Update app.py

app.py

@@ -165,37 +165,40 @@ def run_model_gpu1000(img):
 
 #@spaces.GPU(duration=10)
 def cellpose_segment(filepath, resize = 1000):
-    img_input = imread(filepath[0])
-    print(img_input.shape)
-    #img_input = np.array(img_pil)
-    img = image_resize(img_input, resize = resize)
-    print(img.shape)
-
-    resize = np.max(img.shape)
-    if resize<1000:
-        masks, flows = run_model_gpu(img)
-    elif resize < 5000:
-        masks, flows = run_model_gpu60(img)
-    elif resize < 20000:
-        masks, flows = run_model_gpu240(img)
-    else:
-        raise ValueError("Image size must be less than 20,000")
-    
+
+    for j in range((len(filepath))):
+        img_input = imread(filepath[j])
+        #img_input = np.array(img_pil)
+        img = image_resize(img_input, resize = resize)
         
+        resize = np.max(img.shape)
+        if resize<1000:
+            masks, flows = run_model_gpu(img)
+        elif resize < 5000:
+            masks, flows = run_model_gpu60(img)
+        elif resize < 20000:
+            masks, flows = run_model_gpu240(img)
+        else:
+            raise ValueError("Image size must be less than 20,000")
+    
+        target_size = (img_input.shape[1], img_input.shape[0])
+        if (target_size[0]!=img.shape[1] or target_size[1]!=img.shape[0]):
+            # scale it back to keep the orignal size
+            masks = cv2.resize(masks.astype('uint16'), target_size, interpolation=cv2.INTER_NEAREST).astype('uint16')
+
+        fname_masks = os.path.splitext(filepath[j])[0]+"_masks.tif"
+        imsave(fname_masks, masks)
+    
+    
     #masks, flows, _ = model.eval(img, channels=[0,0])
     flows = flows[0]
     # masks = np.zeros(img.shape[:2])
     # flows = np.zeros_like(img)
 
     outpix = plot_outlines(img, masks)
-    overlay = plot_overlay(img, masks)
-    
-    target_size = (img_input.shape[1], img_input.shape[0])
-    if (target_size[0]!=img.shape[1] or target_size[1]!=img.shape[0]):
-        # scale it back to keep the orignal size
-        masks = cv2.resize(masks.astype('uint16'), target_size, interpolation=cv2.INTER_NEAREST).astype('uint16')
-        #flows = cv2.resize(flows.astype('float32'), target_size).astype('uint8')
+    #overlay = plot_overlay(img, masks)
     
+        
     
     #crand = .2 + .8 * np.random.rand(np.max(masks.flatten()).astype('int')+1,).astype('float32')
     #crand[0] = 0
@@ -206,19 +209,10 @@ def cellpose_segment(filepath, resize = 1000):
     Ly, Lx = img.shape[:2]
     c = Lx
     outpix = outpix.resize((Lx, Ly), resample  = Image.BICUBIC)
-    overlay = overlay.resize((Lx, Ly), resample  = Image.BICUBIC)
+    #overlay = overlay.resize((Lx, Ly), resample  = Image.BICUBIC)
     flows = flows.resize((Lx, Ly), resample  = Image.BICUBIC)
 
-    #masks = Image.fromarray(255. * crand[masks])
-    #pil_masks = Image.fromarray(masks.astype('int32'))
-    #pil_masks.save(fname_mask)
-
-    fname_out  = os.path.splitext(filepath[0])[0]+"_outlines.png"
-    fname_masks = os.path.splitext(filepath[0])[0]+"_masks.tif"
-
-    imsave(fname_masks, masks)
-    
-    
+    fname_out  = os.path.splitext(filepath[-1])[0]+"_outlines.png"
     outpix.save(fname_out) #"outlines.png")
 
     b1 = gr.DownloadButton(visible=True, value = fname_masks)
@@ -250,7 +244,7 @@ def upload_file(filepath):
 
     #b1 = gr.DownloadButton("Download masks as TIFF", visible=False)
     #b2 = gr.DownloadButton("Download outline image as PNG", visible=False)
-    return filepath[0] #, b1, b2
+    return filepath[-1] #, b1, b2
 
  
 def update_image(filepath): 
@@ -298,7 +292,6 @@ with gr.Blocks(title = "Hello",
             #img_overlay = gr.Image(label = "Overlay", type = "pil", format = 'png') #, width = "50vw", height = "20vw")
             flows = gr.Image(label = "Cellpose flows", type = "pil", format = 'png') #, width = "50vw", height = "20vw")
 
-
             
     sample_list = []
     for j in range(23):