def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(dets['cfg']), cfg)
# re-filter on score threshold:
dets['all_boxes'] = \
[
[
im[im[:,4] > cfg.TEST.SCORE_THRESH,:] if len(im) != 0 else []
for im in cls
]
for cls in dets['all_boxes']
]
results = task_evaluation.evaluate_all(dataset,
dets['all_boxes'],
dets['all_segms'],
dets['all_keyps'],
output_dir,
use_matlab=args.matlab_eval)
task_evaluation.log_copy_paste_friendly_results(results)
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__)
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 get_detectron_result(im):
"""
功能: 获取传入图像的检测结果
输入参数列表:
im: BGR格式图片
返回参数列表:
img: 检测结果图片
detectron_result_info: 检测结果字典
"""
setup_logging(__name__)
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
if rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
pkl = rpn_pkl
yml = rpn_cfg
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()
img, detectron_result_info = vis_utils.vis_one_image_opencv(
im,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
show_box=True,
show_class=True)
return img, detectron_result_info
def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(dets['cfg']), cfg)
results = task_evaluation.evaluate_all(dataset,
dets['all_boxes'],
dets['all_segms'],
dets['all_keyps'],
output_dir,
use_matlab=args.matlab_eval)
task_evaluation.log_copy_paste_friendly_results(results)
def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(dets['cfg']), cfg)
results = task_evaluation.evaluate_all(
dataset,
dets['all_boxes'],
dets['all_segms'],
dets['all_keyps'],
output_dir,
use_matlab=args.matlab_eval
)
task_evaluation.log_copy_paste_friendly_results(results)
def inference(self, image_list):
"""Do an inference on the model with a set of inputs.
# Arguments:
image_list: The input image list
Return the result of the inference.
"""
image = image_list[0]
image = self.linear_to_srgb(image)*255.
imcpy = image.copy()
# Initialize the model out of the configuration and weights files
if not hasattr(self, 'model'):
workspace.ResetWorkspace()
# Reset to default config
merge_cfg_from_cfg(self.default_cfg)
# Load mask rcnn configuration file
merge_cfg_from_file(self.cfg_file)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
# Save mask rcnn full configuration file
self.mrcnn_cfg = copy.deepcopy(AttrDict(cfg)) # cfg from detectron.core.config
else:
# There is a global config file for all detectron models (Densepose, Mask RCNN..)
# Check if current global config file is correct for mask rcnn
if not dict_equal(self.mrcnn_cfg, cfg):
# Free memory of previous workspace
workspace.ResetWorkspace()
# Load mask rcnn configuration file
merge_cfg_from_cfg(self.mrcnn_cfg)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps, _ = infer_engine.im_detect_all(
self.model, image[:, :, ::-1], None
)
if self.binary_masks:
res = vis_utils.vis_one_image_binary(
imcpy,
cls_boxes,
cls_segms,
thresh=self.thresh
)
else:
res = vis_utils.vis_one_image_opencv(
imcpy,
cls_boxes,
cls_segms,
cls_keyps,
thresh=self.thresh,
show_box=self.show_box,
show_class=self.show_class,
dataset=self.dummy_coco_dataset,
alpha=self.alpha,
show_border=self.show_border,
border_thick=self.border_thick,
bbox_thick=self.bbox_thick,
font_scale=self.font_scale
)
res = self.srgb_to_linear(res.astype(np.float32) / 255.)
return [res]
def test_merge_cfg_from_cfg(self):
# Test: merge from deepcopy
s = 'dummy0'
cfg2 = copy.deepcopy(cfg)
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge from yaml
s = 'dummy1'
cfg2 = core_config.load_cfg(yaml.dump(cfg))
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with a valid key
s = 'dummy2'
cfg2 = AttrDict()
cfg2.MODEL = AttrDict()
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with an invalid key
s = 'dummy3'
cfg2 = AttrDict()
cfg2.FOO = AttrDict()
cfg2.FOO.BAR = s
with self.assertRaises(KeyError):
core_config.merge_cfg_from_cfg(cfg2)
# Test: merge with converted type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = [1]
core_config.merge_cfg_from_cfg(cfg2)
assert type(cfg.TRAIN.SCALES) is tuple
assert cfg.TRAIN.SCALES[0] == 1
# Test: merge with invalid type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = 1
with self.assertRaises(ValueError):
core_config.merge_cfg_from_cfg(cfg2)
def test_merge_cfg_from_cfg(self):
# Test: merge from deepcopy
s = 'dummy0'
cfg2 = copy.deepcopy(cfg)
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge from yaml
s = 'dummy1'
cfg2 = core_config.load_cfg(envu.yaml_dump(cfg))
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with a valid key
s = 'dummy2'
cfg2 = AttrDict()
cfg2.MODEL = AttrDict()
cfg2.MODEL.TYPE = s
core_config.merge_cfg_from_cfg(cfg2)
assert cfg.MODEL.TYPE == s
# Test: merge with an invalid key
s = 'dummy3'
cfg2 = AttrDict()
cfg2.FOO = AttrDict()
cfg2.FOO.BAR = s
with self.assertRaises(KeyError):
core_config.merge_cfg_from_cfg(cfg2)
# Test: merge with converted type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = [1]
core_config.merge_cfg_from_cfg(cfg2)
assert type(cfg.TRAIN.SCALES) is tuple
assert cfg.TRAIN.SCALES[0] == 1
# Test: merge with invalid type
cfg2 = AttrDict()
cfg2.TRAIN = AttrDict()
cfg2.TRAIN.SCALES = 1
with self.assertRaises(ValueError):
core_config.merge_cfg_from_cfg(cfg2)
setup_logging(__name__)
logger = logging.getLogger(__name__)
cfg_orig = load_cfg(yaml.dump(cfg))
#Download model of choice from https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md"
#Yaml files available at https://github.com/facebookresearch/Detectron/tree/master/configs
models_dir = os.path.join(os.path.dirname(__file__), 'model_files')
pkl = models_dir + "/kps_R-50-FPN.pkl"
yml = models_dir + "/kps_R-50-FPN.yaml"
if not os.path.isfile(pkl):
exit("MODEL FILES NOT FOUND (check kpdetection.py)")
#Config Setup
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
weights_file = pkl
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
#Detect all keypoints in im (image/frame)
def detect(im):
proposal_boxes, cls_boxes, cls_segms, cls_keyps \
= None, None, None, None
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
def inference(self, image_list):
"""Do an inference of the DensePose model with a set of image inputs.
# Arguments:
image_list: The input image list
Return the result of the inference.
"""
# Directly return image when no inference options
if not (self.show_human_index or self.show_uv or self.show_border
or self.show_grid):
return [image_list[0]]
image = image_list[0]
image = self.linear_to_srgb(image) * 255.
imcpy = image.copy()
# Initialize the model out of the configuration and weights files
if not hasattr(self, 'model'):
workspace.ResetWorkspace()
# Reset to default config
merge_cfg_from_cfg(self.default_cfg)
# Load densepose configuration file
merge_cfg_from_file(self.cfg_file)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(self.weights)
# Save densepose full configuration file
self.densepose_cfg = copy.deepcopy(
AttrDict(cfg)) #cfg from detectron.core.config
else:
# There is a global config file for all detectron models (Densepose, Mask RCNN..)
# Check if current global config file is correct for densepose
if not dict_equal(self.densepose_cfg, cfg):
# Free memory of previous workspace
workspace.ResetWorkspace()
# Load densepose configuration file
merge_cfg_from_cfg(self.densepose_cfg)
assert_and_infer_cfg(cache_urls=False, make_immutable=False)
self.model = infer_engine.initialize_model_from_cfg(
self.weights)
# Compute the image inference
with c2_utils.NamedCudaScope(0):
# image in BGR format for inference
cls_boxes, cls_segms, cls_keyps, cls_bodys = infer_engine.im_detect_all(
self.model, image[:, :, ::-1], None)
res = vis_utils.vis_densepose(
imcpy, # image in RGB format for visualization
cls_boxes,
cls_bodys,
show_human_index=self.show_human_index,
show_uv=self.show_uv,
show_grid=self.show_grid,
show_border=self.show_border,
border_thick=self.border_thick,
alpha=self.alpha)
res = self.srgb_to_linear(res.astype(np.float32) / 255.)
return [res]
