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]]) return cv2.transform(frame, sepia_filter) 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]]) return cv2.transform(frame, fall_filter) def apply_emboss_filter(frame): kernel = np.array([[2, 0, 0], [0, -1, 0], [0, 0, -2]]) return cv2.filter2D(frame, -1, kernel) def apply_cartoon_filter(frame): # Renkleri yumuşatmak için bilateral filtre color = cv2.bilateralFilter(frame, d=9, sigmaColor=75, sigmaSpace=75) # Kenar tespiti için gri tonlamalı görüntü ve adaptive threshold kullanımı gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) edges = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9 ) # Kenarları renklere uygula cartoon = cv2.bitwise_and(color, color, mask=edges) return cartoon def apply_motion_blur(frame): kernel_size = 15 kernel = np.zeros((kernel_size, kernel_size)) kernel[int((kernel_size - 1)/2), :] = np.ones(kernel_size) kernel = kernel / kernel_size return cv2.filter2D(frame, -1, kernel) def apply_vignette_filter(frame): # Vignette maskesi oluştur rows, cols = frame.shape[:2] kernel_x = cv2.getGaussianKernel(cols, cols / 2) kernel_y = cv2.getGaussianKernel(rows, rows / 2) kernel = kernel_y * kernel_x.T mask = 255 * kernel / np.max(kernel) vignette = np.zeros_like(frame, dtype=np.float32) # Daha hassas türde işlem yapma for i in range(3): # Her renk kanalı için vignette uygulama vignette[:, :, i] = frame[:, :, i] * mask # Değerleri uint8 formatına dönüştürme vignette = np.clip(vignette, 0, 255).astype(np.uint8) return vignette def apply_pencil_sketch(frame): gray, sketch = cv2.pencilSketch(frame, sigma_s=60, sigma_r=0.07, shade_factor=0.05) return sketch def apply_hdr_filter(frame): return cv2.detailEnhance(frame, sigma_s=12, sigma_r=0.15) def apply_summer_filter(frame): summer_filter = np.array([[0.272, 0.543, 0.131], [0.349, 0.686, 0.168], [0.393, 0.769, 0.189]]) return cv2.transform(frame, summer_filter) def apply_warm_filter(frame): increase_red = cv2.addWeighted(frame, 1, np.array([0, 20, 0]), 0.5, 0) return increase_red def apply_cold_filter(frame): decrease_red = cv2.addWeighted(frame, 1, np.array([0, -20, 0]), 0.5, 0) return decrease_red def apply_solarize_filter(frame): return cv2.bitwise_not(frame) def apply_color_boost(frame): hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) hsv[...,1] = cv2.add(hsv[...,1], 50) return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) def apply_dilation_filter(frame): kernel = np.ones((5, 5), np.uint8) return cv2.dilate(frame, kernel, iterations=1) # Filtre uygulama fonksiyonu def apply_filter(filter_type, input_image=None): if input_image is not None: frame = input_image else: cap = cv2.VideoCapture(0) ret, frame = cap.read() cap.release() if not ret: return "Web kameradan görüntü alınamadı" 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": return apply_emboss_filter(frame) elif filter_type == "Cartoon": return apply_cartoon_filter(frame) elif filter_type == "Motion Blur": return apply_motion_blur(frame) elif filter_type == "Vignette": return apply_vignette_filter(frame) elif filter_type == "Pencil Sketch": return apply_pencil_sketch(frame) elif filter_type == "HDR": return apply_hdr_filter(frame) elif filter_type == "Summer": return apply_summer_filter(frame) elif filter_type == "Warm": return apply_warm_filter(frame) elif filter_type == "Cold": return apply_cold_filter(frame) elif filter_type == "Solarize": return apply_solarize_filter(frame) elif filter_type == "Color Boost": return apply_color_boost(frame) elif filter_type == "Dilation": return apply_dilation_filter(frame) # Gradio arayüzü with gr.Blocks(css=""" .gr-button { width: 80px; /* Buton genişliği */ height: 30px; /* Buton yüksekliği */ font-size: 12px; /* Yazı boyutu */ } .gr-image { width: 400px; /* Sabit genişlik */ height: 300px; /* Sabit yükseklik */ } #image-upload { width: 400px; /* Sabit genişlik */ height: 300px; /* Sabit yükseklik */ } #output-image { width: 400px; /* Sabit genişlik */ height: 300px; /* Sabit yükseklik */ } """) as demo: gr.Markdown("# Resim Filtreleme") # Filtre seçenekleri filter_type = gr.Dropdown( label="Filtre Seçin", choices=["Gaussian Blur", "Sharpen", "Edge Detection", "Invert", "Brightness", "Grayscale", "Sepia", "Sonbahar", "Emboss", "Cartoon", "Motion Blur", "Vignette", "Pencil Sketch", "HDR", "Summer", "Warm", "Cold", "Solarize", "Color Boost", "Dilation"], value="Gaussian Blur" ) with gr.Row(): # 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()