def main():
label_map = cv2.imread('./data/labelmap_filled.png', -1)
label_inner = getinnerpts('./data/labelmap_filled.png')
mask_inner = getinnerpts('./data/img_input_mask.png')
input_mask = cv2.imread('./data/img_input_mask.png')
disp = input_mask.copy()
# warp_label_map(label_inner, label_map, mask_inner, head_label, disp)
# warp_label_map(label_inner, label_map, mask_inner, torso_label, disp)
# warp_label_map(label_inner, label_map, mask_inner, left_down_label, disp)
# warp_label_map(label_inner, label_map, mask_inner, right_up_label, disp)
# warp_label_map(label_inner, label_map, mask_inner, right_down_label, disp)
# warp_label_map(label_inner, label_map, mask_inner, left_up_label, disp)
#
# cv2.imwrite('./data/labelmap_warp_coarse.png', disp)
labelmapfilling(mask_inner, './data/labelmap_warp_coarse.png',
'./data/labelmap_warp_filled.png')
def warpnfill(mask_input, mask_smpl, map_smpl, warp_coarse, warp_filled, ifoutlier=False):
contour_input = pc.getContour(mask_input, mode=0)
contour_smpl = pc.getContour(mask_smpl, mode=0)
phi = pc.dpBoundarymatch(contour_input, contour_smpl, 32)
# to check the contour point match
# pc.dispCorres([224, 224], contour_input, contour_smpl, phi)
# exit()
inner_smpl = wp.getinnerpts(mask_smpl)
weights = wp.calweights(contour_smpl, phi, inner_smpl)
wp.warpmap(mask_input, map_smpl, warp_coarse, contour_input, inner_smpl, weights)
inner_input = wp.getinnerpts(mask_input)
input_contour_all = pc.getContour(mask_input, mode=1)
hf.holefilling(warp_coarse, warp_filled, inner_input, input_contour_all, mask_input_front, outlier=ifoutlier)
def getlabelarea(initial_map_path, smpl_map_path, part_label):
initial_map = cv2.imread(initial_map_path, -1)
smpl_map = cv2.imread(smpl_map_path, -1)
initial_inner = wp.getinnerpts(initial_map_path)
smpl_inner = wp.getinnerpts(smpl_map_path)
initial_output = np.zeros((224, 224), dtype=np.uint8)
smpl_output = np.zeros((224, 224), dtype=np.uint8)
for i in range(initial_inner.shape[0]):
x, y = initial_inner[i, :]
if (initial_map[y, x] == part_label).all() == True:
initial_output[y, x] = 255
for i in range(smpl_inner.shape[0]):
x, y = smpl_inner[i, :]
if (smpl_map[y, x] == part_label).all() == True:
smpl_output[y, x] = 255
return initial_output, smpl_output
def initialcontours(front_path, back_path):
front = cv2.imread(front_path, -1)
inner_pts1 = getinnerpts(front_path)
inner_pts1 = inner_pts1.tolist()
if len(front.shape) == 3:
front = cv2.cvtColor(front, cv2.COLOR_BGR2GRAY)
contours1, hierarchy = cv2.findContours(front, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours1 = np.array(contours1)
back = cv2.imread(back_path, -1)
inner_pts2 = getinnerpts(back_path)
inner_pts2 = inner_pts2.tolist()
if len(back.shape) == 3:
back = cv2.cvtColor(back, cv2.COLOR_BGR2GRAY)
contours2, hierarchy = cv2.findContours(back, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours2 = np.array(contours2)
return contours1, contours2, inner_pts1, inner_pts2
def main():
# left_up_initial, left_up_smpl = getlabelarea('./data/labelmap_warp_filled.png', './data/labelmap_filled.png',
# left_up_label)
# left_up_map = label_match(left_up_initial, left_up_smpl)
#
# left_down_initial, left_down_smpl = getlabelarea('./data/labelmap_warp_filled.png', './data/labelmap_filled.png',
# left_down_label)
#
# left_down_map = label_match(left_down_initial, left_down_smpl)
#
# torso_initial, torso_smpl = getlabelarea('./data/labelmap_warp_filled.png', './data/labelmap_filled.png',
# torso_label)
# torso_map = label_match(torso_initial, torso_smpl)
#
# occlusion_pairs = pixel_occlusion('./data/labelmap_warp_filled.png', left_up_initial, left_up_map, torso_map,
# left_down_map, './data/img_depth.png')
# occlusion_mask_init = cv2.imread('./data/labelmap_warp_filled.png', -1)
# for i in range(occlusion_pairs.shape[0]):
# x1, y1, x2, y2 = occlusion_pairs[i, :]
# occlusion_mask_init[y1, x1] = [255, 255, 255]
# occlusion_mask_init[y2, x2] = [255, 255, 255]
#
# cv2.imwrite('./data/occlusion_mask_init.png', occlusion_mask_init)
# occlusion_mask_dilate = dilate_occlusion_mask('./data/occlusion_mask_init.png')
# cv2.imwrite('./data/occlusion_mask_dilate.png', occlusion_mask_dilate)
mask_O = []
occlusion_mask_dilate = cv2.imread('./data/occlusion_mask_dilate.png', -1)
for i in range(occlusion_mask_dilate.shape[0]):
for j in range(occlusion_mask_dilate.shape[1]):
if (occlusion_mask_dilate[i, j] == [255, 255, 255]).all() == True:
mask_O.append([j, i])
mask_O = np.array(mask_O)
label_inner = wp.getinnerpts('./data/labelmap_warp_filled.png')
contours = conbine_main_parts('./data/labelmap_warp_filled.png',
label_inner, left_up_label)
contour_points, start, end = find_occlusion_endpoints(contours, mask_O)
print(start, end)
def main():
mask_smpl = './data/img_smpl_mask.png'
pred_vert = './savetxt/pred_vertices.txt'
head = './smpl_seg_raw/refined/head.txt'
torso = './smpl_seg_raw/refined/torso.txt'
left_up = './smpl_seg_raw/refined/left_arm_hand.txt'
left_down = './smpl_seg_raw/refined/left_leg_foot.txt'
right_up = './smpl_seg_raw/refined/right_arm_hand.txt'
right_down = './smpl_seg_raw/refined/right_leg_foot.txt'
head_index = np.loadtxt(head, dtype=np.int)
torso_index = np.loadtxt(torso, dtype=np.int)
left_up_index = np.loadtxt(left_up, dtype=np.int)
left_down_index = np.loadtxt(left_down, dtype=np.int)
right_up_index = np.loadtxt(right_up, dtype=np.int)
right_down_index = np.loadtxt(right_down, dtype=np.int)
inner_pts = getinnerpts(mask_smpl)
rect1 = cv2.minAreaRect(inner_pts)
pred_v = np.loadtxt(pred_vert, dtype=np.float32)
pred_v2d = pred_v[:, :2] + 1 # to eliminate negative values
rect2 = cv2.minAreaRect(pred_v2d)
a = rect1[1][0] / rect2[1][0]
b = rect1[1][1] / rect2[1][1]
c = rect1[0][0] - rect2[0][0]
d = rect1[0][1] - rect2[0][1]
print(a, b, c, d)
pred_v2d[:, 0] = (pred_v2d[:, 0] - rect2[0][0]) * a + c
pred_v2d[:, 1] = (pred_v2d[:, 1] - rect2[0][1]) * b + d
pred_v2d = np.array(pred_v2d, dtype=np.int)
mask = cv2.imread(mask_smpl)
# mask = np.zeros((mask.shape[0], mask.shape[1]))
# for i in range(pred_v2d.shape[0]):
# x, y = pred_v2d[i, :]
# cv2.circle(mask, (x, y), 1, (255, 0, 0))
for i in range(head_index.shape[0]):
x, y = pred_v2d[head_index[i], :]
cv2.circle(mask, (x, y), 1, (193, 182, 255))
for i in range(torso_index.shape[0]):
x, y = pred_v2d[torso_index[i], :]
cv2.circle(mask, (x, y), 1, (144, 128, 112))
for i in range(left_down_index.shape[0]):
x, y = pred_v2d[left_down_index[i], :]
cv2.circle(mask, (x, y), 1, (0, 255, 127))
for i in range(right_up_index.shape[0]):
x, y = pred_v2d[right_up_index[i], :]
cv2.circle(mask, (x, y), 1, (0, 69, 255))
for i in range(right_down_index.shape[0]):
x, y = pred_v2d[right_down_index[i], :]
cv2.circle(mask, (x, y), 1, (205, 250, 255))
for i in range(left_up_index.shape[0]):
x, y = pred_v2d[left_up_index[i], :]
cv2.circle(mask, (x, y), 1, (139, 139, 0))
cv2.imshow('label', mask)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite('./data/labelmap_coarse.png', mask)
labelmapfilling(inner_pts, './data/labelmap_coarse.png',
'./data/labelmap_filled.png')