Update run.py

run.py

@@ -42,14 +42,14 @@ def segment(im, interval_s=2):
     im = cv2.resize(im, (im.shape[1] // 2, im.shape[0] // 2))
     imout = im.copy()
     
-    if counter.increment() % 3 == 0:
-        # if (counter.imout.sum() == 0) or ((cnt % 100) == 0):
-        pixel_values = image_processor(im, return_tensors="pt").pixel_values.to(device)
-        outputs = model(pixel_values=pixel_values)
-        logits = outputs.logits.cpu().detach().numpy() ** 2
-        logits_n = (logits[0, 0] - logits[0, 0].min()) / (logits[0, 0].max() - logits[0, 0].min())
-        logits_n = cv2.resize(logits_n, (im.shape[1], im.shape[0]))
-        imout[..., 1] = np.clip(imout[..., 1] + logits_n * 200, 0, 254)
+    # if counter.increment() % 3 == 0:
+    #     # if (counter.imout.sum() == 0) or ((cnt % 100) == 0):
+    #     pixel_values = image_processor(im, return_tensors="pt").pixel_values.to(device)
+    #     outputs = model(pixel_values=pixel_values)
+    #     logits = outputs.logits.cpu().detach().numpy() ** 2
+    #     logits_n = (logits[0, 0] - logits[0, 0].min()) / (logits[0, 0].max() - logits[0, 0].min())
+    #     logits_n = cv2.resize(logits_n, (im.shape[1], im.shape[0]))
+    #     imout[..., 1] = np.clip(imout[..., 1] + logits_n * 200, 0, 254)
     return imout, counter.count  #np.flipud(im)
 
 # with gr.Blocks() as demo: