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()