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import json
import os
import cv2
from tqdm import tqdm
import re
from generate_features import write_npy
def read_json_file(file_path):
with open(file_path, 'r') as json_file:
data = json.load(json_file)
return data
def search_a_file_in_directory(directory, file_name):
for root, dirs, files in os.walk(directory):
if file_name in files:
return os.path.join(root, file_name)
raise FileNotFoundError(f"{file_name} not found in {directory}.")
def normalize_bbox(bbox, width, height):
"""
Normalize the bbox
"""
xmin, ymin, xmax, ymax = bbox
xmin = int(round(xmin / width, 2) * 100)
ymin = int(round(ymin / height, 2) * 100)
xmax = int(round(xmax / width, 2) * 100)
ymax = int(round(ymax / height, 2) * 100)
return [xmin, ymin, xmax, ymax]
def write_frames(video_path, frame_dir, start_vid, end_vid, asked_frames):
"""
Write frames to a directory
"""
if asked_frames > (end_vid - start_vid):
raise ValueError("asked_frames is greater than the frames in the video")
dir_name = os.path.splitext(os.path.basename(video_path))[0]
base_path = os.path.join(frame_dir, dir_name)
if not os.path.exists(base_path):
os.makedirs(base_path)
if asked_frames == 0:
step = 1
else:
step = (end_vid - start_vid) // asked_frames
cap = cv2.VideoCapture(video_path)
cap.set(cv2.CAP_PROP_POS_FRAMES, start_vid)
count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
cv2.imwrite(os.path.join(base_path, f'{count}.jpg') , frame)
count += 1
if count == asked_frames:
break
next_frame = start_vid + step * count
cap.set(cv2.CAP_PROP_POS_FRAMES, next_frame)
cap.release()
cv2.destroyAllWindows()
def generate_prompt(vid, vid_path, question, answer,
begin_fid, end_fid,
temporal_gt_begin_fid, temporal_gt_end_fid,
frame_count,
question_tid, vido_data, asked_frames = 0):
# the unique id = vid + begin_fid + end_fid
vid = vid + '-' + str(begin_fid) + '-' + str(end_fid)
target = vido_data['subject/objects'][question_tid]['category']
frame_to_bbox = {}
for fid in range(temporal_gt_begin_fid, temporal_gt_end_fid + 1):
start_bbox = vido_data['trajectories'][fid]
found = False
for a_dict in start_bbox:
if question_tid is a_dict['tid']:
start_bbox = a_dict['bbox']
found = True
break
if not found:
raise ValueError("start_bbox not found")
start_bbox = list(start_bbox.values())
frame_to_bbox[fid] = start_bbox
# # verify the start_bbox
# cap = cv2.VideoCapture(vid_path)
# if not cap.isOpened():
# raise ValueError("Error opening video file")
# cap.set(cv2.CAP_PROP_POS_FRAMES, int(temporal_gt_begin_fid))
# ret, frame = cap.read()
# if not ret:
# raise ValueError("Error reading video file")
# cv2.rectangle(frame, (start_bbox[0], start_bbox[1]),
# (start_bbox[2], start_bbox[3]), (0, 0, 255), 1)
# cv2.imshow('Frame', frame)
# cv2.waitKey(0)
width = vido_data['width']
height = vido_data['height']
# normalize the frame count
original_frame_count = end_fid - begin_fid + 1
if asked_frames > original_frame_count:
if begin_fid + asked_frames < frame_count:
new_end_fid = begin_fid + asked_frames - 1
# print(f"adjusting end_fid from {end_fid} to {new_end_fid}")
end_fid = new_end_fid
else:
# print(f"asked end_fid {begin_fid + asked_frames} is greater than frame_count {frame_count}")
return None
if asked_frames != 0:
frame_count = asked_frames
else:
frame_count = original_frame_count
# normalized start and end frame
normalize_frame_to_bbox = {}
for fid in range(temporal_gt_begin_fid, temporal_gt_end_fid + 1):
relative_start_fid = fid - begin_fid
if asked_frames != 0:
normalized_frame = asked_frames * (relative_start_fid / original_frame_count)
normalized_frame = int(normalized_frame)
normalize_frame_to_bbox[normalized_frame] = normalize_bbox(frame_to_bbox[fid], width, height)
# write_npy(vid_path, vid, begin_fid, end_fid, asked_frames)
new_prompt = r' The {} is at '.format(target)
bboxes = re.sub(r'\s+', '', str(normalize_frame_to_bbox))
new_prompt += bboxes
json_obj = {}
json_obj["id"] = vid
question_dict = {"from": "human", "value": "<video>\n"+question['description']}
answer_dict = {"from": "gpt", "value": answer['description'] + new_prompt}
json_obj["conversations"] = [question_dict, answer_dict]
# token = {"<s0>": start_frame, "<e0>" : end_frame}
json_obj["meta"] = {"asked_frames": asked_frames, "vid_path": vid_path,
"begin_fid": begin_fid, "end_fid": end_fid,
"temporal_gt_begin_fid": temporal_gt_begin_fid, "temporal_gt_end_fid": temporal_gt_end_fid}
return json_obj
def visualize_video(vid_path, begin_fid, end_fid, temporal_gt_begin_fid, temporal_gt_end_fid,
question_tid, answer_tid, vido_data):
"""
visualize the video from begin_fid to end_fid
"""
def on_trackbar(val):
cap.set(cv2.CAP_PROP_POS_FRAMES, val)
ret, frame = cap.read()
if ret:
cv2.imshow('Video', frame)
# Load the video
cap = cv2.VideoCapture(vid_path)
# Check if video opened successfully
if not cap.isOpened():
print("Error opening video file")
total_frames = end_fid - begin_fid + 1
cv2.namedWindow('Video')
cv2.createTrackbar('Frame', 'Video', begin_fid, end_fid, on_trackbar)
cap.set(cv2.CAP_PROP_POS_FRAMES, begin_fid)
# Read until video is completed
while(cap.isOpened()):
current_fid = int(cap.get(cv2.CAP_PROP_POS_FRAMES))
ret, frame = cap.read()
if ret == True:
if current_fid < begin_fid:
current_fid = begin_fid
cap.set(cv2.CAP_PROP_POS_FRAMES, current_fid)
cv2.setTrackbarPos('Frame', 'Video', current_fid)
# show current frame number and total frame number
cv2.putText(frame, f"frame: {current_fid}/{end_fid}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 1)
if current_fid >= temporal_gt_begin_fid and current_fid <= temporal_gt_end_fid:
cv2.putText(frame, f"temporal_gt: {temporal_gt_begin_fid}/{temporal_gt_end_fid}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 1)
elif current_fid < temporal_gt_begin_fid:
cv2.putText(frame, f"start in {temporal_gt_begin_fid - current_fid}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1)
elif current_fid > temporal_gt_end_fid:
cv2.putText(frame, f"end {current_fid - temporal_gt_end_fid} frames before", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1)
# add bbox
try:
question_bbox = vido_data['trajectories'][current_fid][question_tid]['bbox']
# BGR
cv2.rectangle(frame, (question_bbox['xmin'], question_bbox['ymin']),
(question_bbox['xmax'], question_bbox['ymax']), (0, 0, 255), 1)
category = vido_data['subject/objects'][question_tid]['category']
cv2.putText(frame, f"question_{category}", (question_bbox['xmin'], question_bbox['ymax']), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 1)
except IndexError:
# print(f"question bbox not found: current_fid: {current_fid}")
pass
try:
answer_bbox = vido_data['trajectories'][current_fid][answer_tid]['bbox']
cv2.rectangle(frame, (answer_bbox['xmin'], answer_bbox['ymin']),
(answer_bbox['xmax'], answer_bbox['ymax']), (0, 255, 0), 1)
category = vido_data['subject/objects'][answer_tid]['category']
cv2.putText(frame, f"answer_{category}", (answer_bbox['xmin'] + (answer_bbox['xmax'] - answer_bbox['xmin']) // 2, answer_bbox['ymin'] + (answer_bbox['ymax'] - answer_bbox['ymin']) // 2), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1)
except IndexError:
# print(f"answer bbox not found: current_fid: {current_fid}")
pass
# Display the resulting frame
cv2.imshow('Video', frame)
if current_fid >= end_fid:
break
# Wait for a key press
key = cv2.waitKey(25) & 0xFF
# If the 'p' key is pressed, pause the video
if key == ord('p'):
cv2.waitKey(-1) # wait until any key is pressed
# If the 'q' key is pressed, break from the loop
elif key == ord('q'):
break
# Break the loop
else:
break
# When everything done, release the video capture object
cap.release()
cv2.destroyAllWindows()
def process_record(record, vidor_anno_path_base, vidor_path_base):
all_results = []
vid = record['vid']
begin_fid = record['used_segment']['begin_fid']
end_fid = record['used_segment']['end_fid']
# temporal_gt_begin_fid can be 0, count 8456 out of 36202
temporal_gt_begin_fid = record['temporal_gt']['begin_fid']
# temporal_gt_end_fid can be frame_count, count 6622 out of 36202
temporal_gt_end_fid = record['temporal_gt']['end_fid'] - 1
frame_count = record['frame_count']
# path to vidor annotation file
vidor_anno_path = search_a_file_in_directory(vidor_anno_path_base, vid + '.json')
# all other related data in this file
vido_data = read_json_file(vidor_anno_path)
# each record has only one caption
if len(record['captions']) >= 2:
raise ValueError("more than one captions")
answer_tid = record['captions'][0]['target_id']
# each record might has multiple questions,
for q_index, question in enumerate(record['questions']):
question_tid = question['target_id']
# question_bbox_at_begin_fid = vido_data['trajectories'][temporal_gt_begin_fid][question_tid]
# path to video file
vid_path = vido_data['video_path']
vid_path = os.path.join(vidor_path_base, vid_path)
if temporal_gt_begin_fid == -1:
continue
result = generate_prompt(vid, vid_path, question, record['captions'][0],
begin_fid, end_fid,
temporal_gt_begin_fid, temporal_gt_end_fid,
frame_count,
question_tid, vido_data, asked_frames = 100)
if result is not None:
all_results.append(result)
# print(json.dumps(result, indent=4))
# visualize_video(vid_path, begin_fid, end_fid, temporal_gt_begin_fid, temporal_gt_end_fid,
# question_tid, answer_tid, vido_data)
return all_results
def main():
vidor_anno_path_base = 'vidor/train_annotation/training/'
vidor_path_base = 'vidor/train/video'
vidstg_data = read_json_file('VidSTG-Dataset/annotations/train_annotations.json')
# vidor_data = read_json_file('vidor/train_annotation/training/0000/2401075277.json')
all_results = []
# remeber the current index
current_index = 0
if os.path.exists('current_index.txt'):
with open('current_index.txt', 'r') as f:
current_index = int(f.read())
with open('results.json', 'r') as json_file:
all_results = json.load(json_file)
for index, record in tqdm(enumerate(vidstg_data), total=len(vidstg_data)):
if index < current_index:
continue
results = process_record(record, vidor_anno_path_base, vidor_path_base)
if results is None:
continue
all_results.extend(results)
if index % 100 == 0:
with open('results.json', 'w') as json_file:
json.dump(all_results, json_file)
with open('current_index.txt', 'w') as f:
f.write(str(index))
if __name__ == "__main__":
main()
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