Update app.py

app.py

@@ -1,4 +1,6 @@
-# Maximize CPU usage
+##############
+
+#Maximize CPU usage
 import multiprocessing
 import cv2
 
@@ -176,7 +178,7 @@ 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 process_video(confidence_threshold=0.5, selected_classes=None, stream_url=None, resolution_scale=1.0):
+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.
     """
@@ -211,19 +213,8 @@ def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=No
             errors.append("Error: Could not read frame from the stream.")
             break
 
-        # Get the original frame dimensions
-        original_height, original_width = frame.shape[:2]
-
-        # Resize the frame for processing
-        new_width = int(original_width * resolution_scale)
-        new_height = int(original_height * resolution_scale)
-        resized_frame = cv2.resize(frame, (new_width, new_height))
-
-        # Perform object tracking with confidence threshold on the resized frame
-        results = model.track(resized_frame, persist=True, conf=confidence_threshold)
-
-        # Create an annotator for the original frame
-        annotator = Annotator(frame, line_width=2)
+        # Perform object tracking with confidence threshold
+        results = model.track(frame, persist=True, conf=confidence_threshold)
 
         if results[0].boxes.id is not None:
             track_ids = results[0].boxes.id.int().cpu().tolist()
@@ -233,13 +224,6 @@ def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=No
 
             for box, cls, t_id, conf in zip(boxes, clss, track_ids, confs):
                 if conf >= confidence_threshold and model.names[cls] in selected_classes:
-                    # Scale the bounding box back to the original resolution
-                    box = box * (original_width / new_width)
-                    box = box.int().tolist()
-
-                    # Draw the bounding box on the original frame
-                    annotator.box_label(box, label=f"{model.names[cls]} {conf:.2f}", color=colors(cls))
-
                     # Check if the object crosses the line
                     if is_object_crossing_line(box, line_params) and t_id not in crossed_objects:
                         crossed_objects[t_id] = True
@@ -248,8 +232,11 @@ def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=No
                         if len(crossed_objects) > max_tracked_objects:
                             crossed_objects.clear()
 
-        # Draw the angled line on the original frame
-        draw_angled_line(frame, line_params, color=(0, 255, 0), thickness=2)
+        # Visualize the results with bounding boxes, masks, and IDs
+        annotated_frame = results[0].plot()
+
+        # Draw the angled line on the frame
+        draw_angled_line(annotated_frame, line_params, color=(0, 255, 0), thickness=2)
 
         # Display the count on the frame with a modern look
         count = len(crossed_objects)
@@ -257,17 +244,17 @@ def process_video(confidence_threshold=0.5, selected_classes=None, stream_url=No
 
         # Calculate the position for the middle of the top
         margin = 10  # Margin from the top
-        x = (frame.shape[1] - text_width) // 2  # Center-align the text horizontally
+        x = (annotated_frame.shape[1] - text_width) // 2  # Center-align the text horizontally
         y = text_height + margin  # Top-align the text
 
         # Draw the black background rectangle
-        cv2.rectangle(frame, (x - margin, y - text_height - margin), (x + text_width + margin, y + margin), (0, 0, 0), -1)
+        cv2.rectangle(annotated_frame, (x - margin, y - text_height - margin), (x + text_width + margin, y + margin), (0, 0, 0), -1)
 
         # Draw the text
-        cv2.putText(frame, f"COUNT: {count}", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(annotated_frame, f"COUNT: {count}", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
 
         # Yield the annotated frame to Gradio
-        yield frame, ""
+        yield annotated_frame, ""
 
     cap.release()
     logger.info("Stream processing completed.")
@@ -277,7 +264,11 @@ 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
@@ -289,9 +280,15 @@ 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
@@ -302,11 +299,7 @@ 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")
 
-        # Step 4: Adjust resolution scale
-        gr.Markdown("### Step 4: Adjust Resolution Scale (Optional)")
-        resolution_scale = gr.Slider(minimum=0.1, maximum=1.0, value=1.0, label="Resolution Scale")
-
-        # Process the stream
+        #process the stream
         process_button = gr.Button("Process Stream")
 
         # Output image for real-time frame rendering
@@ -316,7 +309,7 @@ with gr.Blocks() as demo:
         error_box = gr.Textbox(label="Errors/Warnings", interactive=False)
 
         # Event listener for processing the video
-        process_button.click(process_video, inputs=[confidence_threshold, selected_classes, stream_url, resolution_scale], outputs=[output_image, error_box])
+        process_button.click(process_video, inputs=[confidence_threshold, selected_classes, stream_url], outputs=[output_image, error_box])
 
 # Launch the interface
 demo.launch(debug=True)