Update app.py

app.py

@@ -1,127 +1,127 @@
-import gradio as gr
-import numpy as np
-import cv2
-from fastapi import FastAPI, Request, Response
-from src.body import Body
-
-body_estimation = Body('model/body_pose_model.pth')
-
-def pil2cv(image):
-    ''' PIL型 -> OpenCV型 '''
-    new_image = np.array(image, dtype=np.uint8)
-    if new_image.ndim == 2:  # モノクロ
-        pass
-    elif new_image.shape[2] == 3:  # カラー
-        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
-    elif new_image.shape[2] == 4:  # 透過
-        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
-    return new_image
-
-with open("static/poseEditor.js", "r") as f:
-    file_contents = f.read()
-
-app = FastAPI()
-
-@app.middleware("http")
-async def some_fastapi_middleware(request: Request, call_next):
-    path = request.scope['path']  # get the request route
-    response = await call_next(request)
-    
-    if path == "/":
-        response_body = ""
-        async for chunk in response.body_iterator:
-            response_body += chunk.decode()
-
-        some_javascript = f"""
-        <script type="text/javascript" defer>
-{file_contents}
-        </script>
-        """
-
-        response_body = response_body.replace("</body>", some_javascript + "</body>")
-
-        del response.headers["content-length"]
-
-        return Response(
-            content=response_body,
-            status_code=response.status_code, 
-            headers=dict(response.headers),
-            media_type=response.media_type
-        )
-
-    return response
-
-# make cndidate to json
-def candidate_to_json_string(arr):
-    a = [f'[{x:.2f}, {y:.2f}]' for x, y, *_ in arr]
-    return '[' + ', '.join(a) + ']'
-
-# make subset to json
-def subset_to_json_string(arr):
-    arr_str = ','.join(['[' + ','.join([f'{num:.2f}' for num in row]) + ']' for row in arr])
-    return '[' + arr_str + ']'
-
-def estimate_body(source):
-    if source == None:
-      return None
-
-    candidate, subset = body_estimation(pil2cv(source))
-    return "{ \"candidate\": " + candidate_to_json_string(candidate) + ", \"subset\": " + subset_to_json_string(subset) + " }"
-    
-def image_changed(image):
-  if (image == None):
-    return {}, 512, 512
-  json = estimate_body(image)
-  return json, image.width, image.height
-
-html_text = f"""
-    <canvas id="canvas" width="512" height="512"></canvas>
-    <script type="text/javascript" defer>{file_contents}</script>
-    """
-
-with gr.Blocks() as demo:
-  gr.Markdown("""### Usage
-
-Choose one of the following methods to edit the pose:
-
-| Style            | Description                                                                               |
-| -----------------| ----------------------------------------------------------------------------------------- |
-| Pose recognition | Upload an image and click "Start edit".                                               |
-| Input json       | Input json to "Json source" and click "Input Json", edit the width/height, then click "Start edit".    |
-| Free style       | Edit the width/height, then click "Start edit".                                        |
-
-To save the pose image, click "Save".  
-To export the pose data, click "Save" and "Copy to clipboard" of "Json" section.
-""")
-  with gr.Row():
-    with gr.Column(scale=1):
-      source = gr.Image(type="pil")
-      width = gr.Slider(label="Width", mininmum=512, maximum=1024, step=64, value=512, key="Width", interactive=True)
-      height = gr.Slider(label="Height", mininmum=512, maximum=1024, step=64, value=512, key="Height", interactive=True)
-      startBtn = gr.Button(value="Start edit")
-      json = gr.JSON(label="Json", lines=10)
-      jsonInput = gr.Textbox(label="Json source", lines=10)
-      jsonInputBtn = gr.Button(value="Input Json")
-    with gr.Column(scale=2):
-      html = gr.HTML(html_text)
-      saveBtn = gr.Button(value="Save")
-      gr.HTML("<ul><li>ctrl + drag to scale</li><li>alt + drag to translate</li><li>shift + drag to rotate(move right first, then up or down)</li></ul>")
-
-  source.change(
-    fn = image_changed,
-    inputs = [source],
-    outputs = [json, width, height])
-  startBtn.click(
-    fn = None,
-    inputs = [json, width, height], 
-    outputs = [],
-    _js="(json, w, h) => { initializePose(json,w,h); return []; }")
-  saveBtn.click(
-    fn = None,
-    inputs = [], outputs = [json],
-    _js="() => { return [savePose()]; }")
-  jsonInputBtn.click(
-    fn = lambda x: x,
-    inputs = [jsonInput], outputs = [json])
-    
-gr.mount_gradio_app(app, demo, path="/")
+import gradio as gr
+import numpy as np
+import cv2
+from fastapi import FastAPI, Request, Response
+from src.body import Body
+
+body_estimation = Body('model/body_pose_model.pth')
+
+def pil2cv(image):
+    ''' PIL型 -> OpenCV型 '''
+    new_image = np.array(image, dtype=np.uint8)
+    if new_image.ndim == 2:  # モノクロ
+        pass
+    elif new_image.shape[2] == 3:  # カラー
+        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGB2BGR)
+    elif new_image.shape[2] == 4:  # 透過
+        new_image = cv2.cvtColor(new_image, cv2.COLOR_RGBA2BGRA)
+    return new_image
+
+with open("static/poseEditor.js", "r") as f:
+    file_contents = f.read()
+
+app = FastAPI()
+
+@app.middleware("http")
+async def some_fastapi_middleware(request: Request, call_next):
+    path = request.scope['path']  # get the request route
+    response = await call_next(request)
+    
+    if path == "/":
+        response_body = ""
+        async for chunk in response.body_iterator:
+            response_body += chunk.decode()
+
+        some_javascript = f"""
+        <script type="text/javascript" defer>
+{file_contents}
+        </script>
+        """
+
+        response_body = response_body.replace("</body>", some_javascript + "</body>")
+
+        del response.headers["content-length"]
+
+        return Response(
+            content=response_body,
+            status_code=response.status_code, 
+            headers=dict(response.headers),
+            media_type=response.media_type
+        )
+
+    return response
+
+# make cndidate to json
+def candidate_to_json_string(arr):
+    a = [f'[{x:.2f}, {y:.2f}]' for x, y, *_ in arr]
+    return '[' + ', '.join(a) + ']'
+
+# make subset to json
+def subset_to_json_string(arr):
+    arr_str = ','.join(['[' + ','.join([f'{num:.2f}' for num in row]) + ']' for row in arr])
+    return '[' + arr_str + ']'
+
+def estimate_body(source):
+    if source == None:
+      return None
+
+    candidate, subset = body_estimation(pil2cv(source))
+    return "{ \"candidate\": " + candidate_to_json_string(candidate) + ", \"subset\": " + subset_to_json_string(subset) + " }"
+    
+def image_changed(image):
+  if (image == None):
+    return {}, 512, 512
+  json = estimate_body(image)
+  return json, image.width, image.height
+
+html_text = f"""
+    <canvas id="canvas" width="512" height="512"></canvas>
+    <script type="text/javascript" defer>{file_contents}</script>
+    """
+
+with gr.Blocks() as demo:
+  gr.Markdown("""### Usage
+
+Choose one of the following methods to edit the pose:
+
+| Style            | Description                                                                               |
+| -----------------| ----------------------------------------------------------------------------------------- |
+| Pose recognition | Upload an image and click "Start edit".                                               |
+| Input json       | Input json to "Json source" and click "Input Json", edit the width/height, then click "Start edit".    |
+| Free style       | Edit the width/height, then click "Start edit".                                        |
+
+To save the pose image, click "Save".  
+To export the pose data, click "Save" and "Copy to clipboard" of "Json" section.
+""")
+  with gr.Row():
+    with gr.Column(scale=1):
+      source = gr.Image(type="pil")
+      width = gr.Slider(label="Width", mininmum=512, maximum=1920, step=64, value=512, key="Width", interactive=True)
+      height = gr.Slider(label="Height", mininmum=512, maximum=1080, step=64, value=512, key="Height", interactive=True)
+      startBtn = gr.Button(value="Start edit")
+      json = gr.JSON(label="Json", lines=10)
+      jsonInput = gr.Textbox(label="Json source", lines=10)
+      jsonInputBtn = gr.Button(value="Input Json")
+    with gr.Column(scale=2):
+      html = gr.HTML(html_text)
+      saveBtn = gr.Button(value="Save")
+      gr.HTML("<ul><li>ctrl + drag to scale</li><li>alt + drag to translate</li><li>shift + drag to rotate(move right first, then up or down)</li></ul>")
+
+  source.change(
+    fn = image_changed,
+    inputs = [source],
+    outputs = [json, width, height])
+  startBtn.click(
+    fn = None,
+    inputs = [json, width, height], 
+    outputs = [],
+    _js="(json, w, h) => { initializePose(json,w,h); return []; }")
+  saveBtn.click(
+    fn = None,
+    inputs = [], outputs = [json],
+    _js="() => { return [savePose()]; }")
+  jsonInputBtn.click(
+    fn = lambda x: x,
+    inputs = [jsonInput], outputs = [json])
+    
+gr.mount_gradio_app(app, demo, path="/")