"""
This file contains functions that are used to perform data augmentation.
"""
from turtle import reset
import cv2
import io
import torch
import numpy as np
import scipy.misc
from PIL import Image
from rembg.bg import remove
from torchvision.models import detection
from lib.pymaf.core import constants
from lib.pymaf.utils.streamer import aug_matrix
from lib.common.cloth_extraction import load_segmentation
from torchvision import transforms
def load_img(img_file):
img = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not img_file.endswith("png"):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
else:
img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGR)
return img
def get_bbox(img, det):
input = np.float32(img)
input = (input / 255.0 -
(0.5, 0.5, 0.5)) / (0.5, 0.5, 0.5) # TO [-1.0, 1.0]
input = input.transpose(2, 0, 1) # TO [3 x H x W]
bboxes, probs = det(torch.from_numpy(input).float().unsqueeze(0))
probs = probs.unsqueeze(3)
bboxes = (bboxes * probs).sum(dim=1, keepdim=True) / probs.sum(
dim=1, keepdim=True)
bbox = bboxes[0, 0, 0].cpu().numpy()
return bbox
def get_transformer(input_res):
image_to_tensor = transforms.Compose([
transforms.Resize(input_res),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
mask_to_tensor = transforms.Compose([
transforms.Resize(input_res),
transforms.ToTensor(),
transforms.Normalize((0.0, ), (1.0, ))
])
image_to_pymaf_tensor = transforms.Compose([
transforms.Resize(size=224),
transforms.Normalize(mean=constants.IMG_NORM_MEAN,
std=constants.IMG_NORM_STD)
])
image_to_pixie_tensor = transforms.Compose([transforms.Resize(224)])
def image_to_hybrik_tensor(img):
# mean
img[0].add_(-0.406)
img[1].add_(-0.457)
img[2].add_(-0.480)
# std
img[0].div_(0.225)
img[1].div_(0.224)
img[2].div_(0.229)
return img
return [
image_to_tensor, mask_to_tensor, image_to_pymaf_tensor,
image_to_pixie_tensor, image_to_hybrik_tensor
]
def process_image(img_file,
hps_type,
input_res=512,
device=None,
seg_path=None):
"""Read image, do preprocessing and possibly crop it according to the bounding box.
If there are bounding box annotations, use them to crop the image.
If no bounding box is specified but openpose detections are available, use them to get the bounding box.
"""
[
image_to_tensor, mask_to_tensor, image_to_pymaf_tensor,
image_to_pixie_tensor, image_to_hybrik_tensor
] = get_transformer(input_res)
img_ori = load_img(img_file)
in_height, in_width, _ = img_ori.shape
M = aug_matrix(in_width, in_height, input_res * 2, input_res * 2)
# from rectangle to square
img_for_crop = cv2.warpAffine(img_ori,
M[0:2, :], (input_res * 2, input_res * 2),
flags=cv2.INTER_CUBIC)
# detection for bbox
detector = detection.maskrcnn_resnet50_fpn(pretrained=True)
detector.eval()
predictions = detector(
[torch.from_numpy(img_for_crop).permute(2, 0, 1) / 255.])[0]
human_ids = torch.logical_and(
predictions["labels"] == 1,
predictions["scores"] == predictions["scores"].max()).nonzero().squeeze(1)
bbox = predictions["boxes"][human_ids, :].flatten().detach().cpu().numpy()
width = bbox[2] - bbox[0]
height = bbox[3] - bbox[1]
center = np.array([(bbox[0] + bbox[2]) / 2.0,
(bbox[1] + bbox[3]) / 2.0])
scale = max(height, width) / 180
if hps_type == 'hybrik':
img_np = crop_for_hybrik(img_for_crop, center,
np.array([scale * 180, scale * 180]))
else:
img_np, cropping_parameters = crop(img_for_crop, center, scale,
(input_res, input_res))
with torch.no_grad():
buf = io.BytesIO()
Image.fromarray(img_np).save(buf, format='png')
img_pil = Image.open(io.BytesIO(remove(
buf.getvalue()))).convert("RGBA")
# for icon
img_rgb = image_to_tensor(img_pil.convert("RGB"))
img_mask = torch.tensor(1.0) - (mask_to_tensor(img_pil.split()[-1]) <
torch.tensor(0.5)).float()
img_tensor = img_rgb * img_mask
# for hps
img_hps = img_np.astype(np.float32) / 255.
img_hps = torch.from_numpy(img_hps).permute(2, 0, 1)
if hps_type == 'bev':
img_hps = img_np[:, :, [2, 1, 0]]
elif hps_type == 'hybrik':
img_hps = image_to_hybrik_tensor(img_hps).unsqueeze(0).to(device)
elif hps_type != 'pixie':
img_hps = image_to_pymaf_tensor(img_hps).unsqueeze(0).to(device)
else:
img_hps = image_to_pixie_tensor(img_hps).unsqueeze(0).to(device)
# uncrop params
uncrop_param = {
'center': center,
'scale': scale,
'ori_shape': img_ori.shape,
'box_shape': img_np.shape,
'crop_shape': img_for_crop.shape,
'M': M
}
if not (seg_path is None):
segmentations = load_segmentation(seg_path, (in_height, in_width))
seg_coord_normalized = []
for seg in segmentations:
coord_normalized = []
for xy in seg['coordinates']:
xy_h = np.vstack((xy[:, 0], xy[:, 1], np.ones(len(xy)))).T
warped_indeces = M[0:2, :] @ xy_h[:, :, None]
warped_indeces = np.array(warped_indeces).astype(int)
warped_indeces.resize((warped_indeces.shape[:2]))
# cropped_indeces = crop_segmentation(warped_indeces, center, scale, (input_res, input_res), img_np.shape)
cropped_indeces = crop_segmentation(warped_indeces,
(input_res, input_res),
cropping_parameters)
indices = np.vstack(
(cropped_indeces[:, 0], cropped_indeces[:, 1])).T
# Convert to NDC coordinates
seg_cropped_normalized = 2 * (indices / input_res) - 1
# Don't know why we need to divide by 50 but it works ¯\_(ツ)_/¯ (probably some scaling factor somewhere)
# Divide only by 45 on the horizontal axis to take the curve of the human body into account
seg_cropped_normalized[:,
0] = (1 /
40) * seg_cropped_normalized[:, 0]
seg_cropped_normalized[:,
1] = (1 /
50) * seg_cropped_normalized[:, 1]
coord_normalized.append(seg_cropped_normalized)
seg['coord_normalized'] = coord_normalized
seg_coord_normalized.append(seg)
return img_tensor, img_hps, img_ori, img_mask, uncrop_param, seg_coord_normalized
return img_tensor, img_hps, img_ori, img_mask, uncrop_param
def get_transform(center, scale, res):
"""Generate transformation matrix."""
h = 200 * scale
t = np.zeros((3, 3))
t[0, 0] = float(res[1]) / h
t[1, 1] = float(res[0]) / h
t[0, 2] = res[1] * (-float(center[0]) / h + .5)
t[1, 2] = res[0] * (-float(center[1]) / h + .5)
t[2, 2] = 1
return t
def transform(pt, center, scale, res, invert=0):
"""Transform pixel location to different reference."""
t = get_transform(center, scale, res)
if invert:
t = np.linalg.inv(t)
new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T
new_pt = np.dot(t, new_pt)
return np.around(new_pt[:2]).astype(np.int16)
def crop(img, center, scale, res):
"""Crop image according to the supplied bounding box."""
# Upper left point
ul = np.array(transform([0, 0], center, scale, res, invert=1))
# Bottom right point
br = np.array(transform(res, center, scale, res, invert=1))
new_shape = [br[1] - ul[1], br[0] - ul[0]]
if len(img.shape) > 2:
new_shape += [img.shape[2]]
new_img = np.zeros(new_shape)
# Range to fill new array
new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0]
new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1]
# Range to sample from original image
old_x = max(0, ul[0]), min(len(img[0]), br[0])
old_y = max(0, ul[1]), min(len(img), br[1])
new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1],
old_x[0]:old_x[1]]
if len(img.shape) == 2:
new_img = np.array(Image.fromarray(new_img).resize(res))
else:
new_img = np.array(
Image.fromarray(new_img.astype(np.uint8)).resize(res))
return new_img, (old_x, new_x, old_y, new_y, new_shape)
def crop_segmentation(org_coord, res, cropping_parameters):
old_x, new_x, old_y, new_y, new_shape = cropping_parameters
new_coord = np.zeros((org_coord.shape))
new_coord[:, 0] = new_x[0] + (org_coord[:, 0] - old_x[0])
new_coord[:, 1] = new_y[0] + (org_coord[:, 1] - old_y[0])
new_coord[:, 0] = res[0] * (new_coord[:, 0] / new_shape[1])
new_coord[:, 1] = res[1] * (new_coord[:, 1] / new_shape[0])
return new_coord
def crop_for_hybrik(img, center, scale):
inp_h, inp_w = (256, 256)
trans = get_affine_transform(center, scale, 0, [inp_w, inp_h])
new_img = cv2.warpAffine(img,
trans, (int(inp_w), int(inp_h)),
flags=cv2.INTER_LINEAR)
return new_img
def get_affine_transform(center,
scale,
rot,
output_size,
shift=np.array([0, 0], dtype=np.float32),
inv=0):
def get_dir(src_point, rot_rad):
"""Rotate the point by `rot_rad` degree."""
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
src_result = [0, 0]
src_result[0] = src_point[0] * cs - src_point[1] * sn
src_result[1] = src_point[0] * sn + src_point[1] * cs
return src_result
def get_3rd_point(a, b):
"""Return vector c that perpendicular to (a - b)."""
direct = a - b
return b + np.array([-direct[1], direct[0]], dtype=np.float32)
if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
scale = np.array([scale, scale])
scale_tmp = scale
src_w = scale_tmp[0]
dst_w = output_size[0]
dst_h = output_size[1]
rot_rad = np.pi * rot / 180
src_dir = get_dir([0, src_w * -0.5], rot_rad)
dst_dir = np.array([0, dst_w * -0.5], np.float32)
src = np.zeros((3, 2), dtype=np.float32)
dst = np.zeros((3, 2), dtype=np.float32)
src[0, :] = center + scale_tmp * shift
src[1, :] = center + src_dir + scale_tmp * shift
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
if inv:
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
else:
trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
return trans
def corner_align(ul, br):
if ul[1] - ul[0] != br[1] - br[0]:
ul[1] = ul[0] + br[1] - br[0]
return ul, br
def uncrop(img, center, scale, orig_shape):
"""'Undo' the image cropping/resizing.
This function is used when evaluating mask/part segmentation.
"""
res = img.shape[:2]
# Upper left point
ul = np.array(transform([0, 0], center, scale, res, invert=1))
# Bottom right point
br = np.array(transform(res, center, scale, res, invert=1))
# quick fix
ul, br = corner_align(ul, br)
# size of cropped image
crop_shape = [br[1] - ul[1], br[0] - ul[0]]
new_img = np.zeros(orig_shape, dtype=np.uint8)
# Range to fill new array
new_x = max(0, -ul[0]), min(br[0], orig_shape[1]) - ul[0]
new_y = max(0, -ul[1]), min(br[1], orig_shape[0]) - ul[1]
# Range to sample from original image
old_x = max(0, ul[0]), min(orig_shape[1], br[0])
old_y = max(0, ul[1]), min(orig_shape[0], br[1])
img = np.array(Image.fromarray(img.astype(np.uint8)).resize(crop_shape))
new_img[old_y[0]:old_y[1], old_x[0]:old_x[1]] = img[new_y[0]:new_y[1],
new_x[0]:new_x[1]]
return new_img
def rot_aa(aa, rot):
"""Rotate axis angle parameters."""
# pose parameters
R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0],
[np.sin(np.deg2rad(-rot)),
np.cos(np.deg2rad(-rot)), 0], [0, 0, 1]])
# find the rotation of the body in camera frame
per_rdg, _ = cv2.Rodrigues(aa)
# apply the global rotation to the global orientation
resrot, _ = cv2.Rodrigues(np.dot(R, per_rdg))
aa = (resrot.T)[0]
return aa
def flip_img(img):
"""Flip rgb images or masks.
channels come last, e.g. (256,256,3).
"""
img = np.fliplr(img)
return img
def flip_kp(kp, is_smpl=False):
"""Flip keypoints."""
if len(kp) == 24:
if is_smpl:
flipped_parts = constants.SMPL_JOINTS_FLIP_PERM
else:
flipped_parts = constants.J24_FLIP_PERM
elif len(kp) == 49:
if is_smpl:
flipped_parts = constants.SMPL_J49_FLIP_PERM
else:
flipped_parts = constants.J49_FLIP_PERM
kp = kp[flipped_parts]
kp[:, 0] = -kp[:, 0]
return kp
def flip_pose(pose):
"""Flip pose.
The flipping is based on SMPL parameters.
"""
flipped_parts = constants.SMPL_POSE_FLIP_PERM
pose = pose[flipped_parts]
# we also negate the second and the third dimension of the axis-angle
pose[1::3] = -pose[1::3]
pose[2::3] = -pose[2::3]
return pose
def normalize_2d_kp(kp_2d, crop_size=224, inv=False):
# Normalize keypoints between -1, 1
if not inv:
ratio = 1.0 / crop_size
kp_2d = 2.0 * kp_2d * ratio - 1.0
else:
ratio = 1.0 / crop_size
kp_2d = (kp_2d + 1.0) / (2 * ratio)
return kp_2d
def generate_heatmap(joints, heatmap_size, sigma=1, joints_vis=None):
'''
param joints: [num_joints, 3]
param joints_vis: [num_joints, 3]
return: target, target_weight(1: visible, 0: invisible)
'''
num_joints = joints.shape[0]
device = joints.device
cur_device = torch.device(device.type, device.index)
if not hasattr(heatmap_size, '__len__'):
# width height
heatmap_size = [heatmap_size, heatmap_size]
assert len(heatmap_size) == 2
target_weight = np.ones((num_joints, 1), dtype=np.float32)
if joints_vis is not None:
target_weight[:, 0] = joints_vis[:, 0]
target = torch.zeros((num_joints, heatmap_size[1], heatmap_size[0]),
dtype=torch.float32,
device=cur_device)
tmp_size = sigma * 3
for joint_id in range(num_joints):
mu_x = int(joints[joint_id][0] * heatmap_size[0] + 0.5)
mu_y = int(joints[joint_id][1] * heatmap_size[1] + 0.5)
# Check that any part of the gaussian is in-bounds
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= heatmap_size[0] or ul[1] >= heatmap_size[1] \
or br[0] < 0 or br[1] < 0:
# If not, just return the image as is
target_weight[joint_id] = 0
continue
# # Generate gaussian
size = 2 * tmp_size + 1
# x = np.arange(0, size, 1, np.float32)
# y = x[:, np.newaxis]
# x0 = y0 = size // 2
# # The gaussian is not normalized, we want the center value to equal 1
# g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
# g = torch.from_numpy(g.astype(np.float32))
x = torch.arange(0, size, dtype=torch.float32, device=cur_device)
y = x.unsqueeze(-1)
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
g = torch.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], heatmap_size[0]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], heatmap_size[1]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], heatmap_size[0])
img_y = max(0, ul[1]), min(br[1], heatmap_size[1])
v = target_weight[joint_id]
if v > 0.5:
target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
return target, target_weight