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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() |