Update app.py

app.py

@@ -1,245 +1,115 @@
 import gradio as gr
+import cv2
 import numpy as np
-from PIL import Image, ImageOps, ImageEnhance, ImageFilter
-from skimage.restoration import denoise_nl_means, estimate_sigma
-import io
-
-def process_image(
-    img, 
-    denoise_strength, 
-    sharpen_strength, 
-    gamma, 
-    brightness, 
-    contrast, 
-    saturation
-):
-    """
-    업로드된 이미지를 파라미터(노이즈 제거, 샤프닝, 감마 보정, 밝기, 대비, 채도)에 따라
-    최종적으로 흑백 사진으로 변환하여 반환합니다.
-    """
-    # 혹시 모를 Alpha 채널 문제 방지 (4채널 -> 3채널)
-    img = img.convert("RGB")  # RGBA인 경우에도 RGB로 강제 변환
-
-    # Pillow Image -> Numpy array 변환
-    img_np = np.array(img)
-
-    # ================ 1. 노이즈 제거 ================
-    # 이미지가 RGB(3차원)인지, 흑백(2차원)인지 판별
-    if len(img_np.shape) == 3:
-        # RGB 이미지의 경우 channel_axis=-1
-        sigma_est = np.mean(estimate_sigma(img_np, channel_axis=-1))
-        denoised = denoise_nl_means(
-            img_np,
-            h=denoise_strength * sigma_est,
-            patch_size=5,
-            patch_distance=3,
-            channel_axis=-1,  
-            fast_mode=True
-        )
-    else:
-        # 흑백(2차원) 이미지인 경우 channel_axis=None
-        sigma_est = np.mean(estimate_sigma(img_np, channel_axis=None))
-        denoised = denoise_nl_means(
-            img_np,
-            h=denoise_strength * sigma_est,
-            patch_size=5,
-            patch_distance=3,
-            channel_axis=None,  
-            fast_mode=True
-        )
-
-    # denoise 결과는 [0,1] 범위이므로, 다시 [0,255]로 변환
-    denoised = np.clip(denoised, 0, 1)
-    denoised = (denoised * 255).astype(np.uint8)
-    denoised_img = Image.fromarray(denoised)
-
-    # ================ 2. 샤프닝 ================
-    enhancer_sharpness = ImageEnhance.Sharpness(denoised_img)
-    sharpened_img = enhancer_sharpness.enhance(sharpen_strength)
-
-    # ================ 3. 감마 보정 ================
-    gamma_np = np.array(sharpened_img).astype(np.float32) / 255.0
-    gamma_corrected = np.power(gamma_np, 1.0 / gamma)
-    gamma_corrected = (gamma_corrected * 255).astype(np.uint8)
-    gamma_corrected_img = Image.fromarray(gamma_corrected)
-
-    # ================ 4. 밝기 ================
-    enhancer_brightness = ImageEnhance.Brightness(gamma_corrected_img)
-    bright_img = enhancer_brightness.enhance(brightness)
-
-    # ================ 5. 대비 ================
-    enhancer_contrast = ImageEnhance.Contrast(bright_img)
-    contrast_img = enhancer_contrast.enhance(contrast)
-
-    # ================ 6. 채도 ================
-    enhancer_color = ImageEnhance.Color(contrast_img)
-    saturated_img = enhancer_color.enhance(saturation)
-
-    # ================ 7. 최종 흑백 변환 ================
-    final_img = saturated_img.convert("L")
-
-    return final_img
-
-def generate_output(
-    input_image, 
-    denoise_strength, 
-    sharpen_strength, 
-    gamma, 
-    brightness, 
-    contrast, 
-    saturation
-):
-    """
-    Gradio에서 호출되는 함수로, 변환된 흑백 이미지를 
-    바로 표시할 이미지와 다운로드용 파일로 함께 반환합니다.
-    """
-    if input_image is None:
-        return None, None
+from PIL import Image
+
+def process_image(image, 
+                  convert_bw, 
+                  denoise, 
+                  sharpen, 
+                  gamma, 
+                  brightness, 
+                  contrast, 
+                  saturation):
+    # Convert PIL Image to OpenCV format
+    img = np.array(image)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    
+    # 노이즈 제거
+    if denoise:
+        img = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
+    
+    # 샤프닝
+    if sharpen:
+        kernel = np.array([[0, -1, 0],
+                           [-1, 5,-1],
+                           [0, -1, 0]])
+        img = cv2.filter2D(img, -1, kernel)
+    
+    # 감마 보정
+    if gamma != 1.0:
+        invGamma = 1.0 / gamma
+        table = np.array([((i / 255.0) ** invGamma) * 255
+                          for i in np.arange(256)]).astype("uint8")
+        img = cv2.LUT(img, table)
     
-    # 변환된 이미지
-    transformed = process_image(
-        input_image, 
-        denoise_strength, 
-        sharpen_strength, 
-        gamma, 
-        brightness, 
-        contrast, 
-        saturation
-    )
+    # 밝기 조정
+    if brightness != 0:
+        img = cv2.convertScaleAbs(img, alpha=1, beta=brightness)
     
-    # 다운로드 가능한 JPG 파일 생성
-    with io.BytesIO() as output:
-        transformed.save(output, format="JPEG")
-        contents = output.getvalue()
+    # 대비 조정
+    if contrast != 1.0:
+        img = cv2.convertScaleAbs(img, alpha=contrast, beta=0)
     
-    # Gradio의 File 형식: (파일 바이트, MIME 타입, 다운로드 시 표시될 파일명)
-    file_data = (contents, "image/jpeg", "transformed.jpg")
+    # 채도 조정
+    if saturation != 1.0:
+        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.float32)
+        hsv[...,1] = hsv[...,1] * saturation
+        hsv[...,1] = np.clip(hsv[...,1], 0, 255)
+        img = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2BGR)
+    
+    # 흑백 변환
+    if convert_bw:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+    
+    # 변환된 이미지를 PIL 형식으로 변환
+    transformed_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    transformed_image = Image.fromarray(transformed_image)
+    
+    return transformed_image
 
-    # 변환된 이미지를 그리디오 Image로 보여주고, 다운로드 파일도 함께 반환
-    return transformed, file_data
+def compare_images(original, transformed):
+    return transformed
 
-def main():
+# Gradio 인터페이스 구성
+def create_interface():
     with gr.Blocks() as demo:
-        gr.Markdown("## 흑백 분위기 사진 변환 데모")
-
+        gr.Markdown("## 이미지 처리 애플리케이션")
+        
         with gr.Row():
-            input_image = gr.Image(
-                label="원본 이미지 업로드",
-                type="pil"
-            )
-
-        # 슬라이더들
-        denoise_slider = gr.Slider(
-            0.0, 3.0, step=0.1, value=1.0, 
-            label="노이즈 제거 세기 (denoise_strength)"
-        )
-        sharpen_slider = gr.Slider(
-            0.0, 3.0, step=0.1, value=1.0, 
-            label="샤프닝 (sharpen_strength)"
-        )
-        gamma_slider = gr.Slider(
-            0.1, 3.0, step=0.1, value=1.0, 
-            label="감마 (gamma)"
-        )
-        brightness_slider = gr.Slider(
-            0.0, 2.0, step=0.1, value=1.0, 
-            label="밝기 (brightness)"
-        )
-        contrast_slider = gr.Slider(
-            0.0, 2.0, step=0.1, value=1.0, 
-            label="대비 (contrast)"
+            with gr.Column():
+                input_image = gr.Image(type="pil", label="원본 이미지 업로드")
+                convert_bw = gr.Checkbox(label="흑백으로 변환", value=True)
+                denoise = gr.Checkbox(label="노이즈 제거", value=False)
+                sharpen = gr.Checkbox(label="샤프닝", value=False)
+                gamma = gr.Slider(label="감마 보정", minimum=0.1, maximum=3.0, step=0.1, value=1.0)
+                brightness = gr.Slider(label="밝기 조정", minimum=-100, maximum=100, step=1, value=0)
+                contrast = gr.Slider(label="대비 조정", minimum=0.5, maximum=1.5, step=0.1, value=1.0)
+                saturation = gr.Slider(label="채도 조정", minimum=0.0, maximum=2.0, step=0.1, value=1.0)
+                submit = gr.Button("변환하기")
+            
+            with gr.Column():
+                output_image = gr.Image(type="pil", label="변환된 이미지")
+                download = gr.Button("JPG로 다운로드")
+        
+        submit.click(
+            fn=process_image, 
+            inputs=[input_image, convert_bw, denoise, sharpen, gamma, brightness, contrast, saturation],
+            outputs=output_image
         )
-        saturation_slider = gr.Slider(
-            0.0, 2.0, step=0.1, value=1.0, 
-            label="채도 (saturation)"
+        
+        download.click(
+            fn=lambda img: img.save("transformed_image.jpg"),
+            inputs=output_image,
+            outputs=None
         )
-
-        # 결과(변환된 이미지, 다운로드 버튼)
+        
+        gr.Markdown("""
+        ### 이미지 비교
+        슬라이더를 사용하여 원본 이미지와 변환된 이미지를 비교할 수 있습니다.
+        """)
+        
         with gr.Row():
-            output_image = gr.Image(
-                label="변환된 이미지", 
-                height=300
-            )
-            download_file = gr.File(
-                label="JPG 다운로드"
-            )
-
-        # 변환 실행 버튼
-        btn = gr.Button("변환 실행")
-        btn.click(
-            fn=generate_output,
-            inputs=[
-                input_image,
-                denoise_slider,
-                sharpen_slider,
-                gamma_slider,
-                brightness_slider,
-                contrast_slider,
-                saturation_slider
-            ],
-            outputs=[output_image, download_file]
-        )
+            original_display = gr.Image(type="pil", label="원본 이미지", interactive=False)
+            transformed_display = gr.Image(type="pil", label="변환된 이미지", interactive=False)
         
-        # 원본 vs 결과 비교
-        compare_output = gr.Image(
-            type="pil", 
-            label="원본 vs 변환본 비교 결과",
-            height=300
-        )
-        compare_btn = gr.Button("원본 vs 변환본 비교")
-
-        def compare_images(
-            input_image, 
-            denoise_strength, 
-            sharpen_strength, 
-            gamma, 
-            brightness, 
-            contrast, 
-            saturation
-        ):
-            if input_image is None:
-                return None
-            # 변환
-            transformed = process_image(
-                input_image, 
-                denoise_strength, 
-                sharpen_strength, 
-                gamma, 
-                brightness, 
-                contrast, 
-                saturation
-            )
-            # 두 이미지를 가로로 합침
-            input_image_rgb = input_image.convert("RGB")
-            transformed_rgb = transformed.convert("RGB")
-
-            input_w, input_h = input_image_rgb.size
-            transformed_w, transformed_h = transformed_rgb.size
-            new_w = input_w + transformed_w
-            new_h = max(input_h, transformed_h)
-
-            new_image = Image.new("RGB", (new_w, new_h))
-            new_image.paste(input_image_rgb, (0, 0))
-            new_image.paste(transformed_rgb, (input_w, 0))
-
-            return new_image
-
-        compare_btn.click(
-            fn=compare_images,
-            inputs=[
-                input_image,
-                denoise_slider,
-                sharpen_slider,
-                gamma_slider,
-                brightness_slider,
-                contrast_slider,
-                saturation_slider
-            ],
-            outputs=[compare_output]
-        )
-
+        input_image.change(lambda img: (img, process_image(img, convert_bw.value, denoise.value, sharpen.value, gamma.value, brightness.value, contrast.value, saturation.value)), 
+                           inputs=input_image, 
+                           outputs=[original_display, transformed_display])
+    
     return demo
 
 if __name__ == "__main__":
-    demo_app = main()
-    demo_app.launch()
+    interface = create_interface()
+    interface.launch()