update requirements.txt

Update requirements.txt

@@ -0,0 +1,2 @@
+opencv-python
+numpy

foto_filter_gradio_app

@@ -1,120 +0,0 @@
-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,-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()