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import os
import sys
import cv2
import math
import copy
import hashlib
import imageio
import numpy as np
import pandas as pd
from scipy import interpolate
from PIL import Image, ImageEnhance, ImageFile
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
ImageFile.LOAD_TRUNCATED_IMAGES = True
sys.path.append("./")
from external.landmark_detection.lib.dataset.augmentation import Augmentation
from external.landmark_detection.lib.dataset.encoder import get_encoder
class AlignmentDataset(Dataset):
def __init__(self, tsv_flie, image_dir="", transform=None,
width=256, height=256, channels=3,
means=(127.5, 127.5, 127.5), scale=1 / 127.5,
classes_num=None, crop_op=True, aug_prob=0.0, edge_info=None, flip_mapping=None, is_train=True,
encoder_type='default',
):
super(AlignmentDataset, self).__init__()
self.use_AAM = True
self.encoder_type = encoder_type
self.encoder = get_encoder(height, width, encoder_type=encoder_type)
self.items = pd.read_csv(tsv_flie, sep="\t")
self.image_dir = image_dir
self.landmark_num = classes_num[0]
self.transform = transform
self.image_width = width
self.image_height = height
self.channels = channels
assert self.image_width == self.image_height
self.means = means
self.scale = scale
self.aug_prob = aug_prob
self.edge_info = edge_info
self.is_train = is_train
std_lmk_5pts = np.array([
196.0, 226.0,
316.0, 226.0,
256.0, 286.0,
220.0, 360.4,
292.0, 360.4], np.float32) / 256.0 - 1.0
std_lmk_5pts = np.reshape(std_lmk_5pts, (5, 2)) # [-1 1]
target_face_scale = 1.0 if crop_op else 1.25
self.augmentation = Augmentation(
is_train=self.is_train,
aug_prob=self.aug_prob,
image_size=self.image_width,
crop_op=crop_op,
std_lmk_5pts=std_lmk_5pts,
target_face_scale=target_face_scale,
flip_rate=0.5,
flip_mapping=flip_mapping,
random_shift_sigma=0.05,
random_rot_sigma=math.pi / 180 * 18,
random_scale_sigma=0.1,
random_gray_rate=0.2,
random_occ_rate=0.4,
random_blur_rate=0.3,
random_gamma_rate=0.2,
random_nose_fusion_rate=0.2)
def _circle(self, img, pt, sigma=1.0, label_type='Gaussian'):
# Check that any part of the gaussian is in-bounds
tmp_size = sigma * 3
ul = [int(pt[0] - tmp_size), int(pt[1] - tmp_size)]
br = [int(pt[0] + tmp_size + 1), int(pt[1] + tmp_size + 1)]
if (ul[0] > img.shape[1] - 1 or ul[1] > img.shape[0] - 1 or
br[0] - 1 < 0 or br[1] - 1 < 0):
# If not, just return the image as is
return img
# 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
if label_type == 'Gaussian':
g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))
else:
g = sigma / (((x - x0) ** 2 + (y - y0) ** 2 + sigma ** 2) ** 1.5)
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], img.shape[1]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], img.shape[0]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], img.shape[1])
img_y = max(0, ul[1]), min(br[1], img.shape[0])
img[img_y[0]:img_y[1], img_x[0]:img_x[1]] = 255 * g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
return img
def _polylines(self, img, lmks, is_closed, color=255, thickness=1, draw_mode=cv2.LINE_AA,
interpolate_mode=cv2.INTER_AREA, scale=4):
h, w = img.shape
img_scale = cv2.resize(img, (w * scale, h * scale), interpolation=interpolate_mode)
lmks_scale = (lmks * scale + 0.5).astype(np.int32)
cv2.polylines(img_scale, [lmks_scale], is_closed, color, thickness * scale, draw_mode)
img = cv2.resize(img_scale, (w, h), interpolation=interpolate_mode)
return img
def _generate_edgemap(self, points, scale=0.25, thickness=1):
h, w = self.image_height, self.image_width
edgemaps = []
for is_closed, indices in self.edge_info:
edgemap = np.zeros([h, w], dtype=np.float32)
# align_corners: False.
part = copy.deepcopy(points[np.array(indices)])
part = self._fit_curve(part, is_closed)
part[:, 0] = np.clip(part[:, 0], 0, w - 1)
part[:, 1] = np.clip(part[:, 1], 0, h - 1)
edgemap = self._polylines(edgemap, part, is_closed, 255, thickness)
edgemaps.append(edgemap)
edgemaps = np.stack(edgemaps, axis=0) / 255.0
edgemaps = torch.from_numpy(edgemaps).float().unsqueeze(0)
edgemaps = F.interpolate(edgemaps, size=(int(w * scale), int(h * scale)), mode='bilinear',
align_corners=False).squeeze()
return edgemaps
def _fit_curve(self, lmks, is_closed=False, density=5):
try:
x = lmks[:, 0].copy()
y = lmks[:, 1].copy()
if is_closed:
x = np.append(x, x[0])
y = np.append(y, y[0])
tck, u = interpolate.splprep([x, y], s=0, per=is_closed, k=3)
# bins = (x.shape[0] - 1) * density + 1
# lmk_x, lmk_y = interpolate.splev(np.linspace(0, 1, bins), f)
intervals = np.array([])
for i in range(len(u) - 1):
intervals = np.concatenate((intervals, np.linspace(u[i], u[i + 1], density, endpoint=False)))
if not is_closed:
intervals = np.concatenate((intervals, [u[-1]]))
lmk_x, lmk_y = interpolate.splev(intervals, tck, der=0)
# der_x, der_y = interpolate.splev(intervals, tck, der=1)
curve_lmks = np.stack([lmk_x, lmk_y], axis=-1)
# curve_ders = np.stack([der_x, der_y], axis=-1)
# origin_indices = np.arange(0, curve_lmks.shape[0], density)
return curve_lmks
except:
return lmks
def _image_id(self, image_path):
if not os.path.exists(image_path):
image_path = os.path.join(self.image_dir, image_path)
return hashlib.md5(open(image_path, "rb").read()).hexdigest()
def _load_image(self, image_path):
if not os.path.exists(image_path):
image_path = os.path.join(self.image_dir, image_path)
try:
# img = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)#HWC, BGR, [0-255]
img = cv2.imread(image_path, cv2.IMREAD_COLOR) # HWC, BGR, [0-255]
assert img is not None and len(img.shape) == 3 and img.shape[2] == 3
except:
try:
img = imageio.imread(image_path) # HWC, RGB, [0-255]
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # HWC, BGR, [0-255]
assert img is not None and len(img.shape) == 3 and img.shape[2] == 3
except:
try:
gifImg = imageio.mimread(image_path) # BHWC, RGB, [0-255]
img = gifImg[0] # HWC, RGB, [0-255]
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # HWC, BGR, [0-255]
assert img is not None and len(img.shape) == 3 and img.shape[2] == 3
except:
img = None
return img
def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center, to_center):
cosv = math.cos(angle)
sinv = math.sin(angle)
fx, fy = from_center
tx, ty = to_center
acos = scale * cosv
asin = scale * sinv
a0 = acos
a1 = -asin
a2 = tx - acos * fx + asin * fy + shift_xy[0]
b0 = asin
b1 = acos
b2 = ty - asin * fx - acos * fy + shift_xy[1]
rot_scale_m = np.array([
[a0, a1, a2],
[b0, b1, b2],
[0.0, 0.0, 1.0]
], np.float32)
return rot_scale_m
def _transformPoints2D(self, points, matrix):
"""
points (nx2), matrix (3x3) -> points (nx2)
"""
dtype = points.dtype
# nx3
points = np.concatenate([points, np.ones_like(points[:, [0]])], axis=1)
points = points @ np.transpose(matrix) # nx3
points = points[:, :2] / points[:, [2, 2]]
return points.astype(dtype)
def _transformPerspective(self, image, matrix, target_shape):
"""
image, matrix3x3 -> transformed_image
"""
return cv2.warpPerspective(
image, matrix,
dsize=(target_shape[1], target_shape[0]),
flags=cv2.INTER_LINEAR, borderValue=0)
def _norm_points(self, points, h, w, align_corners=False):
if align_corners:
# [0, SIZE-1] -> [-1, +1]
des_points = points / torch.tensor([w - 1, h - 1]).to(points).view(1, 2) * 2 - 1
else:
# [-0.5, SIZE-0.5] -> [-1, +1]
des_points = (points * 2 + 1) / torch.tensor([w, h]).to(points).view(1, 2) - 1
des_points = torch.clamp(des_points, -1, 1)
return des_points
def _denorm_points(self, points, h, w, align_corners=False):
if align_corners:
# [-1, +1] -> [0, SIZE-1]
des_points = (points + 1) / 2 * torch.tensor([w - 1, h - 1]).to(points).view(1, 1, 2)
else:
# [-1, +1] -> [-0.5, SIZE-0.5]
des_points = ((points + 1) * torch.tensor([w, h]).to(points).view(1, 1, 2) - 1) / 2
return des_points
def __len__(self):
return len(self.items)
def __getitem__(self, index):
sample = dict()
image_path = self.items.iloc[index, 0]
landmarks_5pts = self.items.iloc[index, 1]
landmarks_5pts = np.array(list(map(float, landmarks_5pts.split(","))), dtype=np.float32).reshape(5, 2)
landmarks_target = self.items.iloc[index, 2]
landmarks_target = np.array(list(map(float, landmarks_target.split(","))), dtype=np.float32).reshape(
self.landmark_num, 2)
scale = float(self.items.iloc[index, 3])
center_w, center_h = float(self.items.iloc[index, 4]), float(self.items.iloc[index, 5])
if len(self.items.iloc[index]) > 6:
tags = np.array(list(map(lambda x: int(float(x)), self.items.iloc[index, 6].split(","))))
else:
tags = np.array([])
# image & keypoints alignment
image_path = image_path.replace('\\', '/')
# wflw testset
image_path = image_path.replace(
'//msr-facestore/Workspace/MSRA_EP_Allergan/users/yanghuan/training_data/wflw/rawImages/', '')
# trainset
image_path = image_path.replace('./rawImages/', '')
image_path = os.path.join(self.image_dir, image_path)
# image path
sample["image_path"] = image_path
img = self._load_image(image_path) # HWC, BGR, [0, 255]
assert img is not None
# augmentation
# landmarks_target = [-0.5, edge-0.5]
img, landmarks_target, matrix = \
self.augmentation.process(img, landmarks_target, landmarks_5pts, scale, center_w, center_h)
landmarks = self._norm_points(torch.from_numpy(landmarks_target), self.image_height, self.image_width)
sample["label"] = [landmarks, ]
if self.use_AAM:
pointmap = self.encoder.generate_heatmap(landmarks_target)
edgemap = self._generate_edgemap(landmarks_target)
sample["label"] += [pointmap, edgemap]
sample['matrix'] = matrix
# image normalization
img = img.transpose(2, 0, 1).astype(np.float32) # CHW, BGR, [0, 255]
img[0, :, :] = (img[0, :, :] - self.means[0]) * self.scale
img[1, :, :] = (img[1, :, :] - self.means[1]) * self.scale
img[2, :, :] = (img[2, :, :] - self.means[2]) * self.scale
sample["data"] = torch.from_numpy(img) # CHW, BGR, [-1, 1]
sample["tags"] = tags
return sample
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