def build_debug_sample(components):
"""
Builds a sample for a model ONLY FOR DEBUGING. It returns the full meta
instance instead of an image
:param components: components
:return: list of final components of a sample.
"""
meta = components[0]
if meta.mask is None:
mask_paf = ALL_PAF_MASK
mask_heatmap = ALL_HEATMAP_MASK
else:
mask_paf = create_all_mask(meta.mask, 20, stride=8)
mask_heatmap = create_all_mask(meta.mask, 8, stride=8)
heatmap = create_heatmap(JointsLoader.num_joints_and_bkg,
46,
46,
meta.aug_joints,
7.0,
stride=8)
pafmap = create_paf(JointsLoader.num_connections,
46,
46,
meta.aug_joints,
1,
stride=8)
return [meta, mask_paf, mask_heatmap, pafmap, heatmap]
def build_sample(components):
"""
Builds a sample for a model.
:param components: components
:return: list of final components of a sample.
"""
meta = components[0]
image = meta.img
if meta.mask is None:
mask_paf = ALL_PAF_MASK
mask_heatmap = ALL_HEATMAP_MASK
else:
mask_paf = create_all_mask(meta.mask, 38, stride=8)
mask_heatmap = create_all_mask(meta.mask, 19, stride=8)
# print("hdebug:meta.aug_joints:",len(meta.aug_joints[0]))
# print(type(JointsLoader.num_joints_and_bkg))
heatmap = create_heatmap(JointsLoader.num_joints_and_bkg, 46, 46,
meta.aug_joints, 7.0, stride=8)
pafmap = create_paf(JointsLoader.num_connections, 46, 46,
meta.aug_joints, 1, stride=8)
# release reference to the image/mask/augmented data. Otherwise it would easily consume all memory at some point
meta.mask = None
meta.img = None
meta.aug_joints = None
meta.aug_center = None
return [image.astype(np.uint8), mask_paf, mask_heatmap, pafmap, heatmap]
def build_sample(components):
"""
Builds a sample for a model.
:param components: components
:return: list of final components of a sample.
"""
meta = components[0]
image = meta.img
if meta.mask is None:
mask_paf = ALL_PAF_MASK
mask_heatmap = ALL_HEATMAP_MASK
else:
mask_paf = create_all_mask(meta.mask, KEY_POINT_LINK, stride=1)
mask_heatmap = create_all_mask(meta.mask, KEY_POINT_NUM, stride=1)
stride = 4
heatmap = create_heatmap(JointsLoader.num_joints_and_bkg,
int(meta.crop_y_max / stride),
int(meta.crop_x_max / stride),
int(meta.crop_y / stride),
int(meta.crop_x / stride),
meta.aug_joints,
4.00,
stride=stride)
pafmap = create_paf(JointsLoader.num_connections,
int(meta.crop_y_max / stride),
int(meta.crop_x_max / stride),
int(meta.crop_y / stride),
int(meta.crop_x / stride),
meta.aug_joints,
1,
stride=stride)
maskmap, numlist = create_mask(JointsLoader.num_joints_and_bkg,
JointsLoader.num_connections,
int(meta.crop_y_max / stride),
int(meta.crop_x_max / stride),
int(meta.crop_y / stride),
int(meta.crop_x / stride),
meta.aug_joints,
4.00,
1,
stride=stride)
# release reference to the image/mask/augmented data. Otherwise it would easily consume all memory at some point
meta.mask = None
meta.img = None
meta.aug_joints = None
meta.aug_center = None
return [
image.astype(np.uint8), mask_paf, mask_heatmap, pafmap, heatmap,
maskmap, numlist
]
def build_debug_sample(components):
"""
Builds a sample for a model ONLY FOR DEBUGING. It returns the full meta
instance instead of an image
:param components: components
:return: list of final components of a sample.
"""
meta = components[0]
if meta.mask is None:
mask_paf = None
mask_heatmap = None
else:
mask_paf = create_all_mask(meta.mask, KEY_POINT_LINK, stride=4)
mask_heatmap = create_all_mask(meta.mask, KEY_POINT_NUM, stride=4)
stride = 4
heatmap = create_heatmap(JointsLoader.num_joints_and_bkg,
int(meta.crop_y_max / stride),
int(meta.crop_x_max / stride),
int(meta.crop_y / stride),
int(meta.crop_x / stride),
meta.aug_joints,
3.5,
stride=stride)
pafmap = create_paf(JointsLoader.num_connections,
int(meta.crop_y_max / stride),
int(meta.crop_x_max / stride),
int(meta.crop_y / stride),
int(meta.crop_x / stride),
meta.aug_joints,
1.00,
stride=stride)
return [meta, mask_paf, mask_heatmap, pafmap, heatmap]
def load_refs(batch_refs):
videos = []
masks = [[], []]
targets = [[], []]
for refs in batch_refs:
zipped, augment = refs
frames, boxes, maskpaths, _, coco_coords = zip(*zipped)
imgs, heats, pafs, mask_heats, mask_pafs = [], [], [], [], []
__masks = np.array([np.load(fl) for fl in maskpaths])
imgs = [imread(path) for path in frames]
imsize = imgs[0].shape
imgs = shape_image(imgs, boxes, augment)
width, height = int(imgs[0].shape[0] / 8), int(imgs[0].shape[1] / 8)
for frame_ii in range(len(frames)):
coords = shape_coords(coco_coords[frame_ii], boxes[frame_ii],
imsize, augment)
assert len(coords) == len(coco_coords[frame_ii])
heat = create_heatmap(19,
width,
height, [coords],
sigma=7.0,
stride=8)
paf = create_paf(19,
width,
height, [coords],
threshold=1.0,
stride=8)
assert paf.shape[-1] == 38
heat_mask = create_mask(19, width, height, __masks[frame_ii])
mask_heats.append(heat_mask)
for ind in PENN_MISSING:
heat_mask[:, :, ind] = 0
paf_mask = create_mask(38, width, height, __masks[frame_ii])
mask_pafs.append(paf_mask)
for ind, (j_idx1, j_idx2) in enumerate(JointsLoader.joint_pairs):
if j_idx1 in PENN_MISSING or j_idx2 in PENN_MISSING:
paf_mask[:, :, ind] = 0
heat = np.multiply(heat, heat_mask.astype(np.float32))
paf = np.multiply(paf, paf_mask.astype(np.float32))
heats.append(heat)
pafs.append(paf)
mask_heats = shape_image(mask_heats, boxes, augment, stride=8)
mask_pafs = shape_image(mask_pafs, boxes, augment, stride=8)
imgs = crop(imgs, 368)
mask_heats = crop(mask_heats, 46)
mask_pafs = crop(mask_pafs, 46)
heats = crop(heats, 46)
pafs = crop(pafs, 46)
videos.append(imgs)
masks[0].append(mask_heats)
masks[1].append(mask_pafs)
targets[0].append(heats)
targets[1].append(pafs)
return [
np.array(videos),
np.array(masks[1]),
np.array(masks[0]),
], [
np.array(targets[1]),
np.array(targets[0]),
]
