Update app.py

app.py

@@ -6,7 +6,7 @@ import random
 import matplotlib.pyplot as plt
 import numpy as np
 from datetime import datetime
-from services.video_service import get_next_video_frame
+from services.video_service import get_next_video_frame, reset_video_index
 from services.thermal_service import detect_thermal_anomalies
 from services.overlay_service import overlay_boxes
 from services.metrics_service import update_metrics
@@ -17,43 +17,49 @@ frame_rate = 1
 frame_count = 0
 log_entries = []
 anomaly_counts = []
+last_frame = None
+last_metrics = {}
+last_timestamp = ""
+last_detected_images = []
 
 # Constants
 TEMP_IMAGE_PATH = "temp.jpg"
+CAPTURED_FRAMES_DIR = "captured_frames"
+os.makedirs(CAPTURED_FRAMES_DIR, exist_ok=True)
 
 # Core monitor function
 def monitor_feed():
-    global paused
-    global frame_count
+    global paused, frame_count, last_frame, last_metrics, last_timestamp
 
-    frame = None
-
-    if paused:
-        if os.path.exists(TEMP_IMAGE_PATH):
-            frame = cv2.imread(TEMP_IMAGE_PATH)
-
-    if frame is None:
+    if paused and last_frame is not None:
+        frame = last_frame.copy()
+        metrics = last_metrics.copy()
+    else:
         frame = get_next_video_frame()
-
-    if not paused:
         detected_boxes = detect_thermal_anomalies(frame)
         frame = overlay_boxes(frame, detected_boxes)
         cv2.imwrite(TEMP_IMAGE_PATH, frame, [int(cv2.IMWRITE_JPEG_QUALITY), 95])
         metrics = update_metrics(detected_boxes)
-    else:
-        metrics = update_metrics([])
 
-    frame = cv2.resize(frame, (640, 480))  # Fixed window size
+        frame_count += 1
+        last_timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+        if detected_boxes:
+            captured_frame_path = os.path.join(CAPTURED_FRAMES_DIR, f"frame_{frame_count}.jpg")
+            cv2.imwrite(captured_frame_path, frame)
+            last_detected_images.append(captured_frame_path)
+            if len(last_detected_images) > 5:
+                last_detected_images.pop(0)
 
-    # Add frame count and timestamp
-    frame_count += 1
-    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+        last_frame = frame.copy()
+        last_metrics = metrics.copy()
+
+    frame = cv2.resize(last_frame, (640, 480))
     cv2.putText(frame, f"Frame: {frame_count}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
-    cv2.putText(frame, f"{timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+    cv2.putText(frame, f"{last_timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
 
-    # Update logs and anomaly counts
-    anomaly_detected = metrics["anomalies_detected"]
-    log_entries.append(f"{timestamp} - Frame {frame_count} - Anomalies Detected: {anomaly_detected}")
+    anomaly_detected = len(last_metrics.get('anomalies', []))
+    log_entries.append(f"{last_timestamp} - Frame {frame_count} - Anomalies: {anomaly_detected}")
     anomaly_counts.append(anomaly_detected)
 
     if len(log_entries) > 100:
@@ -61,11 +67,7 @@ def monitor_feed():
     if len(anomaly_counts) > 100:
         anomaly_counts.pop(0)
 
-    # THIS IS IMPORTANT FIX 👇
-    label_output = {"Anomalies": anomaly_detected}
-
-    return frame[:, :, ::-1], label_output, "\n".join(log_entries[-10:]), generate_chart()
-
+    return frame[:, :, ::-1], metrics, "\n".join(log_entries[-10:]), generate_chart(), last_detected_images
 
 # Chart generator
 def generate_chart():
@@ -98,6 +100,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         with gr.Column():
             chart_output = gr.Image(label="Detection Trends")
 
+    with gr.Row():
+        captured_images = gr.Gallery(label="Last 5 Captured Events").style(grid=[1], height="auto")
+
     with gr.Row():
         pause_btn = gr.Button("⏸️ Pause")
         resume_btn = gr.Button("▶️ Resume")
@@ -123,11 +128,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
 
     def streaming_loop():
         while True:
-            frame, metrics, logs, chart = monitor_feed()
-            yield frame, metrics, logs, chart
+            frame, metrics, logs, chart, captured = monitor_feed()
+            yield frame, metrics, logs, chart, captured
             time.sleep(frame_rate)
 
-    app.load(streaming_loop, outputs=[video_output, metrics_output, logs_output, chart_output])
+    app.load(streaming_loop, outputs=[video_output, metrics_output, logs_output, chart_output, captured_images])
 
 if __name__ == "__main__":
     app.launch(share=True)