app.py

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, -1, 0],
                       [ -1,  1, 1],
                       [  0,  1, 2]])
    return cv2.filter2D(frame, -1, kernel)

def apply_cartoon_filter(frame):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray = cv2.medianBlur(gray, 5)
    edges = cv2.adaptiveThreshold(gray, 255,
                                   cv2.ADAPTIVE_THRESH_MEAN_C,
                                   cv2.THRESH_BINARY, 9, 9)
    color = cv2.bilateralFilter(frame, 9, 300, 300)
    return cv2.bitwise_and(color, color, mask=edges)

def apply_threshold(frame, thresh_value=127):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    _, thresh = cv2.threshold(gray, thresh_value, 255, cv2.THRESH_BINARY)
    return thresh

def apply_blurred_edges(frame):
    blurred = cv2.GaussianBlur(frame, (21, 21), 0)
    edges = cv2.Canny(frame, 100, 200)
    return cv2.bitwise_and(blurred, blurred, mask=edges)

# Filtre uygulama fonksiyonu
def apply_filter(filter_type, input_image):
    if input_image is None:
        return "Resim yüklenmedi"

    frame = input_image

    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 == "Threshold":
        return apply_threshold(frame)
    elif filter_type == "Blurred Edges":
        return apply_blurred_edges(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", "Cartoon", "Threshold", "Blurred Edges"],
        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()