class Detector(object):
def __init__(self, opt):
if opt.gpus[0] >= 0:
opt.device = torch.device('cuda')
else:
opt.device = torch.device('cpu')
print('Creating model...')
self.model = create_model(opt.arch, opt.heads, opt.head_conv, opt=opt)
self.model = load_model(self.model, opt.load_model, opt)
self.model = self.model.to(opt.device)
self.model.eval()
self.opt = opt
self.trained_dataset = get_dataset(opt.dataset)
self.mean = np.array(self.trained_dataset.mean,
dtype=np.float32).reshape(1, 1, 3)
self.std = np.array(self.trained_dataset.std,
dtype=np.float32).reshape(1, 1, 3)
self.pause = not opt.no_pause
self.rest_focal_length = self.trained_dataset.rest_focal_length \
if self.opt.test_focal_length < 0 else self.opt.test_focal_length
self.flip_idx = self.trained_dataset.flip_idx
self.cnt = 0
self.pre_images = None
self.pre_image_ori = None
self.tracker = Tracker(opt)
self.debugger = Debugger(opt=opt, dataset=self.trained_dataset)
def run(self, image_or_path_or_tensor, meta={}):
load_time, pre_time, net_time, dec_time, post_time = 0, 0, 0, 0, 0
merge_time, track_time, tot_time, display_time = 0, 0, 0, 0
self.debugger.clear()
start_time = time.time()
# read image
pre_processed = False
if isinstance(image_or_path_or_tensor, np.ndarray):
image = image_or_path_or_tensor
elif type(image_or_path_or_tensor) == type(''):
image = cv2.imread(image_or_path_or_tensor)
else:
image = image_or_path_or_tensor['image'][0].numpy()
pre_processed_images = image_or_path_or_tensor
pre_processed = True
loaded_time = time.time()
load_time += (loaded_time - start_time)
detections = []
# for multi-scale testing
for scale in self.opt.test_scales:
scale_start_time = time.time()
if not pre_processed:
# not prefetch testing or demo
images, meta = self.pre_process(image, scale, meta)
else:
# prefetch testing
images = pre_processed_images['images'][scale][0]
meta = pre_processed_images['meta'][scale]
meta = {k: v.numpy()[0] for k, v in meta.items()}
if 'pre_dets' in pre_processed_images['meta']:
meta['pre_dets'] = pre_processed_images['meta']['pre_dets']
if 'cur_dets' in pre_processed_images['meta']:
meta['cur_dets'] = pre_processed_images['meta']['cur_dets']
images = images.to(self.opt.device,
non_blocking=self.opt.non_block_test)
# initializing tracker
pre_hms, pre_inds = None, None
if self.opt.tracking:
# initialize the first frame
if self.pre_images is None:
print('Initialize tracking!')
self.pre_images = images
self.tracker.init_track(meta['pre_dets'] if 'pre_dets' in
meta else [])
if self.opt.pre_hm:
# render input heatmap from tracker status
# pre_inds is not used in the current version.
# We used pre_inds for learning an offset from previous image to
# the current image.
pre_hms, pre_inds = self._get_additional_inputs(
self.tracker.tracks,
meta,
with_hm=not self.opt.zero_pre_hm)
pre_process_time = time.time()
pre_time += pre_process_time - scale_start_time
# run the network
# output: the output feature maps, only used for visualizing
# dets: output tensors after extracting peaks
output, dets, forward_time = self.process(images,
self.pre_images,
pre_hms,
pre_inds,
return_time=True)
net_time += forward_time - pre_process_time
decode_time = time.time()
dec_time += decode_time - forward_time
# convert the cropped and 4x downsampled output coordinate system
# back to the input image coordinate system
result = self.post_process(dets, meta, scale)
post_process_time = time.time()
post_time += post_process_time - decode_time
detections.append(result)
if self.opt.debug >= 2:
self.debug(self.debugger,
images,
result,
output,
scale,
pre_images=self.pre_images
if not self.opt.no_pre_img else None,
pre_hms=pre_hms)
# merge multi-scale testing results
results = self.merge_outputs(detections)
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
end_time = time.time()
merge_time += end_time - post_process_time
if self.opt.tracking:
# public detection mode in MOT challenge
public_det = meta['cur_dets'] if self.opt.public_det else None
# add tracking id to results
results = self.tracker.step(results, public_det)
self.pre_images = images
tracking_time = time.time()
track_time += tracking_time - end_time
tot_time += tracking_time - start_time
if self.opt.debug >= 1:
self.show_results(self.debugger, image, results)
self.cnt += 1
show_results_time = time.time()
display_time += show_results_time - end_time
# return results and run time
ret = {
'results': results,
'tot': tot_time,
'load': load_time,
'pre': pre_time,
'net': net_time,
'dec': dec_time,
'post': post_time,
'merge': merge_time,
'track': track_time,
'display': display_time
}
if self.opt.save_video:
try:
# return debug image for saving video
ret.update({'generic': self.debugger.imgs['generic']})
except:
pass
return ret
def _transform_scale(self, image, scale=1):
'''
Prepare input image in different testing modes.
Currently support: fix short size/ center crop to a fixed size/
keep original resolution but pad to a multiplication of 32
'''
height, width = image.shape[0:2]
new_height = int(height * scale)
new_width = int(width * scale)
if self.opt.fix_short > 0:
if height < width:
inp_height = self.opt.fix_short
inp_width = (int(width / height * self.opt.fix_short) +
63) // 64 * 64
else:
inp_height = (int(height / width * self.opt.fix_short) +
63) // 64 * 64
inp_width = self.opt.fix_short
c = np.array([width / 2, height / 2], dtype=np.float32)
s = np.array([width, height], dtype=np.float32)
elif self.opt.fix_res:
inp_height, inp_width = self.opt.input_h, self.opt.input_w
c = np.array([new_width / 2., new_height / 2.], dtype=np.float32)
s = max(height, width) * 1.0
# s = np.array([inp_width, inp_height], dtype=np.float32)
else:
inp_height = (new_height | self.opt.pad) + 1
inp_width = (new_width | self.opt.pad) + 1
c = np.array([new_width // 2, new_height // 2], dtype=np.float32)
s = np.array([inp_width, inp_height], dtype=np.float32)
resized_image = cv2.resize(image, (new_width, new_height))
return resized_image, c, s, inp_width, inp_height, height, width
def pre_process(self, image, scale, input_meta={}):
'''
Crop, resize, and normalize image. Gather meta data for post processing
and tracking.
'''
resized_image, c, s, inp_width, inp_height, height, width = \
self._transform_scale(image)
trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height])
out_height = inp_height // self.opt.down_ratio
out_width = inp_width // self.opt.down_ratio
trans_output = get_affine_transform(c, s, 0, [out_width, out_height])
inp_image = cv2.warpAffine(resized_image,
trans_input, (inp_width, inp_height),
flags=cv2.INTER_LINEAR)
inp_image = ((inp_image / 255. - self.mean) / self.std).astype(
np.float32)
images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height,
inp_width)
if self.opt.flip_test:
images = np.concatenate((images, images[:, :, :, ::-1]), axis=0)
images = torch.from_numpy(images)
meta = {'calib': np.array(input_meta['calib'], dtype=np.float32) \
if 'calib' in input_meta else \
self._get_default_calib(width, height)}
meta.update({
'c': c,
's': s,
'height': height,
'width': width,
'out_height': out_height,
'out_width': out_width,
'inp_height': inp_height,
'inp_width': inp_width,
'trans_input': trans_input,
'trans_output': trans_output
})
if 'pre_dets' in input_meta:
meta['pre_dets'] = input_meta['pre_dets']
if 'cur_dets' in input_meta:
meta['cur_dets'] = input_meta['cur_dets']
return images, meta
def _trans_bbox(self, bbox, trans, width, height):
'''
Transform bounding boxes according to image crop.
'''
bbox = np.array(copy.deepcopy(bbox), dtype=np.float32)
bbox[:2] = affine_transform(bbox[:2], trans)
bbox[2:] = affine_transform(bbox[2:], trans)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, width - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, height - 1)
return bbox
def _get_additional_inputs(self, dets, meta, with_hm=True):
'''
Render input heatmap from previous trackings.
'''
trans_input, trans_output = meta['trans_input'], meta['trans_output']
inp_width, inp_height = meta['inp_width'], meta['inp_height']
out_width, out_height = meta['out_width'], meta['out_height']
input_hm = np.zeros((1, inp_height, inp_width), dtype=np.float32)
output_inds = []
for det in dets:
if det['score'] < self.opt.pre_thresh or det['active'] == 0:
continue
bbox = self._trans_bbox(det['bbox'], trans_input, inp_width,
inp_height)
bbox_out = self._trans_bbox(det['bbox'], trans_output, out_width,
out_height)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if (h > 0 and w > 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
if with_hm:
draw_umich_gaussian(input_hm[0], ct_int, radius)
ct_out = np.array([(bbox_out[0] + bbox_out[2]) / 2,
(bbox_out[1] + bbox_out[3]) / 2],
dtype=np.int32)
output_inds.append(ct_out[1] * out_width + ct_out[0])
if with_hm:
input_hm = input_hm[np.newaxis]
if self.opt.flip_test:
input_hm = np.concatenate((input_hm, input_hm[:, :, :, ::-1]),
axis=0)
input_hm = torch.from_numpy(input_hm).to(self.opt.device)
output_inds = np.array(output_inds, np.int64).reshape(1, -1)
output_inds = torch.from_numpy(output_inds).to(self.opt.device)
return input_hm, output_inds
def _get_default_calib(self, width, height):
calib = np.array([[self.rest_focal_length, 0, width / 2, 0],
[0, self.rest_focal_length, height / 2, 0],
[0, 0, 1, 0]])
return calib
def _sigmoid_output(self, output):
if 'hm' in output:
output['hm'] = output['hm'].sigmoid_()
if 'hm_hp' in output:
output['hm_hp'] = output['hm_hp'].sigmoid_()
if 'dep' in output:
output['dep'] = 1. / (output['dep'].sigmoid() + 1e-6) - 1.
output['dep'] *= self.opt.depth_scale
return output
def _flip_output(self, output):
average_flips = ['hm', 'wh', 'dep', 'dim'] ##TODO consider tracking_wh
neg_average_flips = ['amodel_offset']
single_flips = [
'ltrb', 'nuscenes_att', 'velocity', 'ltrb_amodal', 'reg',
'hp_offset', 'rot', 'tracking', 'pre_hm'
] ## TODO consider iou
for head in output:
if head in average_flips:
output[head] = (output[head][0:1] +
flip_tensor(output[head][1:2])) / 2
if head in neg_average_flips:
flipped_tensor = flip_tensor(output[head][1:2])
flipped_tensor[:, 0::2] *= -1
output[head] = (output[head][0:1] + flipped_tensor) / 2
if head in single_flips:
output[head] = output[head][0:1]
if head == 'hps':
output['hps'] = (output['hps'][0:1] + flip_lr_off(
output['hps'][1:2], self.flip_idx)) / 2
if head == 'hm_hp':
output['hm_hp'] = (output['hm_hp'][0:1] + \
flip_lr(output['hm_hp'][1:2], self.flip_idx)) / 2
return output
def process(self,
images,
pre_images=None,
pre_hms=None,
pre_inds=None,
return_time=False):
with torch.no_grad():
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
output = self.model(images, pre_images, pre_hms)[-1]
output = self._sigmoid_output(output)
output.update({'pre_inds': pre_inds})
if self.opt.flip_test:
output = self._flip_output(output)
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
forward_time = time.time()
dets = generic_decode(output, K=self.opt.K, opt=self.opt)
if self.opt.gpus[0] >= 0:
torch.cuda.synchronize()
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
if return_time:
return output, dets, forward_time
else:
return output, dets
def post_process(self, dets, meta, scale=1):
dets = generic_post_process(self.opt, dets, [meta['c']], [meta['s']],
meta['out_height'], meta['out_width'],
self.opt.num_classes, [meta['calib']],
meta['height'], meta['width'])
self.this_calib = meta['calib']
if scale != 1:
for i in range(len(dets[0])):
for k in ['bbox', 'hps']:
if k in dets[0][i]:
dets[0][i][k] = (np.array(dets[0][i][k], np.float32) /
scale).tolist()
return dets[0]
def merge_outputs(self, detections):
assert len(self.opt.test_scales) == 1, 'multi_scale not supported!'
results = []
for i in range(len(detections[0])):
if detections[0][i]['score'] > self.opt.out_thresh:
results.append(detections[0][i])
return results
def debug(self,
debugger,
images,
dets,
output,
scale=1,
pre_images=None,
pre_hms=None):
img = images[0].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * self.std + self.mean) * 255.), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(output['hm'][0].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hm')
if 'hm_hp' in output:
pred = debugger.gen_colormap_hp(
output['hm_hp'][0].detach().cpu().numpy())
debugger.add_blend_img(img, pred, 'pred_hmhp')
if pre_images is not None:
pre_img = pre_images[0].detach().cpu().numpy().transpose(1, 2, 0)
pre_img = np.clip(((pre_img * self.std + self.mean) * 255.), 0,
255).astype(np.uint8)
debugger.add_img(pre_img, 'pre_img')
if pre_hms is not None:
pre_hm = debugger.gen_colormap(
pre_hms[0].detach().cpu().numpy())
debugger.add_blend_img(pre_img, pre_hm, 'pre_hm')
def show_results(self, debugger, image, results):
debugger.add_img(image, img_id='generic')
# if self.opt.tracking:
# debugger.add_img(self.pre_image_ori if self.pre_image_ori is not None else image,
# img_id='previous')
# self.pre_image_ori = image
for j in range(len(results)):
if results[j]['score'] > self.opt.vis_thresh:
if 'active' in results[j] and results[j]['active'] == 0:
continue
item = results[j]
if ('bbox' in item):
sc = item['score'] if self.opt.demo == '' or \
not ('tracking_id' in item) else item['tracking_id']
sc = item[
'tracking_id'] if self.opt.show_track_color else sc
debugger.add_coco_bbox(item['bbox'],
item['class'] - 1,
sc,
img_id='generic')
if 'tracking' in item:
debugger.add_arrow(item['ct'],
item['tracking'],
img_id='generic')
tracking_id = item[
'tracking_id'] if 'tracking_id' in item else -1
if 'tracking_id' in item and self.opt.demo == '' and \
not self.opt.show_track_color:
debugger.add_tracking_id(item['ct'],
item['tracking_id'],
img_id='generic')
if (item['class'] in [1, 2]) and 'hps' in item:
debugger.add_coco_hp(item['hps'],
tracking_id=tracking_id,
img_id='generic')
if len(results) > 0 and \
'dep' in results[0] and 'alpha' in results[0] and 'dim' in results[0]:
debugger.add_3d_detection(
image if not self.opt.qualitative else cv2.resize(
debugger.imgs['pred_hm'],
(image.shape[1], image.shape[0])),
False,
results,
self.this_calib,
vis_thresh=self.opt.vis_thresh,
img_id='ddd_pred')
debugger.add_bird_view(results,
vis_thresh=self.opt.vis_thresh,
img_id='bird_pred',
cnt=self.cnt)
if self.opt.show_track_color and self.opt.debug == 4:
del debugger.imgs['generic'], debugger.imgs['bird_pred']
if 'ddd_pred' in debugger.imgs:
debugger.imgs['generic'] = debugger.imgs['ddd_pred']
if self.opt.debug == 4:
debugger.save_all_imgs(self.opt.debug_dir,
prefix='{}'.format(self.cnt))
else:
debugger.show_all_imgs(pause=self.pause)
def reset_tracking(self):
self.tracker.reset()
self.pre_images = None
self.pre_image_ori = None
class Detector(object):
def __init__(self, opt):
if opt.gpus[0] >= 0:
opt.device = torch.device("cuda")
else:
opt.device = torch.device("cpu")
print("Creating model...")
self.model = create_model(opt.arch, opt.heads, opt.head_conv, opt=opt)
self.model = load_model(self.model, opt.load_model, opt)
self.model = self.model.to(opt.device)
self.model.eval()
self.opt = opt
self.trained_dataset = get_dataset(opt.dataset)
self.mean = np.array(self.trained_dataset.mean,
dtype=np.float32).reshape(1, 1, 3)
self.std = np.array(self.trained_dataset.std,
dtype=np.float32).reshape(1, 1, 3)
# self.pause = not opt.no_pause
self.rest_focal_length = (self.trained_dataset.rest_focal_length
if self.opt.test_focal_length < 0 else
self.opt.test_focal_length)
self.flip_idx = self.trained_dataset.flip_idx
self.cnt = 0
self.pre_images = None
self.pre_image_ori = None
self.dataset = opt.dataset
if self.dataset == "nuscenes":
self.tracker = {}
for class_name in NUSCENES_TRACKING_NAMES:
self.tracker[class_name] = Tracker(opt, self.model)
else:
self.tracker = Tracker(opt, self.model)
self.debugger = Debugger(opt=opt, dataset=self.trained_dataset)
self.img_height = 100
self.img_width = 100
def run(self, image_or_path_or_tensor, meta={}, image_info=None):
load_time, pre_time, net_time, dec_time, post_time = 0, 0, 0, 0, 0
merge_time, track_time, tot_time, display_time = 0, 0, 0, 0
self.debugger.clear()
start_time = time.time()
# read image
pre_processed = False
if isinstance(image_or_path_or_tensor, np.ndarray):
image = image_or_path_or_tensor
elif type(image_or_path_or_tensor) == type(""):
image = cv2.imread(image_or_path_or_tensor)
else:
image = image_or_path_or_tensor["image"][0].numpy()
pre_processed_images = image_or_path_or_tensor
pre_processed = True
loaded_time = time.time()
load_time += loaded_time - start_time
detections = []
# for multi-scale testing
for scale in self.opt.test_scales:
scale_start_time = time.time()
if not pre_processed:
# not prefetch testing or demo
images, meta = self.pre_process(image, scale, meta)
else:
# prefetch testing
images = pre_processed_images["images"][scale][0]
meta = pre_processed_images["meta"][scale]
meta = {k: v.numpy()[0] for k, v in meta.items()}
if "pre_dets" in pre_processed_images["meta"]:
meta["pre_dets"] = pre_processed_images["meta"]["pre_dets"]
if "cur_dets" in pre_processed_images["meta"]:
meta["cur_dets"] = pre_processed_images["meta"]["cur_dets"]
images = images.to(self.opt.device,
non_blocking=self.opt.non_block_test)
# initializing tracker
pre_hms, pre_inds = None, None
pre_process_time = time.time()
pre_time += pre_process_time - scale_start_time
# run the network
# output: the output feature maps, only used for visualizing
# dets: output tensors after extracting peaks
output, dets, forward_time, FeatureMaps = self.process(
images, self.pre_images, pre_hms, pre_inds, return_time=True)
net_time += forward_time - pre_process_time
decode_time = time.time()
dec_time += decode_time - forward_time
# convert the cropped and 4x downsampled output coordinate system
# back to the input image coordinate system
result = self.post_process(dets, meta, scale)
post_process_time = time.time()
post_time += post_process_time - decode_time
detections.append(result)
if self.opt.debug >= 2:
self.debug(
self.debugger,
images,
result,
output,
scale,
pre_images=self.pre_images
if not self.opt.no_pre_img else None,
pre_hms=pre_hms,
)
# merge multi-scale testing results
results = self.merge_outputs(detections)
torch.cuda.synchronize()
end_time = time.time()
merge_time += end_time - post_process_time
# public detection mode in MOT challenge
if self.opt.public_det:
results = (pre_processed_images["meta"]["cur_dets"]
if self.opt.public_det else None)
if self.dataset == "nuscenes":
trans_matrix = np.array(image_info["trans_matrix"], np.float32)
results_by_class = {}
ddd_boxes_by_class = {}
depths_by_class = {}
ddd_boxes_by_class2 = {}
ddd_org_boxes_by_class = {}
ddd_box_submission1 = {}
ddd_box_submission2 = {}
for class_name in NUSCENES_TRACKING_NAMES:
results_by_class[class_name] = []
ddd_boxes_by_class2[class_name] = []
ddd_boxes_by_class[class_name] = []
depths_by_class[class_name] = []
ddd_org_boxes_by_class[class_name] = []
ddd_box_submission1[class_name] = []
ddd_box_submission2[class_name] = []
for det in results:
cls_id = int(det["class"])
class_name = nuscenes_class_name[cls_id - 1]
if class_name not in NUSCENES_TRACKING_NAMES:
continue
if det["score"] < 0.3:
continue
if class_name == "pedestrian" and det["score"] < 0.35:
continue
results_by_class[class_name].append(det["bbox"].tolist() +
[det["score"]])
size = [
float(det["dim"][1]),
float(det["dim"][2]),
float(det["dim"][0]),
]
rot_cam = Quaternion(axis=[0, 1, 0], angle=det["rot_y"])
translation_submission1 = np.dot(
trans_matrix,
np.array(
[
det["loc"][0], det["loc"][1] - size[2],
det["loc"][2], 1
],
np.float32,
),
).copy()
loc = np.array([det["loc"][0], det["loc"][1], det["loc"][2]],
np.float32)
depths_by_class[class_name].append([float(det["loc"][2])
].copy())
trans = [det["loc"][0], det["loc"][1], det["loc"][2]]
ddd_org_boxes_by_class[class_name].append([
float(det["dim"][0]),
float(det["dim"][1]),
float(det["dim"][2])
] + trans + [det["rot_y"]])
box = Box(loc, size, rot_cam, name="2", token="1")
box.translate(np.array([0, -box.wlh[2] / 2, 0]))
box.rotate(Quaternion(image_info["cs_record_rot"]))
box.translate(np.array(image_info["cs_record_trans"]))
box.rotate(Quaternion(image_info["pose_record_rot"]))
box.translate(np.array(image_info["pose_record_trans"]))
rotation = box.orientation
rotation = [
float(rotation.w),
float(rotation.x),
float(rotation.y),
float(rotation.z),
]
ddd_box_submission1[class_name].append([
float(translation_submission1[0]),
float(translation_submission1[1]),
float(translation_submission1[2]),
].copy() + size.copy() + rotation.copy())
q = Quaternion(rotation)
angle = q.angle if q.axis[2] > 0 else -q.angle
ddd_boxes_by_class[class_name].append([
size[2],
size[0],
size[1],
box.center[0],
box.center[1],
box.center[2],
angle,
].copy())
online_targets = []
for class_name in NUSCENES_TRACKING_NAMES:
if len(results_by_class[class_name]) > 0 and NMS:
boxess = torch.from_numpy(
np.array(results_by_class[class_name])[:, :4])
scoress = torch.from_numpy(
np.array(results_by_class[class_name])[:, -1])
if class_name == "bus" or class_name == "truck":
ovrlp = 0.7
else:
ovrlp = 0.8
keep, count = nms(boxess, scoress, overlap=ovrlp)
keep = keep.data.numpy().tolist()
keep = sorted(set(keep))
results_by_class[class_name] = np.array(
results_by_class[class_name])[keep]
ddd_boxes_by_class[class_name] = np.array(
ddd_boxes_by_class[class_name])[keep]
depths_by_class[class_name] = np.array(
depths_by_class[class_name])[keep]
ddd_org_boxes_by_class[class_name] = np.array(
ddd_org_boxes_by_class[class_name])[keep]
ddd_box_submission1[class_name] = np.array(
ddd_box_submission1[class_name])[keep]
online_targets += self.tracker[class_name].update(
results_by_class[class_name],
FeatureMaps,
ddd_boxes=ddd_boxes_by_class[class_name],
depths_by_class=depths_by_class[class_name],
ddd_org_boxes=ddd_org_boxes_by_class[class_name],
submission=ddd_box_submission1[class_name],
classe=class_name,
)
else:
online_targets = self.tracker.update(results, FeatureMaps)
return online_targets
def _transform_scale(self, image, scale=1):
"""
Prepare input image in different testing modes.
Currently support: fix short size/ center crop to a fixed size/
keep original resolution but pad to a multiplication of 32
"""
height, width = image.shape[0:2]
new_height = int(height * scale)
new_width = int(width * scale)
if self.opt.fix_short > 0:
if height < width:
inp_height = self.opt.fix_short
inp_width = (int(width / height * self.opt.fix_short) +
63) // 64 * 64
else:
inp_height = (int(height / width * self.opt.fix_short) +
63) // 64 * 64
inp_width = self.opt.fix_short
c = np.array([width / 2, height / 2], dtype=np.float32)
s = np.array([width, height], dtype=np.float32)
elif self.opt.fix_res:
inp_height, inp_width = self.opt.input_h, self.opt.input_w
c = np.array([new_width / 2.0, new_height / 2.0], dtype=np.float32)
s = max(height, width) * 1.0
# s = np.array([inp_width, inp_height], dtype=np.float32)
else:
inp_height = (new_height | self.opt.pad) + 1
inp_width = (new_width | self.opt.pad) + 1
c = np.array([new_width // 2, new_height // 2], dtype=np.float32)
s = np.array([inp_width, inp_height], dtype=np.float32)
resized_image = cv2.resize(image, (new_width, new_height))
return resized_image, c, s, inp_width, inp_height, height, width
def pre_process(self, image, scale, input_meta={}):
"""
Crop, resize, and normalize image. Gather meta data for post processing
and tracking.
"""
resized_image, c, s, inp_width, inp_height, height, width = self._transform_scale(
image)
trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height])
out_height = inp_height // self.opt.down_ratio
out_width = inp_width // self.opt.down_ratio
trans_output = get_affine_transform(c, s, 0, [out_width, out_height])
inp_image = cv2.warpAffine(resized_image,
trans_input, (inp_width, inp_height),
flags=cv2.INTER_LINEAR)
inp_image = ((inp_image / 255.0 - self.mean) / self.std).astype(
np.float32)
images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height,
inp_width)
if self.opt.flip_test:
images = np.concatenate((images, images[:, :, :, ::-1]), axis=0)
images = torch.from_numpy(images)
meta = {
"calib":
np.array(input_meta["calib"], dtype=np.float32) if "calib"
in input_meta else self._get_default_calib(width, height)
}
meta.update({
"c": c,
"s": s,
"height": height,
"width": width,
"out_height": out_height,
"out_width": out_width,
"inp_height": inp_height,
"inp_width": inp_width,
"trans_input": trans_input,
"trans_output": trans_output,
})
if "pre_dets" in input_meta:
meta["pre_dets"] = input_meta["pre_dets"]
if "cur_dets" in input_meta:
meta["cur_dets"] = input_meta["cur_dets"]
return images, meta
def _trans_bbox(self, bbox, trans, width, height):
"""
Transform bounding boxes according to image crop.
"""
bbox = np.array(copy.deepcopy(bbox), dtype=np.float32)
bbox[:2] = affine_transform(bbox[:2], trans)
bbox[2:] = affine_transform(bbox[2:], trans)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, width - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, height - 1)
return bbox
def _get_additional_inputs(self, dets, meta, with_hm=True):
"""
Render input heatmap from previous trackings.
"""
trans_input, trans_output = meta["trans_input"], meta["trans_output"]
inp_width, inp_height = meta["inp_width"], meta["inp_height"]
out_width, out_height = meta["out_width"], meta["out_height"]
input_hm = np.zeros((1, inp_height, inp_width), dtype=np.float32)
output_inds = []
for det in dets:
if det["score"] < self.opt.pre_thresh or det["active"] == 0:
continue
bbox = self._trans_bbox(det["bbox"], trans_input, inp_width,
inp_height)
bbox_out = self._trans_bbox(det["bbox"], trans_output, out_width,
out_height)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
if with_hm:
draw_umich_gaussian(input_hm[0], ct_int, radius)
ct_out = np.array(
[(bbox_out[0] + bbox_out[2]) / 2,
(bbox_out[1] + bbox_out[3]) / 2],
dtype=np.int32,
)
output_inds.append(ct_out[1] * out_width + ct_out[0])
if with_hm:
input_hm = input_hm[np.newaxis]
if self.opt.flip_test:
input_hm = np.concatenate((input_hm, input_hm[:, :, :, ::-1]),
axis=0)
input_hm = torch.from_numpy(input_hm).to(self.opt.device)
output_inds = np.array(output_inds, np.int64).reshape(1, -1)
output_inds = torch.from_numpy(output_inds).to(self.opt.device)
return input_hm, output_inds
def _get_default_calib(self, width, height):
calib = np.array([
[self.rest_focal_length, 0, width / 2, 0],
[0, self.rest_focal_length, height / 2, 0],
[0, 0, 1, 0],
])
return calib
def _sigmoid_output(self, output):
if "hm" in output:
output["hm"] = output["hm"].sigmoid_()
if "hm_hp" in output:
output["hm_hp"] = output["hm_hp"].sigmoid_()
if "dep" in output:
output["dep"] = 1.0 / (output["dep"].sigmoid() + 1e-6) - 1.0
output["dep"] *= self.opt.depth_scale
return output
def _flip_output(self, output):
average_flips = ["hm", "wh", "dep", "dim"]
neg_average_flips = ["amodel_offset"]
single_flips = [
"ltrb",
"nuscenes_att",
"velocity",
"ltrb_amodal",
"reg",
"hp_offset",
"rot",
"tracking",
"pre_hm",
]
for head in output:
if head in average_flips:
output[head] = (output[head][0:1] +
flip_tensor(output[head][1:2])) / 2
if head in neg_average_flips:
flipped_tensor = flip_tensor(output[head][1:2])
flipped_tensor[:, 0::2] *= -1
output[head] = (output[head][0:1] + flipped_tensor) / 2
if head in single_flips:
output[head] = output[head][0:1]
if head == "hps":
output["hps"] = (output["hps"][0:1] + flip_lr_off(
output["hps"][1:2], self.flip_idx)) / 2
if head == "hm_hp":
output["hm_hp"] = (output["hm_hp"][0:1] + flip_lr(
output["hm_hp"][1:2], self.flip_idx)) / 2
return output
def process(self,
images,
pre_images=None,
pre_hms=None,
pre_inds=None,
return_time=False):
with torch.no_grad():
torch.cuda.synchronize()
output, FeatureMaps = self.model(images, pre_images, pre_hms)
output = output[-1]
output = self._sigmoid_output(output)
output.update({"pre_inds": pre_inds})
if self.opt.flip_test:
output = self._flip_output(output)
torch.cuda.synchronize()
forward_time = time.time()
dets = generic_decode(output, K=self.opt.K, opt=self.opt)
torch.cuda.synchronize()
for k in dets:
dets[k] = dets[k].detach().cpu().numpy()
if return_time:
return output, dets, forward_time, FeatureMaps
else:
return output, dets, FeatureMaps
def post_process(self, dets, meta, scale=1):
dets = generic_post_process(
self.opt,
dets,
[meta["c"]],
[meta["s"]],
meta["out_height"],
meta["out_width"],
self.opt.num_classes,
[meta["calib"]],
meta["height"],
meta["width"],
)
self.this_calib = meta["calib"]
if scale != 1:
for i in range(len(dets[0])):
for k in ["bbox", "hps"]:
if k in dets[0][i]:
dets[0][i][k] = (np.array(dets[0][i][k], np.float32) /
scale).tolist()
return dets[0]
def merge_outputs(self, detections):
assert len(self.opt.test_scales) == 1, "multi_scale not supported!"
results = []
for i in range(len(detections[0])):
if detections[0][i]["score"] > self.opt.out_thresh:
results.append(detections[0][i])
return results
def debug(self,
debugger,
images,
dets,
output,
scale=1,
pre_images=None,
pre_hms=None):
img = images[0].detach().cpu().numpy().transpose(1, 2, 0)
img = np.clip(((img * self.std + self.mean) * 255.0), 0,
255).astype(np.uint8)
pred = debugger.gen_colormap(output["hm"][0].detach().cpu().numpy())
debugger.add_blend_img(img, pred, "pred_hm")
if "hm_hp" in output:
pred = debugger.gen_colormap_hp(
output["hm_hp"][0].detach().cpu().numpy())
debugger.add_blend_img(img, pred, "pred_hmhp")
if pre_images is not None:
pre_img = pre_images[0].detach().cpu().numpy().transpose(1, 2, 0)
pre_img = np.clip(((pre_img * self.std + self.mean) * 255.0), 0,
255).astype(np.uint8)
debugger.add_img(pre_img, "pre_img")
if pre_hms is not None:
pre_hm = debugger.gen_colormap(
pre_hms[0].detach().cpu().numpy())
debugger.add_blend_img(pre_img, pre_hm, "pre_hm")
def show_results(self, debugger, image, results):
debugger.add_img(image, img_id="generic")
if self.opt.tracking:
debugger.add_img(
self.pre_image_ori
if self.pre_image_ori is not None else image,
img_id="previous",
)
self.pre_image_ori = image
for j in range(len(results)):
if results[j]["score"] > self.opt.vis_thresh:
if "active" in results[j] and results[j]["active"] == 0:
continue
item = results[j]
if "bbox" in item:
sc = (item["score"] if self.opt.demo == "" or
not ("tracking_id" in item) else item["tracking_id"])
sc = item[
"tracking_id"] if self.opt.show_track_color else sc
debugger.add_coco_bbox(item["bbox"],
item["class"] - 1,
sc,
img_id="generic")
if "tracking" in item:
debugger.add_arrow(item["ct"],
item["tracking"],
img_id="generic")
tracking_id = item[
"tracking_id"] if "tracking_id" in item else -1
if ("tracking_id" in item and self.opt.demo == ""
and not self.opt.show_track_color):
debugger.add_tracking_id(item["ct"],
item["tracking_id"],
img_id="generic")
if (item["class"] in [1, 2]) and "hps" in item:
debugger.add_coco_hp(item["hps"],
tracking_id=tracking_id,
img_id="generic")
if (len(results) > 0 and "dep" in results[0] and "alpha" in results[0]
and "dim" in results[0]):
debugger.add_3d_detection(
image if not self.opt.qualitative else cv2.resize(
debugger.imgs["pred_hm"],
(image.shape[1], image.shape[0])),
False,
results,
self.this_calib,
vis_thresh=self.opt.vis_thresh,
img_id="ddd_pred",
)
debugger.add_bird_view(
results,
vis_thresh=self.opt.vis_thresh,
img_id="bird_pred",
cnt=self.cnt,
)
if self.opt.show_track_color and self.opt.debug == 4:
del debugger.imgs["generic"], debugger.imgs["bird_pred"]
def reset_tracking(self, opt):
if self.dataset == "nuscenes":
self.tracker = {}
for class_name in NUSCENES_TRACKING_NAMES:
self.tracker[class_name] = Tracker(opt,
self.model,
h=self.img_height,
w=self.img_width)
else:
self.tracker = Tracker(opt,
self.model,
h=self.img_height,
w=self.img_width)
self.pre_images = None
self.pre_image_ori = None
def update_public_detections(self, detections_file):
self.det_file = pd.read_csv(detections_file, header=None, sep=" ")
self.det_group = self.det_file.groupby(0)
self.det_group_keys = self.det_group.indices.keys()
