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visualize_results.py
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import pdb
import os
import cv2
from yolox.utils import vis
import numpy as np
import argparse
import sys
'''
MOT submission format:
<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>
'''
MOT17_VIDEO_LEN = {
"MOT17-02-FRCNN": 600,
"MOT17-04-FRCNN": 1050,
"MOT17-05-FRCNN": 837,
"MOT17-09-FRCNN": 525,
"MOT17-10-FRCNN": 654,
"MOT17-11-FRCNN": 900,
"MOT17-13-FRCNN": 750
}
MOT17_VIDEO_LEN_TEST = {
"MOT17-01-FRCNN": 600,
"MOT17-03-FRCNN": 1050,
"MOT17-06-FRCNN": 837,
"MOT17-07-FRCNN": 525,
"MOT17-08-FRCNN": 654,
"MOT17-12-FRCNN": 900,
"MOT17-14-FRCNN": 750
}
MOT20_VIDEO_LEN = {
"MOT20-04": 2080,
"MOT20-06": 1008,
"MOT20-07": 585,
"MOT20-08": 806
}
MOT17_VIDEO_SPLIT = dict()
MOT20_VIDEO_SPLIT = dict()
for video_name in MOT17_VIDEO_LEN:
num_images = MOT17_VIDEO_LEN[video_name]
MOT17_VIDEO_SPLIT[video_name] = dict()
MOT17_VIDEO_SPLIT[video_name]["train_half"] = [1, num_images // 2 + 1]
MOT17_VIDEO_SPLIT[video_name]["val_half"] = [num_images // 2 + 2, num_images]
MOT17_VIDEO_SPLIT[video_name]["full"] = [1, num_images]
for video_name in MOT20_VIDEO_LEN:
num_images = MOT20_VIDEO_LEN[video_name]
MOT20_VIDEO_SPLIT[video_name] = dict()
MOT20_VIDEO_SPLIT[video_name]["full"] = [1, num_images]
_COLORS = np.array(
[
0.000, 0.447, 0.741,
0.850, 0.325, 0.098,
0.929, 0.694, 0.125,
0.494, 0.184, 0.556,
0.466, 0.674, 0.188,
0.301, 0.745, 0.933,
0.635, 0.078, 0.184,
0.300, 0.300, 0.300,
0.600, 0.600, 0.600,
1.000, 0.000, 0.000,
1.000, 0.500, 0.000,
0.749, 0.749, 0.000,
0.000, 1.000, 0.000,
0.000, 0.000, 1.000,
0.667, 0.000, 1.000,
0.333, 0.333, 0.000,
0.333, 0.667, 0.000,
0.333, 1.000, 0.000,
0.667, 0.333, 0.000,
0.667, 0.667, 0.000,
0.667, 1.000, 0.000,
1.000, 0.333, 0.000,
1.000, 0.667, 0.000,
1.000, 1.000, 0.000,
0.000, 0.333, 0.500,
0.000, 0.667, 0.500,
0.000, 1.000, 0.500,
0.333, 0.000, 0.500,
0.333, 0.333, 0.500,
0.333, 0.667, 0.500,
0.333, 1.000, 0.500,
0.667, 0.000, 0.500,
0.667, 0.333, 0.500,
0.667, 0.667, 0.500,
0.667, 1.000, 0.500,
1.000, 0.000, 0.500,
1.000, 0.333, 0.500,
1.000, 0.667, 0.500,
1.000, 1.000, 0.500,
0.000, 0.333, 1.000,
0.000, 0.667, 1.000,
0.000, 1.000, 1.000,
0.333, 0.000, 1.000,
0.333, 0.333, 1.000,
0.333, 0.667, 1.000,
0.333, 1.000, 1.000,
0.667, 0.000, 1.000,
0.667, 0.333, 1.000,
0.667, 0.667, 1.000,
0.667, 1.000, 1.000,
1.000, 0.000, 1.000,
1.000, 0.333, 1.000,
1.000, 0.667, 1.000,
0.333, 0.000, 0.000,
0.500, 0.000, 0.000,
0.667, 0.000, 0.000,
0.833, 0.000, 0.000,
1.000, 0.000, 0.000,
0.000, 0.167, 0.000,
0.000, 0.333, 0.000,
0.000, 0.500, 0.000,
0.000, 0.667, 0.000,
0.000, 0.833, 0.000,
0.000, 1.000, 0.000,
0.000, 0.000, 0.167,
0.000, 0.000, 0.333,
0.000, 0.000, 0.500,
0.000, 0.000, 0.667,
0.000, 0.000, 0.833,
0.000, 0.000, 1.000,
0.000, 0.000, 0.000,
0.143, 0.143, 0.143,
0.286, 0.286, 0.286,
0.429, 0.429, 0.429,
0.571, 0.571, 0.571,
0.714, 0.714, 0.714,
0.857, 0.857, 0.857,
0.000, 0.447, 0.741,
0.314, 0.717, 0.741,
0.50, 0.5, 0
]
).astype(np.float32).reshape(-1, 3)
def visualize_box(img, text, box, color_index):
x0, y0, width, height = box
x0, y0, width, height = int(x0), int(y0), int(width), int(height)
color = (_COLORS[color_index%80] * 255).astype(np.uint8).tolist()
txt_color = (0, 0, 0) if np.mean(_COLORS[color_index%80]) > 0.5 else (255, 255, 255)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.6, 1)[0]
cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)
txt_bk_color = (_COLORS[color_index%80] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(
img,
(x0, y0 + 1),
(x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),
txt_bk_color,
-1
)
cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.6, txt_color, thickness=1)
return img
def visualize_detections(img_dir, out_dir, detections_dir, mode="val_half", path="{}/{}_detections.txt", dataset="mot17", test=False):
if dataset == "mot17":
VIDEO_LEN = MOT17_VIDEO_LEN
VIDEO_SPLIT = MOT17_VIDEO_SPLIT
elif dataset == "mot20":
VIDEO_LEN = MOT20_VIDEO_LEN
VIDEO_SPLIT = MOT20_VIDEO_SPLIT
else:
assert 0
for video_name in VIDEO_LEN:
detection_f = path.format(detections_dir, video_name)
# detection_f = os.path.join(detections_dir, "{}_detections.txt".format(video_name))
f = open(detection_f)
dets = np.loadtxt(f, delimiter=",")
frame_range = VIDEO_SPLIT[video_name][mode]
assert(frame_range[1]-frame_range[0] == dets[:, 0].max()-dets[:,0].min())
frame_gap = dets[:,0].min() - frame_range[0]
video_img_dir = os.path.join(img_dir, video_name, "img1")
video_out_dir = os.path.join(out_dir, video_name)
os.makedirs(video_out_dir, exist_ok=True)
fake_frame_min = int(dets[:,0].min())
fake_frame_max = int(dets[:,0].max())
for frame_ind in range(fake_frame_min, fake_frame_max+1):
real_frame_ind = frame_ind - frame_gap
frame_dets = dets[np.where(dets[:,0]==frame_ind)]
im_path = os.path.join(video_img_dir, "%06d.jpg" % real_frame_ind)
img = cv2.imread(im_path)
for i in range(frame_dets.shape[0]):
box = frame_dets[i]
score = box[6]
text = '{:.1f}'.format(score * 100)
img = visualize_box(img, text, box[2:6], i)
'''
x0, y0, width, height = box[2:6]
x0, y0, width, height = int(x0), int(y0), int(width), int(height)
color = (_COLORS[i%80] * 255).astype(np.uint8).tolist()
txt_color = (0, 0, 0) if np.mean(_COLORS[i%80]) > 0.5 else (255, 255, 255)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)
txt_bk_color = (_COLORS[i%80] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(
img,
(x0, y0 + 1),
(x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),
txt_bk_color,
-1
)
cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)
'''
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)
cv2.putText(img, "%06d.jpg" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)
cv2.imwrite(os.path.join(video_out_dir, "%06d.jpg" % real_frame_ind), img)
def visualize_tracks(img_dir, out_dir, tracks_dir, mode, dataset="mot17"):
if dataset == "mot17":
VIDEO_LEN = MOT17_VIDEO_LEN
elif dataset == "mot20":
VIDEO_LEN = MOT20_VIDEO_LEN
elif dataset == "dancetrack_val":
VIDEO_LEN = os.listdir(tracks_dir)
VIDEO_LEN = [d for d in VIDEO_LEN if "dancetrack" in d]
elif dataset == "dancetrack_test":
VIDEO_LEN = os.listdir(tracks_dir)
VIDEO_LEN = [d for d in VIDEO_LEN if "dancetrack" in d]
# import pdb; pdb.set_trace()
for video_name in VIDEO_LEN:
video_name = video_name.replace(".txt", "")
track_f = os.path.join(tracks_dir, "{}.txt".format(video_name))
f = open(track_f)
tracks = np.loadtxt(f, delimiter=",")
if dataset == "mot17":
frame_range = MOT17_VIDEO_SPLIT[video_name][mode]
elif dataset == "mot20":
frame_range = MOT20_VIDEO_SPLIT[video_name]["full"]
elif dataset == "dancetrack_val":
frame_range = [tracks[:,0].min(), tracks[:,0].max()]
elif dataset == "dancetrack_test":
frame_range = [tracks[:,0].min(), tracks[:,0].max()]
assert(frame_range[1]-frame_range[0] == tracks[:, 0].max()-tracks[:,0].min())
frame_gap = tracks[:,0].min() - frame_range[0]
video_img_dir = os.path.join(img_dir, video_name, "img1")
video_out_dir = os.path.join(out_dir, video_name)
os.makedirs(video_out_dir, exist_ok=True)
fake_frame_min = int(tracks[:,0].min())
fake_frame_max = int(tracks[:,0].max())
for frame_ind in range(fake_frame_min, fake_frame_max+1):
real_frame_ind = frame_ind - frame_gap
frame_tracks = tracks[np.where(tracks[:,0]==frame_ind)]
if "dancetrack" in dataset:
im_path = os.path.join(video_img_dir, "%08d.jpg" % real_frame_ind)
else:
im_path = os.path.join(video_img_dir, "%06d.jpg" % real_frame_ind)
img = cv2.imread(im_path)
for i in range(frame_tracks.shape[0]):
box = frame_tracks[i]
obj_id = int(box[1])
text = '{}'.format(obj_id)
img = visualize_box(img, text, box[2:6], obj_id)
'''
x0, y0, width, height = box[2:6]
score = box[6]
x0, y0, width, height = int(x0), int(y0), int(width), int(height)
obj_id = int(obj_id)
color = (_COLORS[obj_id%80] * 255).astype(np.uint8).tolist()
txt_color = (0, 0, 0) if np.mean(_COLORS[obj_id%80]) > 0.5 else (255, 255, 255)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)
txt_bk_color = (_COLORS[obj_id%80] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(
img,
(x0, y0 + 1),
(x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),
txt_bk_color,
-1
)
cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)
'''
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)
if "dancetrack" in dataset:
cv2.putText(img, "%08d.jpg" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)
# import pdb; pdb.set_trace()
cv2.imwrite(os.path.join(video_out_dir, "%08d.jpg" % real_frame_ind), img)
else:
cv2.putText(img, "%06d.jpg" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)
cv2.imwrite(os.path.join(video_out_dir, "%06d.jpg" % real_frame_ind), img)
cmd = "ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/{}.mp4".format(video_out_dir, out_dir, video_name)
os.popen(cmd)
def visualize_gt(img_dir, out_dir):
for video_name in VIDEO_LEN:
track_f = os.path.join(img_dir, video_name, "gt/gt.txt")
f = open(track_f)
tracks = np.loadtxt(f, delimiter=",")
video_img_dir = os.path.join(img_dir, video_name, "img1")
video_out_dir = os.path.join(out_dir, video_name)
os.makedirs(video_out_dir, exist_ok=True)
fake_frame_min = int(tracks[:,0].min())
fake_frame_max = int(tracks[:,0].max())
for frame_ind in range(fake_frame_min, fake_frame_max+1):
real_frame_ind = frame_ind
frame_tracks = tracks[np.where(tracks[:,0]==frame_ind)]
im_path = os.path.join(video_img_dir, "%06d.jpg" % real_frame_ind)
img = cv2.imread(im_path)
for i in range(frame_tracks.shape[0]):
box = frame_tracks[i]
obj_id = int(box[1])
text = '{}'.format(obj_id)
img = visualize_box(img, text, box[2:6], obj_id)
'''
x0, y0, width, height = box[2:6]
score = box[6]
x0, y0, width, height = int(x0), int(y0), int(width), int(height)
obj_id = int(obj_id)
color = (_COLORS[obj_id%80] * 255).astype(np.uint8).tolist()
txt_color = (0, 0, 0) if np.mean(_COLORS[obj_id%80]) > 0.5 else (255, 255, 255)
font = cv2.FONT_HERSHEY_SIMPLEX
txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
cv2.rectangle(img, (x0, y0), (x0+width, y0+height), color, 2)
txt_bk_color = (_COLORS[obj_id%80] * 255 * 0.7).astype(np.uint8).tolist()
cv2.rectangle(
img,
(x0, y0 + 1),
(x0 + txt_size[0] + 1, y0 + int(1.5*txt_size[1])),
txt_bk_color,
-1
)
cv2.putText(img, text, (x0, y0 + txt_size[1]), font, 0.4, txt_color, thickness=1)
'''
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.rectangle(img, (2, 2), (120, 30), (30,30,30), -1)
cv2.putText(img, "%06d.jpg" % real_frame_ind, (10, 20), font, 0.6, (255,255,255), thickness=2)
cv2.imwrite(os.path.join(video_out_dir, "%06d.jpg" % real_frame_ind), img)
cmd = "ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/{}.mp4".format(video_out_dir, out_dir, video_name)
os.popen(cmd)
def merge_visualization(det_dir, track_dir, gt_dir, out_dir):
os.makedirs(out_dir, exist_ok=True)
seqs = os.listdir(track_dir)
for seq in seqs:
if "mp4" in seq:
continue
seq_track_dir = os.path.join(track_dir, seq)
seq_det_dir = os.path.join(det_dir, seq)
seq_gt_dir = os.path.join(gt_dir, seq)
seq_out_dir = os.path.join(out_dir, seq)
os.makedirs(seq_out_dir, exist_ok=True)
frames = sorted(os.listdir(seq_track_dir))
for frame in frames:
f_track_path = os.path.join(seq_track_dir, frame)
f_det_path = os.path.join(seq_det_dir, frame)
f_gt_path = os.path.join(seq_gt_dir, frame)
im1 = cv2.imread(f_track_path)
im2 = cv2.imread(f_det_path)
im3 = cv2.imread(f_gt_path)
im_concat = cv2.vconcat([im2, im3, im1])
f_out_dir = os.path.join(seq_out_dir, frame)
cv2.imwrite(f_out_dir, im_concat)
cmd = "ffmpeg -framerate 5 -pattern_type glob -i '{}/*.jpg' -c:v libx264 -pix_fmt yuv420p {}/merged_{}.mp4".format(seq_out_dir, out_dir, seq)
os.popen(cmd)
def make_parser():
parser = argparse.ArgumentParser("Visualize Results")
parser.add_argument('--mode', default="val_half", type=str)
parser.add_argument('--img_dir', default="datasets/mot/train")
parser.add_argument('--exp_dir', default="yolox_x_ablation", type=str)
parser.add_argument('--exp_name', default="track_results", type=str)
parser.add_argument('--vis', default="det", type=str, help="det/track/gt")
parser.add_argument("--dataset", default="mot17", type=str)
parser.add_argument("--res", type=str)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = make_parser()
if args.dataset == "mot17":
img_dir = "datasets/mot/train"
elif args.dataset == "mot20":
img_dir = "datasets/MOT20/test"
elif args.dataset == "dancetrack_val":
img_dir = "datasets/dancetrack/val"
elif args.dataset == "dancetrack_test":
img_dir = "datasets/dancetrack/test"
# result_src_dir = "YOLOX_outputs/"
# result_src_dir = "evaldata/trackers/mot_challenge/MOT17-val/"
res_dir = args.res
out_src_dir = "visualizations"
# exp_dir = args.exp_dir
exp_name = args.exp_name
# res_dir = os.path.join(result_src_dir, exp_name, "data")
out_dir = os.path.join(out_src_dir, args.dataset, exp_name, args.vis)
os.makedirs(out_dir, exist_ok=True)
if args.vis == "det":
visualize_detections(img_dir, out_dir, res_dir, mode=args.mode)
elif args.vis == "track":
visualize_tracks(img_dir, out_dir, res_dir, mode=args.mode, dataset=args.dataset)
elif args.vis == "merge":
det_dir = "visualizations/yolox_x_ablation/{}/det".format(args.exp_name)
track_dir = "visualizations/yolox_x_ablation/{}/track".format(args.exp_name)
gt_dir = "visualizations/GTs"
out_dir = "visualizations/yolox_x_ablation/{}/merged".format(args.exp_name)
merge_visualization(det_dir, track_dir, gt_dir, out_dir)
elif args.vis == "det_fasterrcnn":
res_dir = "datasets/mot/train"
out_dir = "visualizations/mot17/fasterrcnn_dets"
visualize_detections(img_dir, out_dir, res_dir, mode="full", path="{}/{}/det/det.txt")
elif args.vis == "gt":
out_dir = os.path.join(out_src_dir, "GTs")
os.makedirs(out_dir, exist_ok=True)
visualize_gt(img_dir, out_dir)