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0bdfe9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | import itertools
import json
import os
import pickle
from argparse import ArgumentParser
from functools import partial
from multiprocessing import cpu_count, Pool
import numpy as np
from tqdm import tqdm
from ultralytics import YOLO
from input_output.tao_format_output import generate_tao_format_output
from tracking.tracker import Annotation, Box, Tracker
RAW_RESULTS_PICKLE_NAME = "raw_results.pkl"
MIN_SCORE_FOR_MATCH_VALUES = list(np.arange(0.1, 1.0, 0.25))
MIN_FRAMES_VALUES = range(1, 26, 5)
MAX_MISSING_FRAMES_VALUES = range(0, 25, 5)
# MIN_SCORE_FOR_MATCH_VALUES = list(np.arange(0.01, 0.17, 0.025))
# MIN_FRAMES_VALUES = range(10, 17, 1)
# MAX_MISSING_FRAMES_VALUES = range(7, 13, 1)
PARAMETER_VALUES = list(itertools.product(MIN_SCORE_FOR_MATCH_VALUES, MIN_FRAMES_VALUES, MAX_MISSING_FRAMES_VALUES))
def predict_without_tracking_single_video(model, frames):
raw_results_per_frame = list()
for frame in tqdm(frames, leave=False):
raw_results = model.predict(frame[1], verbose=False, conf=0.001)
raw_results_per_frame.append(raw_results[0])
return raw_results_per_frame
def filter_and_get_annotations_for_video(raw_results_per_frame, confidence):
return [
filter_and_get_annotations_for_frame(raw_results, confidence)
for raw_results in raw_results_per_frame
]
def filter_and_get_annotations_for_frame(raw_results, confidence):
return [
Annotation(Box(*box.xyxy[0].tolist()), int(box.cls[0]), float(box.conf[0]))
for box in raw_results.boxes
if float(box.conf[0]) >= confidence and int(box.cls[0]) == 0 # tmot dataset contains only "person" annotations
]
def parse_tao_annotations(tao_annotations_file_path):
with open(tao_annotations_file_path) as tao_annotations_file:
return json.load(tao_annotations_file)
def parse_video_frames_from_tao(video_id, tao_annotations, images_seq_dir_path):
video_name = next(video["name"] for video in tao_annotations["videos"] if video["id"] == video_id)
return sorted([
(
image["frame_index"],
os.path.join(images_seq_dir_path, video_name, "thermal", image["file_name"])
)
for image in tao_annotations["images"]
if image["video_id"] == video_id
], key=lambda x: x[0])
def track_for_params_all_videos(untracked_results_for_video_id, params):
return {
video_id: track_for_params_single_video(untracked_results, params)
for video_id, untracked_results in untracked_results_for_video_id.items()
}
def track_for_params_single_video(untracked_results, params):
min_score_for_match, min_frames, max_missing_frames = params
tracker = Tracker(np.full((1, 1), 1), min_score_for_match=min_score_for_match, min_frames=min_frames, max_missing_frames=max_missing_frames)
for raw_annotations in untracked_results:
tracker.advance_frame(raw_annotations)
tracker.finish()
return tracker
def track_for_params_and_save_results(params, untracked_results_for_video_id, video_name_per_id, video_ids, results_dir_path, confidence):
results_per_video_id = track_for_params_all_videos(untracked_results_for_video_id, params)
tao_output = generate_tao_format_output([
(video_name_per_id[video_id], results_per_video_id[video_id])
for video_id in video_ids
])
min_score_for_match, min_frames, max_missing_frames = params
save_path = (
results_dir_path, f"{confidence}",
f"{min_score_for_match}_{min_frames}_{max_missing_frames}",
"data",
)
os.makedirs(os.path.join(*save_path), exist_ok=True)
with open(os.path.join(*save_path, "results.json"), "w", ) as results_file:
json.dump(tao_output["annotations"], results_file, indent=4)
def main(model_name, confidence, images_seq_dir_path, tao_annotations_file_path, results_dir_path, use_pickle):
model = YOLO(model_name)
tao_annotations = parse_tao_annotations(tao_annotations_file_path)
video_ids = sorted([video["id"] for video in tao_annotations["videos"]])
video_name_per_id = {
video["id"]: video["name"]
for video in tao_annotations["videos"]
}
frames_per_video_id = {
video_id: parse_video_frames_from_tao(video_id, tao_annotations, images_seq_dir_path)
for video_id in video_ids
}
if not use_pickle or not os.path.isfile(RAW_RESULTS_PICKLE_NAME):
print("predicting on videos")
raw_results_for_video_ids = dict()
for video_id, frames in tqdm(frames_per_video_id.items()):
raw_results_for_video_ids[video_id] = predict_without_tracking_single_video(model, frames)
with open(RAW_RESULTS_PICKLE_NAME, "wb") as f:
pickle.dump(raw_results_for_video_ids, f)
else:
print("loading predictions from pickle")
with open(RAW_RESULTS_PICKLE_NAME, "rb") as f:
raw_results_for_video_ids = pickle.load(f)
untracked_results_for_video_id = {
video_id: filter_and_get_annotations_for_video(raw_results_per_frame, confidence)
for video_id, raw_results_per_frame in raw_results_for_video_ids.items()
}
print("tracking for all parameters")
worker = partial(
track_for_params_and_save_results,
untracked_results_for_video_id=untracked_results_for_video_id,
video_name_per_id=video_name_per_id,
video_ids=video_ids,
results_dir_path=results_dir_path,
confidence=confidence,
)
with Pool(processes=max(1, 1 - cpu_count())) as pool:
list(tqdm(pool.imap_unordered(worker, PARAMETER_VALUES), total=len(PARAMETER_VALUES)))
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("model")
parser.add_argument("confidence", type=float)
parser.add_argument("images_seq_dir")
parser.add_argument("tao_annotations")
parser.add_argument("results_dir")
parser.add_argument("--ignore-pickle", action="store_true")
args = parser.parse_args()
main(args.model, args.confidence, args.images_seq_dir, args.tao_annotations, args.results_dir, not args.ignore_pickle)
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