Update app.py

app.py

@@ -1,6 +1,4 @@
-##############
-
-#Maximize CPU usage
+# Maximize CPU usage
 import multiprocessing
 import cv2
 
@@ -32,6 +30,9 @@ start_point = None
 end_point = None
 line_params = None  # Stores (slope, intercept) of the line
 
+# Low-resolution for inference
+LOW_RES = (320, 180)
+
 def extract_first_frame(stream_url):
     """
     Extracts the first available frame from the IP camera stream and returns it as a PIL image.
@@ -178,6 +179,32 @@ def draw_angled_line(image, line_params, color=(0, 255, 0), thickness=2):
     _, _, start_point, end_point = line_params
     cv2.line(image, start_point, end_point, color, thickness)
 
+def detect_and_draw(frame):
+    """
+    Processes the frame in low resolution and scales the results back to high resolution.
+    """
+    # Create low-res copy
+    low_res_frame = cv2.resize(frame, LOW_RES)
+    
+    # Perform detection on the low-res frame
+    results = model(low_res_frame, verbose=False)
+
+    # Calculate scaling factors for bounding boxes
+    scale_x = frame.shape[1] / LOW_RES[0]
+    scale_y = frame.shape[0] / LOW_RES[1]
+
+    # Draw bounding boxes on the high-res frame
+    for detection in results[0].boxes.data:
+        x1, y1, x2, y2, conf, cls = detection
+        # Scale bounding box coordinates to high-res
+        x1, y1, x2, y2 = int(x1 * scale_x), int(y1 * scale_y), int(x2 * scale_x), int(y2 * scale_y)
+        label = f"{results[0].names[int(cls)]} {conf:.2f}"
+        # Draw the bounding box and label on the high-res frame
+        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+    return frame
+
 def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=None):
     """
     Processes the IP camera stream to count objects of the selected classes crossing the line.
@@ -233,7 +260,7 @@ def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=No
                             crossed_objects.clear()
 
         # Visualize the results with bounding boxes, masks, and IDs
-        annotated_frame = results[0].plot()
+        annotated_frame = detect_and_draw(frame)
 
         # Draw the angled line on the frame
         draw_angled_line(annotated_frame, line_params, color=(0, 255, 0), thickness=2)
@@ -264,11 +291,7 @@ with gr.Blocks() as demo:
     gr.Markdown("<h1>Real-time monitoring, object tracking, and line-crossing detection for CCTV camera streams.</h1></center>")
     gr.Markdown("## https://github.com/SanshruthR/CCTV_SENTRY_YOLO11")
     
-   
-   
     # Step 1: Enter the IP Camera Stream URL
-    # gr.Markdown("### Step 0: Enter the IP Camera Stream URL")
-    # stream_url = gr.Textbox(label="Enter IP Camera Stream URL", value="https://s103.ipcamlive.com/streams/67n4ojknye7lkxpmf/stream.m3u8", visible=False)
     stream_url = gr.Textbox(label="Enter IP Camera Stream URL", value="https://s104.ipcamlive.com/streams/68idokwtondsqpmkr/stream.m3u8", visible=False)
 
     # Step 1: Extract the first frame from the stream
@@ -280,15 +303,9 @@ with gr.Blocks() as demo:
         # Image component for displaying the first frame
         image = gr.Image(value=first_frame, label="First Frame of Stream", type="pil")
 
-      
         line_info = gr.Textbox(label="Line Coordinates", value="Line Coordinates:\nStart: None, End: None")
         image.select(update_line, inputs=image, outputs=[image, line_info])
 
-        # Reset the line (optional)
-        # gr.Markdown("### Step 4: Reset the Line (Optional)")
-        # reset_button = gr.Button("Reset Line")
-        # reset_button.click(reset_line, inputs=None, outputs=[image, line_info])
-
         # Step 2: Select classes to detect
         gr.Markdown("### Step 2: Select Classes to Detect")
         model = YOLO(model="yolo11n.pt")  # Load the model to get class names
@@ -299,7 +316,7 @@ with gr.Blocks() as demo:
         gr.Markdown("### Step 3: Adjust Confidence Threshold (Optional)")
         confidence_threshold = gr.Slider(minimum=0.0, maximum=1.0, value=0.2, label="Confidence Threshold")
 
-        #process the stream
+        # Process the stream
         process_button = gr.Button("Process Stream")
 
         # Output image for real-time frame rendering