Update requirements

app.py

@@ -13,7 +13,6 @@ def format_results(result,filter = 0):
         annotation = {}
         mask = result.masks.data[i] == 1.0
 
-    
         if torch.sum(mask) < filter:
             continue
         annotation['id'] = i
@@ -50,51 +49,16 @@ def post_process(annotations, image, mask_random_color=True, bbox=None, points=N
     for i, mask in enumerate(annotations):
         show_mask(mask, plt.gca(),random_color=mask_random_color,bbox=bbox,points=points)
     plt.axis('off')
-    # # create a BytesIO object
-    # buf = io.BytesIO()
-
-    # # save plot to buf
-    # plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0.0)
-    
-    # # use PIL to open the image
-    # img = Image.open(buf)
     
-    # # copy the image data
-    # img_copy = img.copy()
     plt.tight_layout()
-    
-    # # don't forget to close the buffer
-    # buf.close()
     return fig
 
 
-# def show_mask(annotation, ax, random_color=False):
-#     if random_color :    # 掩膜颜色是否随机决定
-#         color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
-#     else:
-#         color = np.array([30 / 255, 144 / 255, 255 / 255, 0.6])
-#     mask = annotation.cpu().numpy()
-#     h, w = mask.shape[-2:]
-#     mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
-#     ax.imshow(mask_image)
-
-# def post_process(annotations, image):
-#     plt.figure(figsize=(10, 10))
-#     plt.imshow(image)
-#     for i, mask in enumerate(annotations):
-#         show_mask(mask.data, plt.gca(),random_color=True)
-#     plt.axis('off')
-    
-    # 获取渲染后的像素数据并转换为PIL图像
-    
-    return pil_image
-
-
 # post_process(results[0].masks, Image.open("../data/cake.png"))
 
-def predict(inp, imgsz):
-    imgsz = int(imgsz)  # 确保 imgsz 是整数
-    results = model(inp, device='cpu', retina_masks=True, iou=0.7, conf=0.25, imgsz=imgsz)
+def predict(inp, input_size):
+    input_size = int(input_size)  # 确保 imgsz 是整数
+    results = model(inp, device='cpu', retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size)
     results = format_results(results[0], 100)
     results.sort(key=lambda x: x['area'], reverse=True)
     pil_image = post_process(annotations=results, image=inp)
@@ -106,10 +70,10 @@ def predict(inp, imgsz):
 # post_process(annotations=results, image_path=inp)
 
 demo = gr.Interface(fn=predict,
-                    inputs=[gr.inputs.Image(type='pil'), gr.inputs.Dropdown(choices=[800, 960, 1024])],
+                    inputs=[gr.inputs.Image(type='pil'), gr.inputs.Dropdown(choices=[512, 800, 1024])],
                     outputs=['plot'],
-                     examples=[["assets/sa_8776.jpg", 1024],
-                               ["assets/sa_1309.jpg", 1024]],
+                     examples=[["assets/sa_8776.jpg", 1024]],
+                            #    ["assets/sa_1309.jpg", 1024]],
                     # examples=[["assets/sa_192.jpg"], ["assets/sa_414.jpg"],
                     #           ["assets/sa_561.jpg"], ["assets/sa_862.jpg"],
                     #           ["assets/sa_1309.jpg"], ["assets/sa_8776.jpg"],