def draw_face_edges(self, keypoints, part_list, transform_A, size, add_dist_map, outsize=(256,256)):
w, h = size
w_o, h_o = outsize
edge_len = 3 # interpolate 3 keypoints to form a curve when drawing edges
# edge map for face region from keypoints
im_edges = np.zeros((h_o, w_o), np.uint8) # edge map for all edges
dist_tensor = 0
e = 1
for edge_list in part_list:
for edge in edge_list:
im_edge = np.zeros((h_o, w_o), np.uint8) # edge map for the current edge
for i in range(0, max(1, len(edge)-1), edge_len-1): # divide a long edge into multiple small edges when drawing
sub_edge = edge[i:i+edge_len]
x = keypoints[sub_edge, 0].astype(np.float32)/ w * w_o
y = keypoints[sub_edge, 1].astype(np.float32)/ h * h_o
curve_x, curve_y = interpPoints(x.astype(np.int), y.astype(np.int)) # interp keypoints to get the curve shape
drawEdge(im_edges, curve_x, curve_y, bw=0)
if add_dist_map:
drawEdge(im_edge, curve_x, curve_y, bw=0)
if add_dist_map: # add distance transform map on each facial part
im_edge_tr = Image.fromarray(im_edge).resize(self.osize, Image.NEAREST)
im_edge_tr = np.asarray(im_edge_tr)
im_dist = cv2.distanceTransform(255-im_edge_tr, cv2.DIST_L1, 3)
im_dist = np.clip((im_dist / 3), 0, 255).astype(np.uint8)
im_dist = Image.fromarray(im_dist)
tensor_cropped = transform_A(im_dist)
dist_tensor = tensor_cropped if e == 1 else torch.cat([dist_tensor, tensor_cropped])
e += 1
return im_edges, dist_tensor
def draw_face_edges(self, keypoints, part_list, transform_A, size, add_dist_map):
w, h = size
edge_len = 3 # interpolate 3 keypoints to form a curve when drawing edges
# edge map for face region from keypoints
im_edges = np.zeros((h, w), np.uint8) # edge map for all edges
dist_tensor = 0
e = 1
for edge_list in part_list:
for edge in edge_list:
im_edge = np.zeros((h, w), np.uint8) # edge map for the current edge
for i in range(0, max(1, len(edge)-1), edge_len-1): # divide a long edge into multiple small edges when drawing
sub_edge = edge[i:i+edge_len]
x = keypoints[sub_edge, 0]
y = keypoints[sub_edge, 1]
curve_x, curve_y = interpPoints(x, y) # interp keypoints to get the curve shape
drawEdge(im_edges, curve_x, curve_y)
if add_dist_map:
drawEdge(im_edge, curve_x, curve_y)
if add_dist_map: # add distance transform map on each facial part
im_dist = cv2.distanceTransform(255-im_edge, cv2.DIST_L1, 3)
im_dist = np.clip((im_dist / 3), 0, 255).astype(np.uint8)
im_dist = Image.fromarray(im_dist)
tensor_cropped = transform_A(self.crop(im_dist))
dist_tensor = tensor_cropped if e == 1 else torch.cat([dist_tensor, tensor_cropped])
e += 1
return im_edges, dist_tensor
def get_face_image(self, keypoints, transform_L, size, bw):
w, h = size
edge_len = 3 # interpolate 3 keypoints to form a curve when drawing edges
# edge map for face region from keypoints
im_edges = np.zeros((h, w), np.uint8) # edge map for all edges
for edge_list in self.part_list:
for edge in edge_list:
for i in range(0, max(1, len(edge)-1), edge_len-1): # divide a long edge into multiple small edges when drawing
sub_edge = edge[i:i+edge_len]
x = keypoints[sub_edge, 0]
y = keypoints[sub_edge, 1]
curve_x, curve_y = interpPoints(x, y) # interp keypoints to get the curve shape
drawEdge(im_edges, curve_x, curve_y, bw=1)
input_tensor = transform_L(Image.fromarray(im_edges))
return input_tensor