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import cv2
import numpy as np
import gradio as gr
# Türler at fonksiyonlar
def ag_blur(frame):
return cv2.GaussianBlur(frame, (15, 15), 0)
def bileteral (frame):
return cv2.bilateralFilter(frame, 9, 75, 75)
def median_blur(frame):
return cv2.medianBlur(frame, 5)
def sharpen(frame):
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
return cv2.filter2D(frame, -1, kernel)
def ad_detection(frame):
return cv2.Canny(frame, 100, 200)
def brightness(frame):
return cv2.convertScaleAbs(frame, alpha=1.0, beta=50)
def invert(frame):
return cv2.bitwise_not(frame)
def emboss(frame):
kernel = np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]])
return cv2.filter2D(frame, -1, kernel)
def ab_contrast(frame, alpha=1.0, beta=50):
return cv2.convertScaleAbs(frame, alpha=alpha, beta=beta)
def ag_filter(frame):
return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
def 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):
frame = input_image if input_image is not None else None
if frame is None:
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
cap.release()
if not ret:
return "Kameradan görüntü alınamadı"
if filter_type == "Gaussian Blur":
return ag_blur(frame)
elif filter_type == "Median Blur":
return median_blur(frame)
elif filter_type == "Brightness":
return brightness(frame)
elif filter_type == "Bileteral Blur":
return bileteral(frame)
elif filter_type == "Sharpen":
return sharpen(frame)
elif filter_type == "Edge Detection":
return ad_detection(frame)
elif filter_type == "Emboss":
return emboss(frame)
elif filter_type == "Invert":
return invert(frame)
elif filter_type == "Contrast":
return ab_contrast(frame, alpha=1.0, beta=50)
elif filter_type == "Grayscale":
return ag_filter(frame)
elif filter_type == "Sepia":
return sepia_filter(frame)
elif filter_type == "Sonbahar":
return apply_fall_filter(frame)
# Gradio
with gr.Blocks() as demo:
gr.Markdown("Canlı Kamera ve Resim Yükleyerek Filtre Uygulaması ")
# Dropdownd filtre seçimi
filter_type = gr.Dropdown(
label="1. Filtreyi Seçin",
choices=["Gaussian Blur","Brightness", "Median Blur", "Bileteral Blur","Emboss", "Sharpen", "Edge Detection", "Invert", "Contrast", "Grayscale", "Sepia", "Sonbahar"],
value="Gaussian Blur"
)
# İkinci filtre seçimi
filter_type2 = gr.Dropdown(
label="2. Filtreyi Seçin",
choices=["Gaussian Blur","Brightness","Median Blur", "Bileteral Blur" ,"Emboss", "Sharpen", "Edge Detection", "Invert", "Contrast", "Grayscale", "Sepia", "Sonbahar"],
value="Gaussian Blur"
)
# Girdi resmi
input_image = gr.Image(label="Resim Yükle", type="numpy")
# Çıktı resimleri
output_image = gr.Image(label="1. Filtre Uygulandı")
output_image2 = gr.Image(label="2. Filtre Uygulandı")
# Seçilen filtre türlerini gösteren metin alanları
selected_filter_text = gr.Textbox(label="Seçilen Filtre 1", interactive=False)
selected_filter_text2 = gr.Textbox(label="Seçilen Filtre 2", interactive=False)
# Mesaj alanı
message = gr.Markdown("")
message2 = gr.Markdown("")
# Filtre uygula butonu
apply_button = gr.Button("Filtreleri Uygula")
# Butona tıklanınca filtre uygulama fonksiyonu
def update_output(f1, f2, img):
out1 = apply_filter(f1, img)
out2 = apply_filter(f2, img)
# Mesajı güncelle
msg = f"Filtreler uygulandı. Teşekkür ederim "
msg2 = f"Gökay GÖKÇE"
return out1, out2, f1, f2, msg, msg2
apply_button.click(
update_output,
inputs=[filter_type, filter_type2, input_image],
outputs=[output_image, output_image2, selected_filter_text, selected_filter_text2, message, message2]
)
demo.launch()