Update app.py
Browse files
app.py
CHANGED
@@ -47,17 +47,22 @@ def generate_journal_with_images(video_path, frame_interval=30,confidence_thresh
|
|
47 |
journal_entries = []
|
48 |
image_paths = []
|
49 |
frame_count = 0
|
50 |
-
last_processed_frame = None
|
51 |
output_folder = "detected_frames"
|
52 |
os.makedirs(output_folder, exist_ok=True) # Create folder to store images
|
|
|
|
|
53 |
|
54 |
while cap.isOpened():
|
55 |
ret, frame = cap.read()
|
56 |
if not ret:
|
57 |
break
|
58 |
|
59 |
-
#
|
60 |
-
|
|
|
|
|
|
|
|
|
61 |
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
62 |
|
63 |
# Make predictions using YOLOv10 on the current frame
|
@@ -80,23 +85,20 @@ def generate_journal_with_images(video_path, frame_interval=30,confidence_thresh
|
|
80 |
cv2.imwrite(frame_filename, annotated_frame[:, :, ::-1]) # Convert back to BGR for saving
|
81 |
image_paths.append(frame_filename)
|
82 |
|
83 |
-
# Get current timestamp in the video
|
84 |
-
timestamp = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 # Convert ms to seconds
|
85 |
-
|
86 |
# Categorize the detected objects into activities
|
87 |
activity_summary = categorize_activity(detected_objects)
|
88 |
|
89 |
# Store the activities with their timestamp
|
90 |
for activity, objects in activity_summary.items():
|
91 |
-
journal_entries.append(f"At {
|
92 |
|
93 |
-
|
94 |
|
95 |
frame_count += 1
|
96 |
|
97 |
cap.release()
|
98 |
|
99 |
-
return journal_entries, image_paths
|
100 |
|
101 |
|
102 |
def display_journal_with_images(video):
|
|
|
47 |
journal_entries = []
|
48 |
image_paths = []
|
49 |
frame_count = 0
|
|
|
50 |
output_folder = "detected_frames"
|
51 |
os.makedirs(output_folder, exist_ok=True) # Create folder to store images
|
52 |
+
|
53 |
+
last_processed_second = -1 # Keep track of the last processed second
|
54 |
|
55 |
while cap.isOpened():
|
56 |
ret, frame = cap.read()
|
57 |
if not ret:
|
58 |
break
|
59 |
|
60 |
+
# Get the current timestamp in the video
|
61 |
+
current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 # Convert ms to seconds
|
62 |
+
current_second = int(current_time) # Round down to the nearest second
|
63 |
+
|
64 |
+
# Process only one frame per second
|
65 |
+
if current_second > last_processed_second:
|
66 |
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
67 |
|
68 |
# Make predictions using YOLOv10 on the current frame
|
|
|
85 |
cv2.imwrite(frame_filename, annotated_frame[:, :, ::-1]) # Convert back to BGR for saving
|
86 |
image_paths.append(frame_filename)
|
87 |
|
|
|
|
|
|
|
88 |
# Categorize the detected objects into activities
|
89 |
activity_summary = categorize_activity(detected_objects)
|
90 |
|
91 |
# Store the activities with their timestamp
|
92 |
for activity, objects in activity_summary.items():
|
93 |
+
journal_entries.append(f"At {current_time:.2f} seconds: {', '.join(objects[0])}")
|
94 |
|
95 |
+
last_processed_second = current_second # Update the last processed second
|
96 |
|
97 |
frame_count += 1
|
98 |
|
99 |
cap.release()
|
100 |
|
101 |
+
return journal_entries, image_paths
|
102 |
|
103 |
|
104 |
def display_journal_with_images(video):
|