def play(mot_folder, image_folder):
sequence_name = [
os.path.basename(s) for s in glob.glob(os.path.join(image_folder, "*"))
]
for s in sequence_name:
print("{} :================".format(s))
s_image_folder = os.path.join(image_folder, s)
s_mot_file = os.path.join(mot_folder, s + ".txt")
if not os.path.exists(s_mot_file):
print("cannot find {}".format(s_mot_file))
continue
data = pd.read_csv(s_mot_file, header=None)
data_group = data.groupby(by=[0])
frame_index_list = sorted(data_group.groups.keys())
for frame_index in frame_index_list:
print(frame_index)
value = data.iloc[data_group.groups[frame_index], :].values
ids = value[:, [1]].astype(int).squeeze(1)
bboxes = value[:, [2, 3, 4, 5]]
conf = value[:, [6]].squeeze(1)
image_path = os.path.join(s_image_folder,
"img{0:05}.jpg".format(frame_index))
image = cv2.imread(image_path)
if image is None:
print("cannot find {}".format(image_path))
bboxes[:, [2, 3]] += bboxes[:, [0, 1]]
if image is None:
continue
bboxes[:, [0, 2]] = np.clip(bboxes[:, [0, 2]],
a_min=0,
a_max=image.shape[1])
bboxes[:, [1, 3]] = np.clip(bboxes[:, [1, 3]],
a_min=0,
a_max=image.shape[0])
bboxes_valid = (bboxes[:, [2, 3]] -
bboxes[:, [0, 1]] == 0).sum(axis=1) == 0
bboxes = bboxes[bboxes_valid, :]
colors = [DrawBoxes.get_random_color(i) for i in ids[bboxes_valid]]
texts = ["{}".format(round(c, 2)) for c in conf]
DrawBoxes.cv_draw_mult_boxes(image,
bboxes,
colors=colors,
texts=texts)
cv2.imshow("result", image)
cv2.waitKey(-1)
def test():
net.eval()
data_loader = data.DataLoader(dataset=dataset,
batch_size=1,
num_workers=1,
shuffle=True,
collate_fn=collate_fn,
pin_memory=False)
batch_iterator = iter(data_loader)
for index in range(len(data_loader)):
frames_1, target_1, times_1 = next(batch_iterator)
# if index % 32 != 0:
# continue
if frames_1 is None:
continue
if args.cuda:
frames_1 = Variable(frames_1.cuda())
with torch.no_grad():
target_1 = [[Variable(target[j].cuda()) for j in range(4)]
for target in target_1]
times_1 = Variable(times_1.cuda())
else:
pass
output_params, output_p_c, output_p_e, output_boxes = net(
frames_1, times_1)
batch_boxes = []
batch_num = output_params.shape[0]
class_num = output_params.size(1)
result = []
for b in range(batch_num):
boxes = []
for c in range(1, class_num):
mask = output_p_c[b, c, :] > 0
result += [[
output_params[b, c, mask, :].data,
output_p_c[b, c, mask].data, output_p_e[b, c, :,
mask].data,
output_boxes[b, c, :, mask, :].data, c
]]
# draw something on the image
for r in result:
all_motion_parameters = r[0]
all_p_c = r[1]
all_p_e = r[2]
all_bboxes_ = r[3]
all_c = r[4]
# draw boxes
for i in range(frames_1.shape[2]):
frame = TypeConverter.image_tensor_2_cv_gpu(frames_1[0, :,
i, :, :])
all_bboxes = TypeConverter.tensor_2_numpy_gpu(all_bboxes_)
frame = cv2.resize(frame, (1920, 960))
h, w, c = frame.shape
all_bboxes[:, :, [0, 2]] *= w
all_bboxes[:, :, [1, 3]] *= h
colors = []
texts = []
for c, e in zip(all_p_c, all_p_e[i, :]):
if e > 0.5:
colors += [(0, 0, 255)]
texts += ["{:.2}, {:.2}".format(c, e)]
else:
colors += [(255, 255, 255)]
texts += ["NO-"]
DrawBoxes.cv_draw_mult_boxes_with_track(
frame, all_bboxes, i, colors, texts)
if cfg['debug_save_image']:
if not os.path.exists(cfg["image_save_folder"]):
os.makedirs(cfg["image_save_folder"])
cv2.imshow("result", frame)
cv2.waitKey(10)
cv2.imwrite(
os.path.join(
cfg["image_save_folder"],
"{}-{}-{}.png".format(index, result.index(r), i)),
frame)
if self.transform is not None:
out = self.transform(out)
return out
if __name__ == "__main__":
from draw_utils.DrawBoxes import DrawBoxes
dataset = MOT17TrainDataset()
for index in range(0, len(dataset), 32):
frame_indexes, track_ids, bboxes, frames, times = dataset[index]
if frame_indexes is None:
continue
for i, frame in enumerate(frames):
h, w, _ = frame.shape
label_map = {v: k for k, v in config["label_map"].items()}
texts = [label_map[c] for c in bboxes[i, :, -1].astype(int)]
DrawBoxes.cv_draw_mult_boxes_with_track(
frame,
bboxes[:, :, :4] * np.array([w, h, w, h]),
i,
texts=texts,
exists=bboxes[:, :, -2].astype(int))
cv2.imshow("result", frame)
cv2.waitKey(25)
return out
if __name__ == "__main__":
from draw_utils.DrawBoxes import DrawBoxes
dataset = AmotTrainDataset()
# dataset.get_mean_pixel()
for index in range(544, len(dataset), 32):
frame_indexes, track_ids, bboxes, frames, times = dataset[index]
if frame_indexes is None:
continue
for i, frame in enumerate(frames):
h, w, _ = frame.shape
label_map = {v: k for k, v in config["label_map"].items()}
texts = ['' if c==0 else label_map[c] for c in bboxes[i, :, -1].astype(int)]
colors = []
for t in track_ids:
colors += [DrawBoxes.get_random_color(t)]
DrawBoxes.cv_draw_mult_boxes_with_track(frame,
bboxes[:, :, :4]*np.array([w, h, w, h]),
i,
colors=colors,
texts=texts,
exists=bboxes[:, :, -2].astype(int))
cv2.imshow("result", frame)
cv2.waitKey(25)
def draw(self, frames, track_id):
# print(self.category)
DrawBoxes.draw_node_result(frames, self.boxes, self.p_c, self.p_e,
Config.category_map[self.category], track_id)
return frames