Update app.py

app.py

@@ -18,7 +18,7 @@ from yolov5.models.experimental import attempt_load
 from yolov5.utils.general import non_max_suppression
 from yolov5.utils.augmentations import letterbox
 
-#'''
+'''
 # Example URLs for downloading images
 file_urls = [
     "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0",
@@ -40,7 +40,7 @@ def download_file(url, save_name):
 # Download images
 for i, url in enumerate(file_urls):
     download_file(url, f"image_{i}.jpg")
-
+'''
 
 # Load YOLOv5 model (placeholder)
 model_path = "best.pt"  # Path to your YOLOv5 model
@@ -48,12 +48,12 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # Use GPU
 model = attempt_load(model_path, device=device)  # Placeholder for model loading
 model.eval()  # Set the model to evaluation mode
 
-def preprocess_image(image_path):
-#def preprocess_image(image):
-    img0 = cv2.imread(image_path)
-    print("in preprocess-0 img0.shape:",img0.shape)
-    #img0 = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
-    img = letterbox(img0, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
+#def preprocess_image(image_path):
+def preprocess_image(image):
+    #img0 = cv2.imread(image_path)
+    print("in preprocess-0 image.shape:",image.shape)
+    img = letterbox(image, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
+    #img = letterbox(img0, 640, stride=32, auto=True)[0]  # Resize and pad to 640x640
     print("in preprocess-1 img.shape:",img.shape)
     img = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, to 3x416x416
     img = np.ascontiguousarray(img)
@@ -97,9 +97,9 @@ def postprocess(pred, img0_shape, img):
     return results
 
 def detect_objects(image_path):
-    #dicom_image, dicom_meta = read_and_preprocess_dicom(image_path)
-    img, img0 = preprocess_image(image_path)
-    #img, img0 = preprocess_image(dicom_image)
+    dicom_image, dicom_meta = read_and_preprocess_dicom(image_path)
+    #img, img0 = preprocess_image(image_path)
+    img, img0 = preprocess_image(dicom_image)
     pred = infer(model, img)
     results = postprocess(pred, img0.shape, img)
     return results
@@ -117,6 +117,7 @@ def show_preds_image(filepath):
     img_with_boxes = draw_bounding_boxes(img0, results)
     return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB)
     
+'''    
 # Define Gradio components
 input_component = gr.components.Image(type="filepath", label="Input Image")
 output_component = gr.components.Image(type="numpy", label="Output Image")
@@ -170,16 +171,13 @@ def read_and_preprocess_dicom(file_path: str):
     # PIL images into NumPy arrays
     numpydata = np.asarray(image_pil)
     #convert to cv2 format
-    numpydata = numpydata.reshape((numpydata.shape[0], numpydata.shape[1], 1))
+    numpydata = numpydata.reshape((numpydata.shape[0], numpydata.shape[1], 3))
     print("In preprocess dicom:", numpydata.shape)
     #image = np.array(numpydata)[::-1].copy()
     image = np.array(numpydata)[:,:,::-1].copy()
-
-    # <class 'numpy.ndarray'>
-    print(type(numpydata))
  
     #  shape
-    print("numpydata.shape:",numpydata.shape)
+    print("In preprocess dicom-image.shape:",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'}
@@ -236,4 +234,4 @@ def build_interface():
 if __name__ == '__main__':
     demo = build_interface()
     demo.launch
-'''
+