Create app.py

app.py

@@ -0,0 +1,218 @@
+import os
+os.system('pip install modelscope')
+os.system('pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html')
+import json
+from PIL import Image
+from skimage import io
+import gradio as gr
+from modelscope_studio import encode_image, decode_image, call_demo_service
+
+
+yes, no = "是", "否"
+
+def get_size(h, w, max_size=720):
+    if min(h, w) > max_size:
+        if h > w:
+            h, w = int(max_size * h / w), max_size
+        else:
+            h, w = max_size, int(max_size * w / h)
+    return h, w
+
+
+def inference(img: Image, colorization_option: str, image_denoise_option: str, color_enhance_option: str) -> Image:
+    if img is None:
+        return None
+    w, h = img.size
+    h, w = get_size(h, w, 512)
+    img = img.resize((w, h))
+    
+    input_url = encode_image(img)
+    res_url = input_url
+    
+    # image-denoising (optional)
+    if image_denoise_option == yes:
+        data = {
+            "task": "image-denoising",
+            "inputs": [
+                res_url
+            ],
+            "parameters":{},
+            "urlPaths": {
+                "inUrls": [
+                    {
+                        "value": res_url,
+                        "fileType": "png",
+                        "type": "image",
+                        "displayType": "ImgUploader",
+                        "validator": {
+                            "accept": "*.jpeg,*.jpg,*.png",
+                            "max_resolution": "5000*5000",
+                            "max_size": "10m"
+                        },
+                        "name": "",
+                        "title": ""
+                    }
+                ],
+                "outUrls": [
+                    {
+                        "outputKey": "output_img",
+                        "type": "image"
+                    }
+                ]
+            }
+        }
+        result = call_demo_service(
+            path='damo', name='cv_nafnet_image-denoise_sidd', data=json.dumps(data))
+        print(f"image-denoising result: {result}")
+        res_url = result['data']['output_img']
+
+    # image-colorization (optional)
+    if colorization_option == yes:
+        data = {
+            "task": "image-colorization",
+            "inputs": [
+                res_url
+            ],
+            "parameters":{},
+            "urlPaths": {
+                "inUrls": [
+                    {
+                        "value": res_url,
+                        "fileType": "png",
+                        "type": "image",
+                        "displayType": "ImgUploader",
+                        "validator": {
+                            "accept": "*.jpeg,*.jpg,*.png",
+                            "max_size": "10m",
+                            "max_resolution": "5000*5000",
+                        },
+                        "name": "",
+                        "title": ""
+                    }
+                ],
+                "outUrls": [
+                    {
+                        "outputKey": "output_img",
+                        "type": "image"
+                    }
+                ]
+            }
+        }
+        result = call_demo_service(
+            path='damo', name='cv_ddcolor_image-colorization', data=json.dumps(data))
+        print(f"image-colorization result: {result}")
+        res_url = result['data']['output_img']
+
+
+    # image-portrait-enhancement
+    data = {
+        "task": "image-portrait-enhancement",
+        "inputs": [
+            res_url
+        ],
+        "parameters":{},
+        "urlPaths": {
+            "inUrls": [
+                {
+                    "value": res_url,
+                    "fileType": "png",
+                    "type": "image",
+                    "displayType": "ImgUploader",
+                    "validator": {
+                        "accept": "*.jpeg,*.jpg,*.png",
+                        "max_size": "10M",
+                        "max_resolution": "2000*2000",
+                    },
+                    "name": "",
+                    "title": ""
+                }
+            ],
+            "outUrls": [
+                {
+                    "outputKey": "output_img",
+                    "type": "image"
+                }
+            ]
+        }
+    }
+    result = call_demo_service(
+        path='damo', name='cv_gpen_image-portrait-enhancement', data=json.dumps(data))
+    print(f"image-portrait-enhancement result: {result}")
+    res_url = result['data']['output_img']
+
+    # image-color-enhancement (optional)
+    if color_enhance_option == yes:
+        data = {
+            "task": "image-color-enhancement",
+            "inputs": [
+                res_url
+            ],
+            "parameters":{},
+            "urlPaths": {
+                "inUrls": [
+                    {
+                        "value": res_url,
+                        "fileType": "png",
+                        "type": "image",
+                        "displayType": "ImgUploader",
+                        "validator": {
+                            "accept": "*.jpeg,*.jpg,*.png",
+                            "max_size": "10m",
+                            "max_resolution": "5000*5000",
+                        },
+                        "name": "",
+                        "title": ""
+                    }
+                ],
+                "outUrls": [
+                    {
+                        "outputKey": "output_img",
+                        "type": "image"
+                    }
+                ]
+            }
+        }
+        result = call_demo_service(
+            path='damo', name='cv_csrnet_image-color-enhance-models', data=json.dumps(data))
+        print(f"image-color-enhancement result: {result}")
+        res_url = result['data']['output_img']
+
+
+    res_img = decode_image(res_url)
+
+    return res_img
+
+
+title = "AI老照片修复"
+description = '''
+输入一张老照片，点击一键修复，就能获得由AI完成画质增强、智能上色等处理后的彩色照片！还等什么呢？快让相册里的老照片坐上时光机吧~
+'''
+examples = [[os.path.dirname(__file__) + './images/input1.jpg'], 
+            [os.path.dirname(__file__) + './images/input2.jpg'], 
+            [os.path.dirname(__file__) + './images/input3.jpg'], 
+            [os.path.dirname(__file__) + './images/input4.jpg'],
+            [os.path.dirname(__file__) + './images/input5.jpg']]
+
+css_style = "#overview {margin: auto;max-width: 600px; max-height: 400px; width: 100%;}"
+
+with gr.Blocks(title=title, css=css_style) as demo:
+    gr.HTML('''
+        <div style="text-align: center; max-width: 720px; margin: 0 auto;">
+            <img id="overview" alt="overview" src="https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/public/ModelScope/studio_old_photo_restoration/overview_long.gif" />
+        </div>
+      ''')
+    gr.Markdown(description)
+    with gr.Row():
+        with gr.Column(scale=2):
+            img_input = gr.components.Image(label="图片", type="pil")
+            colorization_option = gr.components.Radio(label="重新上色", choices=[yes, no], value=yes)
+            image_denoise_option = gr.components.Radio(label="应用图像去噪（存在细节损失风险）", choices=[yes, no], value=no)
+            color_enhance_option = gr.components.Radio(label="应用色彩增强（存在罕见色调风险）", choices=[yes, no], value=no)
+            btn = gr.Button("一键修复")
+        with gr.Column(scale=3):
+            img_output = gr.components.Image(label="图片", type="pil").style(height=600)
+    inputs = [img_input, colorization_option, image_denoise_option, color_enhance_option]
+    btn.click(fn=inference, inputs=inputs, outputs=img_output)
+    gr.Examples(examples, inputs=img_input)
+
+demo.launch()