Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -6,6 +6,7 @@ import random
|
|
6 |
import matplotlib.pyplot as plt
|
7 |
import numpy as np
|
8 |
from datetime import datetime
|
|
|
9 |
from services.video_service import get_next_video_frame, reset_video_index
|
10 |
from services.thermal_service import detect_thermal_anomalies
|
11 |
from services.overlay_service import overlay_boxes
|
@@ -17,6 +18,7 @@ frame_rate = 1
|
|
17 |
frame_count = 0
|
18 |
log_entries = []
|
19 |
anomaly_counts = []
|
|
|
20 |
last_frame = None
|
21 |
last_metrics = {}
|
22 |
last_timestamp = ""
|
@@ -54,25 +56,28 @@ def monitor_feed():
|
|
54 |
last_frame = frame.copy()
|
55 |
last_metrics = metrics.copy()
|
56 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
57 |
frame = cv2.resize(last_frame, (640, 480))
|
58 |
cv2.putText(frame, f"Frame: {frame_count}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
59 |
cv2.putText(frame, f"{last_timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
60 |
|
61 |
-
|
62 |
-
log_entries.append(f"{last_timestamp} - Frame {frame_count} - Anomalies: {anomaly_detected}")
|
63 |
-
anomaly_counts.append(anomaly_detected)
|
64 |
-
|
65 |
-
if len(log_entries) > 100:
|
66 |
-
log_entries.pop(0)
|
67 |
-
# if len(anomaly_counts) > 100:
|
68 |
-
# anomaly_counts.pop(0)
|
69 |
-
|
70 |
-
metrics_str = "\n".join([f"{k}: {v}" for k, v in last_metrics.items()])
|
71 |
|
72 |
-
|
73 |
|
74 |
-
|
75 |
-
def generate_chart():
|
76 |
fig, ax = plt.subplots(figsize=(4, 2))
|
77 |
ax.plot(anomaly_counts[-50:], marker='o')
|
78 |
ax.set_title("Anomalies Over Time")
|
@@ -84,31 +89,45 @@ def generate_chart():
|
|
84 |
plt.close(fig)
|
85 |
return chart_path
|
86 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
87 |
# Gradio UI
|
88 |
with gr.Blocks(theme=gr.themes.Soft()) as app:
|
89 |
-
gr.Markdown("#
|
90 |
|
91 |
-
status_text = gr.Markdown("**Status:** 🟢 Running"
|
92 |
|
93 |
with gr.Row():
|
94 |
with gr.Column(scale=3):
|
95 |
-
video_output = gr.Image(label="Live Video Feed",
|
96 |
with gr.Column(scale=1):
|
97 |
-
metrics_output = gr.Textbox(label="Live Metrics", lines=
|
98 |
|
99 |
with gr.Row():
|
100 |
-
|
101 |
-
|
102 |
-
|
103 |
-
chart_output = gr.Image(label="Detection Trends")
|
104 |
|
105 |
with gr.Row():
|
106 |
-
captured_images = gr.Gallery(label="
|
107 |
|
108 |
with gr.Row():
|
109 |
pause_btn = gr.Button("⏸️ Pause")
|
110 |
resume_btn = gr.Button("▶️ Resume")
|
111 |
-
frame_slider = gr.Slider(0.
|
112 |
|
113 |
def toggle_pause():
|
114 |
global paused
|
@@ -130,11 +149,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
|
|
130 |
|
131 |
def streaming_loop():
|
132 |
while True:
|
133 |
-
frame, metrics, logs, chart, captured = monitor_feed()
|
134 |
-
yield frame, metrics, logs, chart, captured
|
135 |
time.sleep(frame_rate)
|
136 |
|
137 |
-
app.load(streaming_loop, outputs=[video_output, metrics_output, logs_output, chart_output, captured_images])
|
138 |
|
139 |
if __name__ == "__main__":
|
140 |
app.launch(share=True)
|
|
|
6 |
import matplotlib.pyplot as plt
|
7 |
import numpy as np
|
8 |
from datetime import datetime
|
9 |
+
from collections import Counter
|
10 |
from services.video_service import get_next_video_frame, reset_video_index
|
11 |
from services.thermal_service import detect_thermal_anomalies
|
12 |
from services.overlay_service import overlay_boxes
|
|
|
18 |
frame_count = 0
|
19 |
log_entries = []
|
20 |
anomaly_counts = []
|
21 |
+
anomaly_types_all = []
|
22 |
last_frame = None
|
23 |
last_metrics = {}
|
24 |
last_timestamp = ""
|
|
|
56 |
last_frame = frame.copy()
|
57 |
last_metrics = metrics.copy()
|
58 |
|
59 |
+
# Update persistent logs and stats
|
60 |
+
anomaly_detected = len(last_metrics.get('anomalies', []))
|
61 |
+
anomaly_types_all.extend([a['label'] for a in last_metrics.get('anomalies', [])])
|
62 |
+
log_entries.append(f"{last_timestamp} - Frame {frame_count} - Anomalies: {anomaly_detected}")
|
63 |
+
anomaly_counts.append(anomaly_detected)
|
64 |
+
|
65 |
+
if len(log_entries) > 100:
|
66 |
+
log_entries.pop(0)
|
67 |
+
if len(anomaly_counts) > 500:
|
68 |
+
anomaly_counts.pop(0)
|
69 |
+
if len(anomaly_types_all) > 500:
|
70 |
+
anomaly_types_all.pop(0)
|
71 |
+
|
72 |
frame = cv2.resize(last_frame, (640, 480))
|
73 |
cv2.putText(frame, f"Frame: {frame_count}", (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
74 |
cv2.putText(frame, f"{last_timestamp}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
75 |
|
76 |
+
return frame[:, :, ::-1], last_metrics, "\n".join(log_entries[-10:]), generate_line_chart(), generate_pie_chart(), last_detected_images
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
77 |
|
78 |
+
# Line chart
|
79 |
|
80 |
+
def generate_line_chart():
|
|
|
81 |
fig, ax = plt.subplots(figsize=(4, 2))
|
82 |
ax.plot(anomaly_counts[-50:], marker='o')
|
83 |
ax.set_title("Anomalies Over Time")
|
|
|
89 |
plt.close(fig)
|
90 |
return chart_path
|
91 |
|
92 |
+
# Pie chart for anomaly types
|
93 |
+
def generate_pie_chart():
|
94 |
+
if not anomaly_types_all:
|
95 |
+
return None
|
96 |
+
fig, ax = plt.subplots(figsize=(4, 2))
|
97 |
+
count = Counter(anomaly_types_all[-200:])
|
98 |
+
labels, sizes = zip(*count.items())
|
99 |
+
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
|
100 |
+
ax.axis('equal')
|
101 |
+
fig.tight_layout()
|
102 |
+
pie_path = "pie_temp.png"
|
103 |
+
fig.savefig(pie_path)
|
104 |
+
plt.close(fig)
|
105 |
+
return pie_path
|
106 |
+
|
107 |
# Gradio UI
|
108 |
with gr.Blocks(theme=gr.themes.Soft()) as app:
|
109 |
+
gr.Markdown("# 🛡️ Command Room Dashboard: Thermal Anomaly Monitoring")
|
110 |
|
111 |
+
status_text = gr.Markdown("**Status:** 🟢 Running")
|
112 |
|
113 |
with gr.Row():
|
114 |
with gr.Column(scale=3):
|
115 |
+
video_output = gr.Image(label="Live Video Feed", width=640, height=480)
|
116 |
with gr.Column(scale=1):
|
117 |
+
metrics_output = gr.Textbox(label="Live Metrics", lines=4)
|
118 |
|
119 |
with gr.Row():
|
120 |
+
logs_output = gr.Textbox(label="Live Logs", lines=8)
|
121 |
+
chart_output = gr.Image(label="Detection Trend")
|
122 |
+
pie_output = gr.Image(label="Anomaly Types")
|
|
|
123 |
|
124 |
with gr.Row():
|
125 |
+
captured_images = gr.Gallery(label="Captured Events (Last 5)")
|
126 |
|
127 |
with gr.Row():
|
128 |
pause_btn = gr.Button("⏸️ Pause")
|
129 |
resume_btn = gr.Button("▶️ Resume")
|
130 |
+
frame_slider = gr.Slider(0.2, 5, value=1, label="Frame Interval (seconds)")
|
131 |
|
132 |
def toggle_pause():
|
133 |
global paused
|
|
|
149 |
|
150 |
def streaming_loop():
|
151 |
while True:
|
152 |
+
frame, metrics, logs, chart, pie, captured = monitor_feed()
|
153 |
+
yield frame, str(metrics), logs, chart, pie, captured
|
154 |
time.sleep(frame_rate)
|
155 |
|
156 |
+
app.load(streaming_loop, outputs=[video_output, metrics_output, logs_output, chart_output, pie_output, captured_images])
|
157 |
|
158 |
if __name__ == "__main__":
|
159 |
app.launch(share=True)
|