Update app.py

app.py

@@ -87,7 +87,7 @@ def draw_bounding_boxes(img, results):
 def show_preds_image(filepath):
     results, img0, dicom_meta = detect_objects(filepath)
     img_with_boxes, results = draw_bounding_boxes(img0, results)
-    
+    print("In show_preds_image:", dicom_meta.IntanceID)
     #return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB)
     return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB), results, dicom_meta
 
@@ -111,19 +111,17 @@ def read_and_preprocess_dicom(file_path: str):
 
     # Normalize the pixel data to 8-bit and convert to a PIL image
     if pixel_array.dtype != np.uint8:
-        print("np.min(pixel_array),np.max(pixel_array):",np.min(pixel_array),np.max(pixel_array))
         pixel_array = ((pixel_array - np.min(pixel_array)) / (np.max(pixel_array) - np.min(pixel_array)) * 255).astype(
             np.uint8)
-        print("Normalized np.min(pixel_array),np.max(pixel_array):",np.min(pixel_array),np.max(pixel_array))
     image_pil = Image.fromarray(pixel_array)
     
     image = image_pil.convert('RGB')
 
-    print("In preprocess dicom:", image.size)
+    #print("In preprocess dicom:", image.size)
     image = np.array(image)[:,:,::-1].copy()
  
     #  shape
-    print("In preprocess dicom-image.shape2:",image.shape)
+    #print("In preprocess dicom-image.shape2:",image.shape)
 
     # Collect metadata in dictionary format and convert to DataFrame
     metadata_dict = {elem.name: str(elem.value) for elem in dicom_data.iterall() if elem.name != 'Pixel Data'}