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import gradio as gr
import cv2
import numpy as np
from PIL import Image, ImageEnhance
from gradio_imageslider import ImageSlider
def apply_filter(image, filter_type, intensity):
# PIL ์ด๋ฏธ์ง€๋ฅผ numpy array๋กœ ๋ณ€ํ™˜
image = np.array(image)
# ๊ฐ•๋„๋ฅผ 0.0์—์„œ 1.0 ์‚ฌ์ด๋กœ ์ •๊ทœํ™”
normalized_intensity = intensity / 100.0
if filter_type == "Grayscale":
return convert_to_grayscale(image)
elif filter_type == "Soft Glow":
base_intensity = 0.1
adjusted_intensity = base_intensity + (normalized_intensity * (1 - base_intensity))
gaussian = cv2.GaussianBlur(image, (15, 15), 0)
soft_glow = cv2.addWeighted(image, 1 - adjusted_intensity, gaussian, adjusted_intensity, 0)
return soft_glow
elif filter_type == "Portrait Enhancer":
base_intensity = 0.5
adjusted_intensity = base_intensity + (normalized_intensity * (1 - base_intensity))
image_pil = Image.fromarray(image)
enhancer = ImageEnhance.Sharpness(image_pil)
image_pil = enhancer.enhance(1 + 0.5 * adjusted_intensity)
enhancer = ImageEnhance.Color(image_pil)
image_pil = enhancer.enhance(1 + 0.5 * adjusted_intensity)
enhanced_image = np.array(image_pil)
return enhanced_image
elif filter_type == "Warm Tone":
# ๊ฐ•๋„๋ฅผ 30%๋กœ ์„ค์ • (๋”ฐ๋œปํ•œ ํ†ค ์ ์šฉ)
warm_image = cv2.addWeighted(image, 1.0, np.full(image.shape, (20, 66, 112), dtype=np.uint8), 0.3 * normalized_intensity, 0)
return warm_image
elif filter_type == "Cold Tone":
# ๊ฐ•๋„๋ฅผ 30%๋กœ ์„ค์ • (์ฐจ๊ฐ€์šด ํ†ค ์ ์šฉ)
cold_image = cv2.addWeighted(image, 1.0, np.full(image.shape, (112, 66, 20), dtype=np.uint8), 0.3 * normalized_intensity, 0)
return cold_image
elif filter_type == "High-Key":
# ๊ฐ•๋„๋ฅผ 30%๋กœ ์„ค์ •
high_key = cv2.convertScaleAbs(image, alpha=1.0 + 0.3 * normalized_intensity, beta=20)
return high_key
elif filter_type == "Low-Key":
# ๊ฐ•๋„๋ฅผ 10%๋กœ ์„ค์ •
low_key = cv2.convertScaleAbs(image, alpha=1.0 - 0.1 * normalized_intensity, beta=-10)
return low_key
elif filter_type == "Haze":
# ๊ฐ•๋„๋ฅผ 30%๋กœ ์„ค์ •
haze = cv2.addWeighted(image, 1.0, np.full(image.shape, 255, dtype=np.uint8), 0.3 * normalized_intensity, 0)
return haze
else:
return image
def convert_to_grayscale(image):
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
return cv2.cvtColor(gray_image, cv2.COLOR_GRAY2BGR)
def convert_and_save(image, filter_type, intensity):
image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
filtered_image = apply_filter(image_cv, filter_type, intensity)
# OpenCV ์ด๋ฏธ์ง€๋ฅผ ๋‹ค์‹œ PIL๋กœ ๋ณ€ํ™˜
original_image_pil = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
filtered_image_pil = Image.fromarray(cv2.cvtColor(filtered_image, cv2.COLOR_BGR2RGB))
# ํ•„ํ„ฐ ์ ์šฉ๋œ ์ด๋ฏธ์ง€๋ฅผ JPG๋กœ ์ €์žฅ
output_path = "filtered_image.jpg"
filtered_image_pil.save(output_path)
return original_image_pil, filtered_image_pil, output_path # ์›๋ณธ, ํ•„ํ„ฐ ์ ์šฉ๋œ ์ด๋ฏธ์ง€์™€ ํŒŒ์ผ ๊ฒฝ๋กœ ๋ฐ˜ํ™˜
def get_filter_description(filter_type):
descriptions = {
"Grayscale": "์ด๋ฏธ์ง€๋ฅผ ํ‘๋ฐฑ์œผ๋กœ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค.",
"Soft Glow": "๋ถ€๋“œ๋Ÿฌ์šด ๋น›์„ ์ถ”๊ฐ€ํ•˜์—ฌ ์ด๋ฏธ์ง€๋ฅผ ์€์€ํ•˜๊ฒŒ ๋งŒ๋“ญ๋‹ˆ๋‹ค.",
"Portrait Enhancer": "ํ”ผ๋ถ€ ํ†ค์„ ๊ท ์ผํ•˜๊ฒŒ ํ•˜๊ณ  ์„ ๋ช…๋„๋ฅผ ์กฐ์ ˆํ•˜์—ฌ ์ธ๋ฌผ์„ ๋”์šฑ ๋‹๋ณด์ด๊ฒŒ ๋งŒ๋“ญ๋‹ˆ๋‹ค.",
"Warm Tone": "๋”ฐ๋œปํ•œ ์ƒ‰์กฐ๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ์ด๋ฏธ์ง€์— ์˜จ๊ธฐ๋ฅผ ๋”ํ•ฉ๋‹ˆ๋‹ค.",
"Cold Tone": "์ฐจ๊ฐ€์šด ์ƒ‰์กฐ๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ์ด๋ฏธ์ง€์— ์‹œ์›ํ•จ์„ ๋”ํ•ฉ๋‹ˆ๋‹ค.",
"High-Key": "๋ฐ๊ณ  ํ™”์‚ฌํ•œ ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ค์–ด๋ƒ…๋‹ˆ๋‹ค.",
"Low-Key": "์–ด๋‘์šด ํ†ค์„ ๊ฐ•์กฐํ•˜์—ฌ ๋ถ„์œ„๊ธฐ ์žˆ๋Š” ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ญ๋‹ˆ๋‹ค.",
"Haze": "๋ถ€๋“œ๋Ÿฝ๊ณ  ํ๋ฆฟํ•œ ํšจ๊ณผ๋ฅผ ์ถ”๊ฐ€ํ•˜์—ฌ ๋ชฝํ™˜์ ์ธ ์ด๋ฏธ์ง€๋ฅผ ๋งŒ๋“ญ๋‹ˆ๋‹ค."
}
return descriptions.get(filter_type, "")
with gr.Blocks() as iface:
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="์ด๋ฏธ์ง€ ์—…๋กœ๋“œ")
filter_input = gr.Radio(
["Grayscale", "Soft Glow", "Portrait Enhancer", "Warm Tone", "Cold Tone", "High-Key", "Low-Key", "Haze"],
label="ํ•„ํ„ฐ ์„ ํƒ",
value="Soft Glow"
)
intensity_slider = gr.Slider(1, 100, value=50, label="ํ•„ํ„ฐ ๊ฐ•๋„")
description_output = gr.Markdown(get_filter_description("Soft Glow"))
with gr.Column():
slider_output = ImageSlider(label="Before and After", type="pil")
download_link = gr.File(label="Download Filtered Image")
filter_input.change(fn=get_filter_description, inputs=filter_input, outputs=description_output)
process_button = gr.Button("ํ•„ํ„ฐ ์ ์šฉ")
process_button.click(
fn=convert_and_save,
inputs=[image_input, filter_input, intensity_slider],
outputs=[slider_output, download_link]
)
iface.title = "์ธ๋ฌผ ์‚ฌ์ง„์— ์ตœ์ ํ™”๋œ ํ•„ํ„ฐ"
iface.description = "์ธ๋ฌผ ์‚ฌ์ง„์— ์ตœ์ ํ™”๋œ ๋‹ค์–‘ํ•œ ํ•„ํ„ฐ๋ฅผ ์ ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค."
iface.launch()