def generateLabelMap(oneimg):
ori_img = cv2.imread(oneimg['img_path'])
ori_img_shape = ori_img.shape
fscale = 368.0 / max(ori_img_shape[0], ori_img_shape[1])
img_pad = padimg(ori_img, 368)
thre = 0.5
crop_size_width = 368
crop_size_height = 368
augmentcols = 368
augmentrows = 368
stride = 8
grid_x = augmentcols / stride
grid_y = augmentrows / stride
sigma = 7.0
heat_map = list()
for i in range(numofparts + 1):
heat_map.append(
np.zeros((crop_size_width / stride, crop_size_height / stride)))
for one_rect in oneimg['annoations']:
for onePoint in one_rect['annopoints']:
x, y, part_id, isVisible = onePoint
x *= fscale
y *= fscale
x = int(x)
y = int(y)
part_id = part2ours[part_id] - 1
if part_id <= -1:
continue
cv2.circle(heat_map[part_id], (x, y), 8, (1, 1, 1), -1, 8)
putGaussianMaps(heat_map[part_id], 368, 368, x, y, stride, grid_x,
grid_y, sigma)
### put background channel
#heat_map[numofparts] = heat_map[0]
heat_map[numofparts] = np.max(heat_map[:-1], axis=0)
# for g_y in range(heat_map[0].shape[0]):
# for g_x in range(heat_map[0].shape[1]):
# maximum=0
# for i in range(numofparts):
# if maximum<heat_map[i][g_y, g_x]:
# maximum = heat_map[i][g_y, g_x]
# heat_map[numofparts][g_y,g_x]=max(maximum,0.0)
mid_1 = [13, 14, 14, 1, 2, 4, 5, 1, 7, 8, 4, 10, 11]
mid_2 = [14, 1, 4, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12]
thre = 1
pag_map = list()
for i in range(numoflinks * 2):
pag_map.append(np.zeros((46, 46)))
count = np.zeros((46, 46))
for one_rect in oneimg['annoations']:
one_person = map2Ai(one_rect, fscale)
for i in range(numoflinks):
putVecMaps(pag_map[2 * i], pag_map[2 * i + 1], count,
one_person[mid_1[i] - 1][0],
one_person[mid_1[i] - 1][1],
one_person[mid_2[i] - 1][0],
one_person[mid_2[i] - 1][1], stride, 46, 46, sigma,
thre)
# for i in range(len(heat_map)):
# heat_map[i]= cv2.resize(heat_map[i],(46,46),interpolation=cv2.INTER_NEAREST)
return img_pad, heat_map, pag_map, genMask(oneimg,
fscale).astype(np.float32)
def generateLabelMap(img_aug, meta):
thre = 1
crop_size_width = 368
crop_size_height = 368
augmentcols = 368
augmentrows = 368
stride = 8
grid_x = augmentcols / stride
grid_y = augmentrows / stride
sigma = 7.0
heat_map = list()
for i in range(19):
heat_map.append(
np.zeros((crop_size_width / stride, crop_size_height / stride)))
for i in range(18):
if (meta['joint_self']['isVisible'][i] <= 1):
putGaussianMaps(heat_map[i], 368, 368,
meta['joint_self']['joints'][i]['x'],
meta['joint_self']['joints'][i]['y'], stride,
grid_x, grid_y, sigma)
for j in meta['joint_others']:
if (meta['joint_others'][j]['isVisible'][i] <= 1):
putGaussianMaps(heat_map[i], 368, 368,
meta['joint_others'][j]['joints'][i]['x'],
meta['joint_others'][j]['joints'][i]['y'],
stride, grid_x, grid_y, sigma)
### put background channel
for g_y in range(grid_y):
for g_x in range(grid_x):
maximum = 0
for i in range(18):
if maximum < heat_map[i][g_y, g_x]:
maximum = heat_map[i][g_y, g_x]
heat_map[18][g_y, g_x] = max(1.0 - maximum, 0.0)
mid_1 = [2, 9, 10, 2, 12, 13, 2, 3, 4, 3, 2, 6, 7, 6, 2, 1, 1, 15, 16]
mid_2 = [
9, 10, 11, 12, 13, 14, 3, 4, 5, 17, 6, 7, 8, 18, 1, 15, 16, 17, 18
]
#mid_1 = [2, 2, 3, 4, 6, 7, 2, 9, 10, 2, 12, 13, 2, 1, 15, 1, 16, 3, 6]
#mid_2 = [3, 6, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 1, 15, 17, 16, 18, 17, 18]
thre = 1
pag_map = list()
for i in range(38):
pag_map.append(np.zeros((46, 46)))
for i in range(19):
count = np.zeros((46, 46))
jo = meta['joint_self']
if (jo['isVisible'][mid_1[i] - 1] <= 1
and jo['isVisible'][mid_2[i] - 1] <= 1):
putVecMaps(pag_map[2 * i], pag_map[2 * i + 1], count,
jo['joints'][mid_1[i] - 1]['x'],
jo['joints'][mid_1[i] - 1]['y'],
jo['joints'][mid_2[i] - 1]['x'],
jo['joints'][mid_2[i] - 1]['y'], stride, 46, 46, sigma,
thre)
for j in meta['joint_others']:
jo = meta['joint_others'][j]
if (jo['isVisible'][mid_1[i] - 1] <= 1
and jo['isVisible'][mid_2[i] - 1] <= 1):
putVecMaps(pag_map[2 * i], pag_map[2 * i + 1], count,
jo['joints'][mid_1[i] - 1]['x'],
jo['joints'][mid_1[i] - 1]['y'],
jo['joints'][mid_2[i] - 1]['x'],
jo['joints'][mid_2[i] - 1]['y'], stride, 46, 46,
sigma, thre)
return (heat_map, pag_map)