Update app.py

app.py

@@ -18,44 +18,20 @@ 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",
-    "https://www.dropbox.com/scl/fi/asrmao4b4fpsrhqex8kog/2_jpg.rf.b87583d95aa220d4b7b532ae1948e7b7.jpg?rlkey=jkmux5jjy8euzhxizupdmpesb&st=v3ld14tx&dl=0",
-    "https://www.dropbox.com/scl/fi/fi0e8zxqqy06asnu0robz/3_jpg.rf.d2932cce7e88c2675e300ececf9f1b82.jpg?rlkey=hfdqwxkxetabe38ukzbb39pl5&st=ga1uouhj&dl=0",
-    "https://www.dropbox.com/scl/fi/ruobyat1ld1c33ch5yjpv/4_jpg.rf.3395c50b4db0ec0ed3448276965b2459.jpg?rlkey=j1m4qa0pmdh3rlr344v82u3am&st=lex8h3qi&dl=0",
-    "https://www.dropbox.com/scl/fi/ok3izk4jj1pg6psxja3aj/5_jpg.rf.62f3dc64b6c894fbb165d8f6e2ee1382.jpg?rlkey=euu16z8fd8u8za4aflvu5qg4v&st=pwno39nc&dl=0",
-    "https://www.dropbox.com/scl/fi/8r1fpwxkwq7c2i6ky6qv5/10_jpg.rf.c1785c33dd3552e860bf043c2fd0a379.jpg?rlkey=fcw41ppgzu0ao7xo6ijbpdi4c&st=to2udvxb&dl=0",
-    "https://www.dropbox.com/scl/fi/ihiid7hbz1vvaoqrstwa5/7_jpg.rf.dfc30f9dc198cf6697d9023ac076e822.jpg?rlkey=yh67p4ex52wn9t0bfw0jr77ef&st=02qw80xa&dl=0",
-]
-
-def download_file(url, save_name):
-    """Downloads a file from a URL."""
-    if not os.path.exists(save_name):
-        file = requests.get(url)
-        with open(save_name, 'wb') as f:
-            f.write(file.content)
-
-# 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
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  # Use GPU if available
 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 image.shape:",img0.size)
-    #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
+#def preprocess_image(image_path):
+def preprocess_image(image):
+    #img0 = cv2.imread(image_path)
+    print("in preprocess-0 image.shape:",image.size)
+    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 = img.transpose(2, 0, 1)[::-1]  # Convert BGR to RGB, 
     img = np.ascontiguousarray(img)
     img = torch.from_numpy(img).to(device)
     img = img.float()  # uint8 to fp16/32
@@ -64,8 +40,8 @@ def preprocess_image(image_path):
         img = img.unsqueeze(0)
     print("in preprocess-2 img.shape:",img.shape)
 
-    #return img, image
-    return img, img0
+    return img, image
+    #return img, img0
 
 def infer(model, img):
     with torch.no_grad():
@@ -86,25 +62,25 @@ def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
     coords[:, :4].clip_(min=0, max=img1_shape[0])  # clip boxes
     return coords
 
-def postprocess(pred, img0_shape, img):
-#def postprocess(pred, img0, img):    
+#def postprocess(pred, img0_shape, img):
+def postprocess(pred, img0, img):    
     pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False)
     results = []
     for det in pred:  # detections per image
         if len(det):
-            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0_shape).round()
-            #det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
+            #det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0_shape).round()
+            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0.shape).round()
             for *xyxy, conf, cls in reversed(det):
                 results.append((xyxy, conf, cls))
     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)
-    #results = postprocess(pred, dicom_image, img)
+    #results = postprocess(pred, img0.shape, img)
+    results = postprocess(pred, dicom_image, img)
     return results #, dicom_image
 
 def draw_bounding_boxes(img, results):
@@ -115,11 +91,9 @@ def draw_bounding_boxes(img, results):
     return img
 
 def show_preds_image(filepath):
-    #dicom_image, dicom_meta = read_and_preprocess_dicom(image_path)
-    #results, img0 = detect_objects(filepath)
-    results = detect_objects(filepath)
-    img0 = cv2.imread(filepath)
-    #img_with_boxes = draw_bounding_boxes(img0, results)
+    results, img0 = detect_objects(filepath)
+    #results = detect_objects(filepath)
+    #img0 = cv2.imread(filepath)
     img_with_boxes = draw_bounding_boxes(img0, results)
     return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB)
     
@@ -134,14 +108,7 @@ interface = gr.Interface(
     inputs=input_component,
     outputs=output_component,
     title="Lung Nodule Detection",
-    examples=[
-        "image_1.jpg",
-        "image_2.jpg",
-        "image_3.jpg",
-        "image_4.jpg",
-        "image_5.jpg",
-        "image_6.jpg",
-    ],
+    examples=['samples/81_80.dcm','samples/110_109.dcm','samples/189_188.dcm'],
     description=' "This online deployment proves the effectiveness and efficient function of the machine learning model in identifying lung cancer nodules. The implementation of YOLO for core detection tasks is employed that is an efficient and accurate algorithm for object detection. Through the precise hyper-parameter tuning process, the model proposed in this paper has given an impressive boost in the performance. Moreover, the model uses Retinanet algorithm which is recognized as the powerful tool effective in dense object detection. In an attempt to enhance the model’s performance, the backbone of this architecture consists of a Feature Pyramid Network (FPN). The FPN plays an important role in boosting the model’s capacity in recognizing objects in different scales through the construction of high semantic feature map in different resolutions. In conclusion, this deployment encompasses YOLOv5, hyperparameter optimization, Retinanet, and FPN as one of the most effective and modern solutions for the detection of lung cancer nodules." ~ Basil Shaji 😇',
     live=False,
 )