Update app.py

app.py

@@ -1,216 +1,29 @@
-import os
-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()
+import os
+import cv2
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import PIL
+import numpy as np
+
+img_colorization = pipeline(Tasks.image_colorization, model='iic/cv_ddcolor_image-colorization')
+img_path = 'input.png'
+##result = img_colorization(img_path)
+##cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
+def color(image):
+    output = img_colorization(image[...,::-1])
+    result = output[OutputKeys.OUTPUT_IMG].astype(np.uint8)
+    result = result[...,::-1]
+    print('infer finished!')
+    return result
+
+
+title = "老照片修复"
+description = "上传图片，达到老照片修复"
+examples = [['./input.png'],]
+
+demo = gr.Interface(fn=color,inputs="image",outputs="image",examples=examples,title=title,description=description)
+
+if __name__ == "__main__":
+    demo.launch(share=True)