def hanle_frame(args, frameId, im, logger, model, dataset):
#out_name = os.path.join(
# args.output_dir, '{}'.format(frameId + '.pdf')
#)
logger.info('Processing frame: {}'.format(frameId))
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if frameId == 1:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
'{}'.format(frameId),
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
def on_frame(self, im, i, cls_boxes, cls_segms, cls_keyps):
"""
Args:
im - the image frame
i - the index of the image in the sequence
cls_boxes - detected boxes (of person class)
cls_segms - detected segments
cls_keyps - detected key points
"""
print("\n\n\n\nvis[{}] to {}".format(i, self.output_dir))
vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
"frame {}".format(i),
self.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=self.dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
)
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(envu.yaml_dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps, cls_bodys = None, None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ , cls_bodys_= \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
cls_bodys = cls_bodys_ if cls_bodys_ is not None else cls_bodys
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf'))
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
with open('test_vis.pkl', 'w') as f:
pickle.dump(
{
'im': im,
'cls_boxes': np.array(cls_boxes),
'cls_bodys': np.array(cls_bodys)
}, f)
vis_utils.vis_one_image(im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
def main(args):
logger = logging.getLogger(__name__)
dummy_nucoco_dataset = dummy_datasets.get_nucoco_dataset()
cfg_orig = load_cfg(envu.yaml_dump(cfg))
## Load image
coco = COCO_PLUS(args.ann_file, args.imgs_dir)
image_id = coco.dataset['images'][args.im_ind]['id']
img_path = os.path.join(args.imgs_dir, coco.imgs[image_id]["file_name"])
im = cv2.imread(img_path)
## Get the proposals for this image
proposals = rrpn_loader(args.rpn_pkl)
proposal_boxes = proposals[image_id]['boxes']
_proposal_scores = proposals[image_id]['scores']
workspace.ResetWorkspace()
## run models
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(img_path) + '.pdf')
)
logger.info('Processing {} -> {}'.format(img_path, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
img_path,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_nucoco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
for i, weights_file in enumerate(args.weights_list):
args.weights_list[i] = cache_url(weights_file, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
preffix_list = args.preffix_list if len(
args.preffix_list) else [""] * len(args.weights_list)
model = infer_engine.initialize_mixed_model_from_cfg(
args.weights_list, preffix_list=preffix_list)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir,
'{}'.format(os.path.basename(im_name) + '.' + args.output_ext))
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box)
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.pdf')
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
f = open(os.path.join(args.output_dir,'test_vis_{}'.format(os.path.basename(im_name).split('.')[0])) + '.pkl' ,'w')
pickle.dump({'im':im, 'kp':np.array(cls_keyps) },f)
f.close()
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
def vis(dataset, detections_pkl, thresh, output_dir, limit=0):
ds = JsonDataset(dataset)
roidb = ds.get_roidb()
with open(detections_pkl, 'rb') as f:
dets = pickle.load(f)
assert all(k in dets for k in ['all_boxes', 'all_segms', 'all_keyps']), \
'Expected detections pkl file in the format used by test_engine.py'
all_boxes = dets['all_boxes']
all_segms = dets['all_segms']
all_keyps = dets['all_keyps']
def id_or_index(ix, val):
if len(val) == 0:
return val
else:
return val[ix]
for ix, entry in enumerate(roidb):
if limit > 0 and ix >= limit:
break
if ix % 10 == 0:
print('{:d}/{:d}'.format(ix + 1, len(roidb)))
im = cv2.imread(entry['image'])
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
cls_boxes_i = [
id_or_index(ix, cls_k_boxes) for cls_k_boxes in all_boxes
]
cls_segms_i = [
id_or_index(ix, cls_k_segms) for cls_k_segms in all_segms
]
cls_keyps_i = [
id_or_index(ix, cls_k_keyps) for cls_k_keyps in all_keyps
]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(ix, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=thresh,
box_alpha=0.8,
dataset=ds,
show_class=True
)
def vis(dataset, detections_pkl, thresh, output_dir, limit=0):
ds = JsonDataset(dataset)
roidb = ds.get_roidb()
with open(detections_pkl, 'r') as f:
dets = pickle.load(f)
assert all(k in dets for k in ['all_boxes', 'all_segms', 'all_keyps']), \
'Expected detections pkl file in the format used by test_engine.py'
all_boxes = dets['all_boxes']
all_segms = dets['all_segms']
all_keyps = dets['all_keyps']
def id_or_index(ix, val):
if len(val) == 0:
return val
else:
return val[ix]
for ix, entry in enumerate(roidb):
if limit > 0 and ix >= limit:
break
if ix % 10 == 0:
print('{:d}/{:d}'.format(ix + 1, len(roidb)))
im = cv2.imread(entry['image'])
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
cls_boxes_i = [
id_or_index(ix, cls_k_boxes) for cls_k_boxes in all_boxes
]
cls_segms_i = [
id_or_index(ix, cls_k_segms) for cls_k_segms in all_segms
]
cls_keyps_i = [
id_or_index(ix, cls_k_keyps) for cls_k_keyps in all_keyps
]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(ix, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=thresh,
box_alpha=0.8,
dataset=ds,
show_class=True
)
def vis(dataset, roidb):
tmp = []
for i, entry in enumerate(roidb):
tmp.append(entry)
tmp.sort(cmp=compare)
file_name = '/home/wushujie/vis/result_scale_mean.txt'
with open(file_name, 'wb') as file_object:
for entry in tmp:
file_object.write(entry['image'])
file_object.write(str(entry['entropy']))
file_object.write('\n')
max_img = tmp[:20]
min_img = tmp[-20:]
for i, entry in enumerate(max_img):
im = cv2.imread(entry['image'])
im_name = str(i) # +'_'+entry['file_name']
output_dir = '/home/wushujie/vis/max_scale_mean'
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
entry['result'],
None,
None,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
ext='png',
out_when_no_box=True)
for i, entry in enumerate(min_img):
im = cv2.imread(entry['image'])
im_name = str(i)
output_dir = '/home/wushujie/vis/min_scale_mean'
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
entry['result'],
None,
None,
dataset=dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
ext='png',
out_when_no_box=True)
def inference(cfg_path, weights, img_pillow, output_dir):
logger = logging.getLogger(__name__)
merge_cfg_from_file(cfg_path)
#print( "cfg : ", cfg )
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
cfg.NUM_GPUS = 1
weights = cache_url(weights, cfg.DOWNLOAD_CACHE)
model = infer_engine.initialize_model_from_cfg(weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
im_name = "test"
#img_cv = cv2.imread(im_name)
img_np = np.asarray(img_pillow)
img_cv = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, img_cv, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
vis_utils.vis_one_image(
img_cv[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
IUV_SaveName = os.path.basename(im_name).split('.')[0] + '_IUV.png'
INDS_SaveName = os.path.basename(im_name).split('.')[0] + '_INDS.png'
iuv_pillow = Image.open(os.path.join(output_dir,
'{}'.format(IUV_SaveName)))
inds_pillow = Image.open(
os.path.join(output_dir, '{}'.format(INDS_SaveName)))
return iuv_pillow, inds_pillow
def __getitem__(self, idx):
im_name = 'DensePoseData/demo_data/xyz.jpg'
output_dir = 'DensePoseData/'
img_name1 = os.path.join(self.root_dir1, self.landmarks_frame.iloc[idx,
0])
image1 = cv2.imread(img_name1)
img_name2 = os.path.join(self.root_dir2, self.landmarks_frame.iloc[idx,
1])
image2 = cv2.imread(img_name2)
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
self.model, image1, None, timers=self.timers)
im1 = vis_utils.vis_one_image(
image1[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
self.model, image2, None, timers=self.timers)
im2 = vis_utils.vis_one_image(
image2[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
image3 = cv2.merge((image1, im1, im2))
sample = {'image1': image1, 'image2': image2, 'image3': image3}
if self.transform:
sample = self.transform(sample)
return sample
def main(args):
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, 'detection_' + '{}'.format(os.path.basename(im_name))
)
print('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
#print('Inference time: {:.3f}s'.format(time.time() - t))
#for k, v in timers.items():
# print(' | {}: {:.3f}s'.format(k, v.average_time))
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
out_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
out_when_no_box=args.out_when_no_box
)
def extract_iuv(image_path, model, infer_engine):
im = cv2.imread(image_path)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None
)
iuv_out = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
None,
None,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
return iuv_out
def single_process(args, dummy_coco_dataset, im, im_name, model):
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
# if i == 0:
# logger.info(
# ' \ Note: inference on the first image will be slower than the '
# 'rest (caches and auto-tuning need to warm up)'
# )
print("预测结果：", cls_boxes, cls_segms, cls_keyps)
new_img = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box)
return new_img
def draw_bbox(self, im, cls_boxes):
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
'bbox',
'/tmp/',
cls_boxes,
None,
None,
dataset=self.dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
#thresh=0.7,
thresh=self.SCORE_THRESH,
kp_thresh=2,
ext='png',
out_when_no_box=True)
return cv2.imread('/tmp/bbox.png')[:, :, ::-1]
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
print("capturing video " + args.input)
cap = cv2.VideoCapture(args.input)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# pdb.set_trace()
grab = 1
if (cap.isOpened() == False):
print("Error opening video stream or file")
exit
while (cap.isOpened() and grab <= total_frames):
grab += 1
ret_val, im = cap.read()
#skips intermediate frames
#if grab%2 !=0:
# continue
#uncomment to resize image
#im = cv2.resize(im, (int(1280/1),int(720/1)))
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
output_name = 'frame' + str(grab).zfill(4) + '.mp4'
print("| Analysed frame {0} / {1} in {2}ms".format(
grab, total_frames, int(1000. * (time.time() - t))))
#print('\t | Inference time: {:.3f}s'.format(time.time() - t))
#for k, v in timers.items():
# print('\t | {}: {:.3f}s'.format(k, v.average_time))
ret = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
output_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=False,
thresh=0.7,
kp_thresh=2)
cap.release()
cv2.destroyAllWindows()
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
h5py_file_path = ('/media/hdd1/tanya/open-pose/'
'paired_filenames512_image_keypoints512_main.h5')
hf = h5py.File(h5py_file_path, 'r')
IUV_image_list = []
for i in range(len(hf['images'])):
im = hf['images'][i]
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
IUV_image = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
'dummy',
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
IUV_image_list.append(IUV_image)
IUV_images_final = np.stack(IUV_image_list, 0)
with h5py.File('./paired_filenames512_image_keypoints_withIUV.h5', 'w') as f:
f.create_dataset('images', data=np.array(hf['images']))
f.create_dataset('keypoints', data=np.array(hf['keypoints']))
f.create_dataset('IUV', data=IUV_images_final)
def run(im, im_name, output_dir, ):
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
self.model, im, None, timers=timers
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=self.dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
def main(args, model, logger):
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
im_list = ["DensePoseData/demo_data/demo_im.jpg"]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.pdf'))
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dataset_name = cfg.TEST.DATASETS[0]
dummy_coco_dataset = JsonDataset(dataset_name)
# dummy_coco_dataset = dummy_datasets.get_paris_dataset()
vid_dir = '/coco/paris_dataset/PARIS_demo.mp4'
cap = cv2.VideoCapture(vid_dir)
ret, im = cap.read()
count = 0
while ret:
im_name = str(count)
out_name = os.path.join(args.output_dir,
'{}'.format(str(count) + '.jpg'))
logger.info('Processing frame -> {}'.format(count))
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
vis_im = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
# cv2.imshow('frame', vis_im)
# cv2.waitKey(10)
ret, im = cap.read()
count += 1
cap.release()
cv2.destroyAllWindows()
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
jsonboxes = []
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir,
'{}'.format(os.path.basename(im_name) + '.' + args.output_ext))
file_name = os.path.basename(im_name)
(image_name, ext) = os.path.splitext(os.path.basename(im_name))
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
boxes, segms, keypoints, classes = convert_from_cls_format(
cls_boxes, cls_segms, cls_keyps)
jsonboxes_perimage = {
'boxes': boxes,
'classes': classes,
'image_id': int(image_name),
'file_name': file_name
}
jsonboxes_perimage['boxes'] = jsonboxes_perimage['boxes'].tolist()
jsonboxes.append(jsonboxes_perimage)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box)
Dataset_name = args.dataset_name
direction = '/home/tecnimaq/Gabriela/Detectron/json_dump'
jsonboxesfile = 'jsonboxes_' + Dataset_name + '.json'
with open(os.path.join(direction, jsonboxesfile), 'w') as thisfile:
json.dump(jsonboxes, thisfile)
dir = os.path.join(direction, jsonboxesfile)
info.get_info(dir, Dataset_name)
def test_net(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
# pose_pred_model = generate_poseJPPNet_pred_model()
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers, entry
)
# test_res_top.feedBlob_run(model, im, entry)
mask_res_top = test_res_top.res_top_result(model, entry)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
mask_png_20 = vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis_9k'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
# fusion
mask_fusion = mask_png_20 + mask_res_top
mask_fusion_out = os.path.join(output_dir, 'fusion')
if not os.path.exists(mask_fusion_out):
os.makedirs(mask_fusion_out)
# logger.info('fusion save to {}'.format(mask_fusion_out))
cv2.imwrite(os.path.join(mask_fusion_out, entry['id']+'.png'), mask_fusion.argmax(0))
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def main(args):
glob_keypoints = []
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*' + '.png')
else:
im_list = [args.im_or_folder]
im_list = sorted(im_list)
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
cls_boxes_np = np.asarray(cls_boxes)
cls_boxes_prob = cls_boxes_np[1][:,4]
idx_max_prob = np.argmax(cls_boxes_prob)
cls_keyps_max_prob = cls_keyps[1][idx_max_prob]
pose_x_y_prob_after_softmax = cls_keyps_max_prob[[0,1,3]]
glob_keypoints.append(np.transpose(pose_x_y_prob_after_softmax))
dictionarry_keypoints={'S1': {'Directions 1' : np.asarray([glob_keypoints])}}
metadata = {'layout_name': 'h36m', 'num_joints': 17, 'keypoints_symmetry': [[4, 5, 6, 11, 12, 13], [1, 2, 3, 14, 15, 16]]}
#np.savez(os.path.join('/home/narvis/Dev/VideoPose3D/data', "data_2d_detections.npz"), metadata=metadata, positions_2d=dictionarry_keypoints)
np.savez(os.path.join(args.output_dir, "data_2d_detections.npz"), metadata=metadata, positions_2d=dictionarry_keypoints)
def main(args):
global dot_count
dot_count = 0
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
host = '127.0.0.1'
port = 9998
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind((host, port))
s.listen(5)
s.settimeout(2)
SIZE = 4096
#s.settimeout(10)
multiTracker = cv2.MultiTracker_create()
while True:
try:
sock, addr = s.accept()
receive = sock.recv(SIZE).decode()
tmp = str(receive).split("|")
path = tmp[0]
device_addr = tmp[1]
print("path: {0}, device_addr: {1}".format(path, device_addr))
im_list = []
im_list.append(path)
for i, im_name in enumerate(im_list):
out_name = os.path.join(args.output_dir,
'{}'.format(os.path.basename(im_name)))
try:
logger.info('Processing {} -> {}'.format(
im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() -
t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
print("len(cls_boxes[1])-1: {0}".format(
len(cls_boxes[1]) - 1))
except:
print("fail in read: {0}".format(out_name))
count_people = 0
now_boxes = []
now_center = []
try:
for i in range(len(cls_boxes[1])):
if cls_boxes[1][i][4] > 0.7:
now_boxes.append(cls_boxes[1][i][:4])
now_center.append([
int((cls_boxes[1][i][0] + cls_boxes[1][i][2])
// 2),
int((cls_boxes[1][i][1] + cls_boxes[1][i][3])
// 2)
])
count_people = count_people + 1
except:
count_people = 0
print("now_center: {0}".format(now_center))
print("count_people: {0}".format(count_people))
ans_command = str(count_people) + " "
for i in range(int(count_people)):
ans_command = ans_command + str(
now_center[i][0]) + "," + str(now_center[i][1]) + ","
ans_command = ans_command.strip(",")
print(ans_command)
sock.send(ans_command.encode())
im_name = ""
except Exception as e:
print(' ',
end='\r')
error_text = "Exception: " + str(e) + ", reconnecting "
for i in range(dot_count):
error_text = error_text + "."
dot_count = dot_count + 1
if dot_count > 3:
dot_count = 0
print(error_text, end='\r')
s.close()
def infer_method(im, mymethod="back"):
logger = logging.getLogger(__name__)
#styleimage = style.style_method()
merge_cfg_from_file(
"configs/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml")
cfg.NUM_GPUS = 1
myweights = cache_url("DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.pkl",
cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(myweights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
out_name = os.path.join("tools/output",
'{}'.format(os.path.basename("myresult") + '.jpg'))
#logger.info('Processing {} -> {}'.format(im_name, out_name))
im_name = "love.jpg"
im2 = cv2.imread("tools/iron8.jpg")
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
if im2 is not None:
cls_boxes2, cls_segms2, cls_keyps2, cls_bodys2 = infer_engine.im_detect_all(
model, im2, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if mymethod == "back":
vis_utils.change_background(
im[:, :, ::-1], # BGR -> RGB for visualization
im2[:, :, ::-1],
im_name,
"static/img",
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
elif mymethod == "iron":
vis_utils.ironman(
im[:, :, ::-1], # BGR -> RGB for visualization
im2[:, :, ::-1],
im_name,
args.output_dir,
cls_boxes,
cls_boxes2,
cls_segms,
cls_keyps,
cls_bodys,
cls_segms2,
cls_keyps2,
cls_bodys2,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
elif mymethod == 'style_b':
styleimage = cv2.cvtColor(
numpy.array(style.stylize_img(im_name, args.image_second)),
cv2.COLOR_RGB2BGR)
resized_im = style.tensor_to_image(style.load_to_mask(im_name))
opencvImage = cv2.cvtColor(numpy.array(resized_im),
cv2.COLOR_RGB2BGR)
print(opencvImage)
with c2_utils.NamedCudaScope(0):
bo, se, ke, bod = infer_engine.im_detect_all(model,
opencvImage,
None,
timers=timers)
vis_utils.change_background(
opencvImage[:, :, ::-1], # BGR -> RGB for visualization
styleimage[:, :, ::-1],
"stylized_img.jpg",
args.output_dir,
bo,
se,
ke,
bod,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
else:
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
def densepose_coach(args):
# args = parse_args()
# print(args)
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
# if os.path.isdir(args.video) #
im_list = video2imgs(args.video)
# if os.path.isdir(args.im_or_folder):
# im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
# else:
# im_list = [args.im_or_folder]
# vis_imgshape = vis_img.shape
# size = (vis_imgshape[1], vis_imgshape[0])
# fourcc = cv2.VideoWriter_fourcc(*'mp4v')
# size = (1600,900)
# videoWriter = cv2.VideoWriter(args.output_dir + os.path.basename(args.video), fourcc, 20, size)
# time_start = time.time()
for i, im in enumerate(im_list):
# out_name = os.path.join(
# args.output_dir, '{}'.format(os.path.basename(im_name) + '.pdf')
# )
# logger.info('Processing {} -> {}'.format(im_name, out_name))
# im = cv2.imread(im_name)
# im = im[0:480, 210:430] #裁剪坐标为[y0:y1, x0:x1]
if i % 6 == 0:
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
model, im, None, timers=timers)
##############################20190123
box_list = [b for b in cls_boxes if len(b) > 0]
if len(box_list) > 0:
boxes = np.concatenate(box_list)
else:
boxes = None
if cls_keyps is not None:
keyps = [k for klist in cls_keyps for k in klist]
else:
keyps = None
a = np.argmax(boxes, axis=0)
j = a[4]
kps = keyps[j]
kpss = np.expand_dims(kps, axis=0)
if i == 0:
kpsstack = kpss
else:
kpsstack = np.vstack((kpsstack, kpss))
# if i == 0:
# kpss = kps
# elif i == 1:
# kpss2 = kps - kpss
# kpss = kps
# kpsss = np.expand_dims(kpss2, axis=0)
# kpsstack = kpsss
# else:
# kpss2 = kps - kpss
# kpss = kps
# kpsss = np.expand_dims(kpss2, axis=0)
# kpsstack = np.vstack((kpsstack, kpsss))
# mat_path = '/home/server010/zhoukaiye/jianshen/jianshenshuju/test/coordinate-161441-keyps.mat'
# io.savemat(mat_path, {'name':kps})
###########
# with open("/home/server010/zhoukaiye/jianshen/jianshenshuju/test/coordinates2.txt", "w") as f:
# for kp in cls_keyps:
# f.write(str(kp))
# mat_path = '/home/server010/zhoukaiye/jianshen/jianshenshuju/test/coordinate-161441.mat'
# io.savemat(mat_path, {'name':cls_keyps})
############
# IPython.embed()
# logger.info('Inference time: {:.3f}s'.format(time.time() - t))
# for k, v in timers.items():
# logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
# if i == 0:
# logger.info(
# ' \ Note: inference on the first image will be slower than the '
# 'rest (caches and auto-tuning need to warm up)'
# )
# if i % 5 == 0:
All_Coords = vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
cls_boxes,
None, # im_name,
None, # args.output_dir,
cls_segms,
cls_keyps,
cls_bodys,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2)
if cls_keyps is None:
vis_img = visualize(im, All_Coords)
else:
# print('keypoint')
vis_img = All_Coords
if i == 0:
fourcc = cv2.VideoWriter_fourcc(*'mp4v'.encode('utf-8'))
# fourcc = cv2.VideoWriter_fourcc(*'MP4V')
#fourcc = cv2.VideoWriter_fourcc(*'XVID')
vis_imgshape = vis_img.shape
size = (vis_imgshape[1], vis_imgshape[0])
videoWriter = cv2.VideoWriter(
args.output_dir +
os.path.basename(args.video).split('.')[0] + '.mp4',
fourcc, 25, size)
# videoWriter.write(vis_img)
# IUV_list.append(vis_img)
#imgs2video
# videoWriter = cv2.VideoWriter(args.output_dir + '/zuoqianceyang5.mp4', fourcc, 20, size)
## 5 qu 1
# if i % 5 == 0:
videoWriter.write(vis_img)
# mat_path = '/home/server010/server010/FitNess/Video_capture/video_out/yingla_test2.mat'
# io.savemat(mat_path, {'name':kpsstack})
videoWriter.release()
data1 = kpsstack[:, 0:2, :]
classify_action, count_action, maxList, maxheight, start_frame = classify_count(
data1)
return classify_action, count_action, maxList, maxheight, start_frame, data1
def test_net_batch(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU, using batch inference
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
# roidb = roidb[:500]
# Debug purposes
# roidb = [{'image': im} for im in glob.glob('/staging/leuven/stg_00027/imob/detectron/lib/datasets/data/coco/coco_val2014/*.png')][:500]
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
ims = []
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
raise NotImplementedError('Precomputed proposals not implemented for batch inference, set TEST.IMS_PER_BATCH to 1')
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
# im = cv2.imread(roidb[0]['image'])
im = cv2.imread(entry['image'])
ims.append(im)
if not ((len(ims) == cfg.TEST.IMS_PER_BATCH) or (i == (num_images - 1))):
continue
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_batch, cls_segms_batch, cls_keyps_batch = im_detect_all_batch(
model, ims, box_proposals, timers
)
for n in range(len(ims)):
local_i = i - len(ims) + n + 1
cls_boxes_i = cls_boxes_batch[n]
cls_segms_i = cls_segms_batch[n] if cls_segms_batch else None
cls_keyps_i = cls_keyps_batch[n] if cls_keyps_batch else None
extend_results(local_i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(local_i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(local_i, all_keyps, cls_keyps_i)
if local_i % (10 * cfg.TEST.IMS_PER_BATCH) == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = int(ave_total_time * (num_images - local_i - 1) / float(cfg.TEST.IMS_PER_BATCH))
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + local_i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
# This will now only show the last image of each batch
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
ims = []
cfg_yaml = envu.yaml_dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def test_net(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps, all_bodys = \
empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if 'has_no_densepose' in entry.keys():
pass
else:
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i,cls_bodys_i = \
im_detect_all(model, im, box_proposals, timers)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if cls_bodys_i is not None:
extend_results(i, all_bodys, cls_bodys_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time +
timers['im_detect_body_uv'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time +
timers['misc_body_uv'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
all_bodys=all_bodys,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps, all_bodys
def test_net(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
if cfg.TEST.IMS_PER_BATCH != 1:
return test_net_batch(weights_file, dataset_name, proposal_file, output_dir, ind_range, gpu_id)
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range
)
# roidb = roidb[:500]
# roidb = [{'image': im} for im in glob.glob('/staging/leuven/stg_00027/imob/detectron/lib/datasets/data/coco/coco_val2014/*.png')][:500]
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(num_classes, num_images)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
model, im, box_proposals, timers
)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (
timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time
)
misc_time = (
timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time
)
logger.info(
(
'im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})'
).format(
start_ind + 1, end_ind, total_num_images, start_ind + i + 1,
start_ind + num_images, det_time, misc_time, eta
)
)
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True
)
cfg_yaml = envu.yaml_dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps
def infer(im,
i,
output_name='None',
video='None',
mot_output=[],
json_output=[],
masks_output=[]):
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)')
boxes, segms, keypoints, classes = vis_utils.convert_from_cls_format(
cls_boxes, cls_segms, cls_keyps)
if segms is not None:
masks = mask_util.decode(segms)
else:
masks = np.asarray([[[]]]) # an empty array with shape[2] == 0
all_contours = [] # This might not be getting reset
for mask_idx in range(masks.shape[2]):
#print("shapes are {}".format(masks[...,mask_idx].shape))
_, contours, _ = cv2.findContours(
masks[..., mask_idx].copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE) # why is this getting copied
all_contours.append(
contours
) # this code is more general and allows for multiple contours, but there aren't any
if boxes is None:
boxes = []
else:
boxes = boxes.tolist()
print("classes are {}".format(classes))
# create the mot formated row
def mot_row(i, boxes, classes):
"""<frame>, <id=class>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x=-1>, <y=-1>, <z=-1>
"""
assert len(boxes) == len(
classes), "the boxes weren't the same length as the boxes"
out_ = np.empty((0, 10), float)
for box_id, box_ in enumerate(boxes):
class_ = classes[box_id]
# check that the conversion is correct
# and that conf is where I think it is
out_ = np.append(out_,
np.array([[
i, classes[box_id], box_[0], box_[1],
box_[2] - box_[0], box_[3] - box_[1],
box_[4], -1.0, -1.0, -1.0
]]),
axis=0)
return out_
if args.mot_format:
#TODO actually write out the class o fthe detection
try:
mot_output = np.append(mot_output,
mot_row(i, boxes, classes),
axis=0)
except ValueError:
import pdb
pdb.set_trace()
json_output.append({
'video':
video,
'frame':
i,
'boxes':
boxes,
'classes':
classes,
'contours': [[c.tolist() for c in some_contours]
for some_contours in all_contours]
})
#masks_output[i] = [[c.tolist() for c in some_contours] for some_contours in all_contours]# this will be a mapping from frame to contours
else:
json_output.append({
'video':
video,
'frame':
i,
'boxes':
boxes,
'classes':
classes,
'contours': [[c.tolist() for c in some_contours]
for some_contours in all_contours]
})
if i % 100 == 0:
print("about to save files")
save_files(
args, im_name, output_basename, mot_output, json_output,
masks_output
) # so this keeps all the data in memory which seems terrible
json_output = []
mot_output = []
masks_output = []
if len(json_output) != 0 or len(masks_output) != 0 or len(
mot_output) != 0:
import pdb
pdb.set_trace()
# MOD
print('output name {}'.format(output_name))
#HACK
VISUALIZATION_FREQUENCY = 100
if args.visualize and i % VISUALIZATION_FREQUENCY == 0:
start_time = time.time()
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
output_name,
"{}/images".format(args.output_dir),
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.4, # change the display confidence
kp_thresh=2,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box)
print(
"only visualizing every {}th frame, it took {} seconds".format(
VISUALIZATION_FREQUENCY,
time.time() - start_time))
if args.mot_format:
return mot_output
else:
pass
def next_batch(self):
data_blob = np.zeros(
(self.batch_size, input_size[0], input_size[1], 40),
dtype=np.float32)
gt_blob = np.zeros((self.batch_size, input_size[0], input_size[1]),
dtype=np.int32)
batch_id = 0
#for i in range(self.batch_size):
while True:
# print("start loading data batch {}".format(batch_id))
entry = self.roidb[self.cur]
self.cur += 1
if self.cur >= self.num_samples:
self.cur = 0
if self.is_train: # shuffle
random.shuffle(self.seq)
im = cv2.imread(entry['image'])
label = cv2.imread(entry['label'], 0)
# model test create human
timers = defaultdict(Timer)
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i = im_detect_all(
self.test_model, im, None, timers, entry)
mask_res_top = test_res_top.res_top_result(self.test_model,
entry,
save_png=False)
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
mask_png_20 = vis_utils.vis_one_image(im[:, :, ::-1],
'{:d}_{:s}'.format(
self.cur, im_name),
output_dir,
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=None,
show_class=False,
save_png=False)
if len(mask_png_20.shape) == 2:
print("data next continue")
continue
#resize
#print("mask_res_top shape:", mask_res_top.shape)
mask_restop_resize = cv2.resize(mask_res_top.transpose([1, 2, 0]),
(input_size[0], input_size[1]),
interpolation=cv2.INTER_LINEAR)
#print("mask_restop_resize: ", mask_restop_resize.shape)
data_blob[batch_id, :, :, 0:20] = mask_restop_resize
#print("data_blob:", data_blob.shape)
# print("mask_png_20 shape", mask_png_20.shape)
mask_png_20 = mask_png_20.transpose((1, 2, 0))
#print("mask_png_20 shape:", mask_png_20.shape)
#print("data_blob shape:", data_blob.shape)
data_blob[batch_id, :, :,
20:] = cv2.resize(mask_png_20,
(input_size[0], input_size[1]),
interpolation=cv2.INTER_LINEAR)
gt_blob[batch_id] = cv2.resize(label,
(input_size[0], input_size[1]),
interpolation=cv2.INTER_NEAREST)
# find c==4 sunglasses
index = np.where(gt_blob[batch_id] == 4)
if len(index[0]):
print('have sunglass, pixles:', len(index[0]))
# print("loaded data batch {}".format(batch_id))
batch_id += 1
if batch_id >= self.batch_size:
break
data_blob = np.transpose(data_blob, [0, 3, 1, 2]) # NHWC-> NCHW
return data_blob, gt_blob
def vis_wholedataset(
dataset_name,
proposal_file,
output_dir,
ind_range=None,
all_boxes=None,
all_segms=None,
all_keyps=None,
all_personmasks=None,
all_parss=None,
all_bodys=None,
img_name=None,
show_box=True,
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
timers = defaultdict(Timer)
txt_all = []
parsing_i_png = None
for i, entry in enumerate(roidb):
if img_name is not None:
if entry['image'].split('/')[-1] not in img_name:
continue
if 'has_no_densepose' in entry.keys():
pass
else:
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
cls_boxes_i, cls_segms_i, cls_keyps_i, cls_personmask_i, cls_parss_i, cls_bodys_i = \
get_ind_results(i, all_boxes), get_ind_results(i, all_segms), get_ind_results(i,all_keyps), \
get_ind_results(i,all_personmasks), get_ind_results(i,all_parss), get_ind_results(i,all_bodys)
if cfg.MODEL.PARSING_ON:
parsing_i_png, txt_result = parsing_utils.parsing2png(
cls_boxes_i, cls_parss_i, output_dir, entry['image'],
im.shape[:2])
uv_parsing_i_png, uv_txt_result = parsing_utils.parsing2png(
cls_boxes_i,
cls_bodys_i,
output_dir,
entry['image'],
im.shape[:2],
uv=True,
)
'''
person_mask_i_png, txt_result_pm = parsing_utils.parsing2png(
cls_boxes_i, cls_personmask_i, output_dir, entry['image'], im.shape[:2],flag_pm=True,
)
'''
txt_all.append(txt_result)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum([t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time +
timers['im_detect_body_uv'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time +
timers['misc_body_uv'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.VIS and not 'has_no_densepose' in entry.keys():
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(
im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_personmask_i,
keypoints=cls_keyps_i,
body_uv=cls_bodys_i,
part_segms=parsing_i_png,
uv_part_segms=uv_parsing_i_png,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True,
show_box=show_box,
)
return all_boxes, all_segms, all_keyps, all_personmasks, all_parss, all_bodys
def test_net(weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=None,
gpu_id=0,
subset_pointer=None):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert not cfg.MODEL.RPN_ONLY, \
'Use rpn_generate to generate proposals from RPN-only models'
# determine file name
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
# load results if already present
if os.path.exists(det_file):
res = load_object(det_file)
all_boxes, all_segms, all_keyps = res['all_boxes'], res[
'all_segms'], res['all_keyps']
else:
roidb, dataset, start_ind, end_ind, total_num_images = get_roidb_and_dataset(
dataset_name, proposal_file, ind_range)
if subset_pointer is not None:
voc_subset = subset_pointer.subset
this_sub = voc_subset[:len(roidb)]
# subset_pointer.subset = voc_subset[len(roidb):]
# filter roidb:
roidb = [roi for taking, roi in zip(this_sub, roidb) if taking]
total_num_images = len(roidb)
end_ind = total_num_images
model = initialize_model_from_cfg(weights_file, gpu_id=gpu_id)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
all_boxes, all_segms, all_keyps = empty_results(
num_classes, num_images)
if cfg.TEST.COLLECT_ALL:
all_feats = []
all_class_weights = np.empty(shape=(num_images, num_classes),
dtype=np.float32)
timers = defaultdict(Timer)
for i, entry in enumerate(roidb):
if cfg.TEST.PRECOMPUTED_PROPOSALS:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select only the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = entry['boxes'][entry['gt_classes'] == 0]
if len(box_proposals) == 0:
continue
else:
# Faster R-CNN type models generate proposals on-the-fly with an
# in-network RPN; 1-stage models don't require proposals.
box_proposals = None
im = cv2.imread(entry['image'])
with c2_utils.NamedCudaScope(gpu_id):
cls_boxes_i, cls_segms_i, cls_keyps_i, sum_softmax, topk_feats = im_detect_all(
model,
im,
box_proposals,
timers,
return_feats=cfg.TEST.COLLECT_ALL)
# print('nfeats:', topk_feats.shape[0])
# print(topk_feats)
extend_results(i, all_boxes, cls_boxes_i)
if cls_segms_i is not None:
extend_results(i, all_segms, cls_segms_i)
if cls_keyps_i is not None:
extend_results(i, all_keyps, cls_keyps_i)
if cfg.TEST.COLLECT_ALL:
all_class_weights[i] = sum_softmax
all_feats.append(
topk_feats
) # will accumulate about 9 Gb of feats on COCO train set (118K imgs)
if i % 10 == 0: # Reduce log file size
ave_total_time = np.sum(
[t.average_time for t in timers.values()])
eta_seconds = ave_total_time * (num_images - i - 1)
eta = str(datetime.timedelta(seconds=int(eta_seconds)))
det_time = (timers['im_detect_bbox'].average_time +
timers['im_detect_mask'].average_time +
timers['im_detect_keypoints'].average_time)
misc_time = (timers['misc_bbox'].average_time +
timers['misc_mask'].average_time +
timers['misc_keypoints'].average_time)
logger.info(('im_detect: range [{:d}, {:d}] of {:d}: '
'{:d}/{:d} {:.3f}s + {:.3f}s (eta: {})').format(
start_ind + 1, end_ind, total_num_images,
start_ind + i + 1, start_ind + num_images,
det_time, misc_time, eta))
if cfg.VIS:
im_name = os.path.splitext(os.path.basename(entry['image']))[0]
vis_utils.vis_one_image(im[:, :, ::-1],
'{:d}_{:s}'.format(i, im_name),
os.path.join(output_dir, 'vis'),
cls_boxes_i,
segms=cls_segms_i,
keypoints=cls_keyps_i,
thresh=cfg.VIS_TH,
box_alpha=0.8,
dataset=dataset,
show_class=True)
cfg_yaml = envu.yaml_dump(cfg)
save_object(
dict(all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(
os.path.abspath(det_file)))
if cfg.TEST.COLLECT_ALL:
save_object(all_class_weights,
os.path.join(output_dir, 'class_weights.pkl'))
save_object(all_feats,
os.path.join(output_dir, 'feature_vectors.pkl'))
logger.info(
'Wrote class weights and feature vectors to output folder')
return all_boxes, all_segms, all_keyps
