import cv2
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt

color_ranges = [
    ([100, 150, 0], [140, 255, 255]),  # Blue range
    ([35, 100, 100], [85, 255, 255])   # Green range
]


# 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)

# 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 == "Random Colorful Dots":
        return apply_porcelain_splatter_effect(frame)
    elif filter_type == "Picasso":
        return split_and_shuffle_image(frame)




def apply_porcelain_splatter_effect(image):
    # Assume 'image' is an RGB NumPy array
    # Get image dimensions
    height, width, _ = image.shape

    # Create a blank layer for the porcelain splatter effect
    splatter_layer = np.zeros((height, width, 3), dtype=np.uint8)

    # Define colors for splatter pattern
    colors = [
        (255, 140, 0),   # orange
        (0, 128, 0),     # green
        (0, 0, 255),     # red
        (70, 130, 180)   # matte blue
    ]

    # Add random splatter patterns
    for _ in range(1000):
        # Random position for splatter
        x, y = np.random.randint(0, width), np.random.randint(0, height)
        # Random color from defined palette
        color = colors[np.random.randint(0, len(colors))]
        # Random radius for splatter
        radius = np.random.randint(1, 10)
        # Draw a small circle on the splatter layer
        cv2.circle(splatter_layer, (x, y), radius, color, -1)

    # Blend the splatter layer with the original image to get the porcelain effect
    alpha = 0.4  # transparency for splatter layer
    porcelain_image = cv2.addWeighted(image, 1 - alpha, splatter_layer, alpha, 0)

    return porcelain_image

def split_and_shuffle_image(image):
    # Determine the height and width of each piece
    h, w, _ = image.shape
    h_split, w_split = h // 3, w // 3
    
    # Split the image into 9 pieces
    pieces = []
    for i in range(3):
        for j in range(3):
            piece = image[i * h_split:(i + 1) * h_split, j * w_split:(j + 1) * w_split]
            pieces.append(piece)
    
    # Shuffle the pieces
    np.random.shuffle(pieces)
    
    # Reconstruct the shuffled image
    shuffled_image = np.zeros_like(image)
    idx = 0
    for i in range(3):
        for j in range(3):
            shuffled_image[i * h_split:(i + 1) * h_split, j * w_split:(j + 1) * w_split] = pieces[idx]
            idx += 1
            
    return shuffled_image

    

     

# Gradio arayüzü
with gr.Blocks() as demo:
    gr.Markdown("# Image Proccessing Exercise 01 - Tech Istanbul")
    gr.Markdown("# I would like to thank to Murat hoca & Tech Istanbul for teaching us image processing ")
    gr.Markdown("# I added 'Picasso' and 'Random Colorful Dots' filters to the options. They are at the top ")

    # Filtre seçenekleri
    filter_type = gr.Dropdown(
        label="Filtre Seçin",
        choices=["Picasso","Random Colorful Dots","Gaussian Blur", "Sharpen", "Edge Detection", "Invert", "Brightness", "Grayscale", "Sepia", "Sonbahar"],
        value="Picasso"
    )

    # 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()