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#################################################
# Copyright (c) 2021-present, xiaobing.ai, Inc. #
# All rights reserved. #
#################################################
# CV Research, DEV(USA) xiaobing. #
# written by [email protected] #
#################################################
##### python internal and external package
import os
import cv2
import torch
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
import math
##### self defined package
from lib.models.ldmk.hrnet import LandmarkDetector
gauss_kernel = None
class ldmkDetector(nn.Module):
def __init__(self, cfg):
super(ldmkDetector, self).__init__()
if cfg.model.ldmk.model_name == "h3r":
self.model = LandmarkDetector(cfg.model.ldmk.model_path)
else:
print("Error: the model {} of landmark is not exists".format(cfg.model.ldmk.model_name))
self.model.eval()
self.model.cuda()
self.size = cfg.model.ldmk.img_size # 256
self.landmark_transform = transforms.Compose([
transforms.Resize(size=(self.size, self.size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
def _transform(self, img):
h, w, c = img.shape
img = img[:, :, ::-1]
img = Image.fromarray(img.astype(np.uint8))
img = self.landmark_transform(img)
img = img.type(torch.FloatTensor).unsqueeze(0)
img = img.cuda()
return img, h, w
def forward(self, img):
img, h, w = self._transform(img)
_, landmarks = self.model(img)
landmarks = landmarks / torch.Tensor([self.size / w, self.size / h]).reshape(1, 1, 2).cuda()
landmarks = landmarks.detach().cpu().numpy()
return landmarks
class ldmk3dDetector(nn.Module):
def __init__(self, cfg):
super(ldmk3dDetector, self).__init__()
self.model_3d = torch.jit.load(cfg.model.ldmk_3d.model_path)
self.model_depth = torch.jit.load(cfg.model.ldmk_3d.model_depth_path)
self.model_3d.eval()
self.model_depth.eval()
self.model_3d.cuda()
self.model_depth.cuda()
self.size = cfg.model.ldmk.img_size # 256
self.landmark_transform = transforms.Compose([
transforms.Resize(size=(self.size, self.size)),
transforms.ToTensor(),
])
def _transform(self, img):
h, w, c = img.shape
img = img[:, :, ::-1]
img = Image.fromarray(img.astype(np.uint8))
img = self.landmark_transform(img)
img = img.type(torch.FloatTensor).unsqueeze(0)
img = img.cuda()
return img, h, w
def get_cropped_img(self, img, box):
center = torch.tensor(
[box[2] - (box[2] - box[0]) / 2.0, box[3] - (box[3] - box[1]) / 2.0])
center[1] = center[1] - (box[3] - box[1]) * 0.12
scale = (box[2] - box[0] + box[3] - box[1]) / 192
inp = crop(img, center, scale)
return inp, center, scale
def forward(self, img, boxes):
ldmks = []
for box in boxes:
img_cropped, center, scale = self.get_cropped_img(img, box)
img_cropped, h, w = self._transform(img_cropped)
out = self.model_3d(img_cropped).detach()
out = out.cpu().numpy()
pts, pts_img, scores = get_preds_fromhm(out, center.numpy(), scale)
pts, pts_img = torch.from_numpy(pts), torch.from_numpy(pts_img)
pts, pts_img = pts.view(68, 2) * 4, pts_img.view(68, 2)
scores = scores.squeeze(0)
heatmaps = np.zeros((68, 256, 256), dtype=np.float32)
for i in range(68):
if pts[i, 0] > 0 and pts[i, 1] > 0:
heatmaps[i] = draw_gaussian(
heatmaps[i], pts[i], 2)
heatmaps = torch.from_numpy(
heatmaps).unsqueeze_(0)
heatmaps = heatmaps.cuda()
depth_pred = self.model_depth(
torch.cat((img_cropped, heatmaps), 1)).data.cpu().view(68, 1)
pts_img = torch.cat(
(pts_img, depth_pred * (1.0 / (256.0 / (200.0 * scale)))), 1).detach().cpu().numpy()
ldmks.append(pts_img)
return np.array(ldmks)
def get_preds_fromhm(hm, center=None, scale=None):
"""Obtain (x,y) coordinates given a set of N heatmaps. If the center
and the scale is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
center {torch.tensor} -- the center of the bounding box (default: {None})
scale {float} -- face scale (default: {None})
"""
B, C, H, W = hm.shape
hm_reshape = hm.reshape(B, C, H * W)
idx = np.argmax(hm_reshape, axis=-1)
scores = np.take_along_axis(hm_reshape, np.expand_dims(idx, axis=-1), axis=-1).squeeze(-1)
preds, preds_orig = _get_preds_fromhm(hm, idx, center, scale)
return preds, preds_orig, scores
def _get_preds_fromhm(hm, idx, center=None, scale=None):
"""Obtain (x,y) coordinates given a set of N heatmaps and the
coresponding locations of the maximums. If the center
and the scale is provided the function will return the points also in
the original coordinate frame.
Arguments:
hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H]
Keyword Arguments:
center {torch.tensor} -- the center of the bounding box (default: {None})
scale {float} -- face scale (default: {None})
"""
B, C, H, W = hm.shape
idx += 1
preds = idx.repeat(2).reshape(B, C, 2).astype(np.float32)
preds[:, :, 0] = (preds[:, :, 0] - 1) % W + 1
preds[:, :, 1] = np.floor((preds[:, :, 1] - 1) / H) + 1
for i in range(B):
for j in range(C):
hm_ = hm[i, j, :]
pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1
if pX > 0 and pX < 63 and pY > 0 and pY < 63:
diff = np.array(
[hm_[pY, pX + 1] - hm_[pY, pX - 1],
hm_[pY + 1, pX] - hm_[pY - 1, pX]])
preds[i, j] += np.sign(diff) * 0.25
preds -= 0.5
preds_orig = np.zeros_like(preds)
if center is not None and scale is not None:
for i in range(B):
for j in range(C):
preds_orig[i, j] = transform_np(
preds[i, j], center, scale, H, True)
return preds, preds_orig
def draw_gaussian(image, point, sigma):
global gauss_kernel
# Check if the gaussian is inside
ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)]
br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)]
if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1):
return image
size = 6 * sigma + 1
if gauss_kernel is None:
g = _gaussian(size)
gauss_kernel = g
else:
g = gauss_kernel
g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))]
g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))]
img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))]
img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))]
assert (g_x[0] > 0 and g_y[1] > 0)
image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]
] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]]
image[image > 1] = 1
return image
def crop(image, center, scale, resolution=256.0):
"""Center crops an image or set of heatmaps
Arguments:
image {numpy.array} -- an rgb image
center {numpy.array} -- the center of the object, usually the same as of the bounding box
scale {float} -- scale of the face
Keyword Arguments:
resolution {float} -- the size of the output cropped image (default: {256.0})
Returns:
[type] -- [description]
""" # Crop around the center point
""" Crops the image around the center. Input is expected to be an np.ndarray """
ul = transform([1, 1], center, scale, resolution, True)
br = transform([resolution, resolution], center, scale, resolution, True)
# pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0)
if image.ndim > 2:
newDim = np.array([br[1] - ul[1], br[0] - ul[0],
image.shape[2]], dtype=np.int32)
newImg = np.zeros(newDim, dtype=np.uint8)
else:
newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int)
newImg = np.zeros(newDim, dtype=np.uint8)
ht = image.shape[0]
wd = image.shape[1]
newX = np.array(
[max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32)
newY = np.array(
[max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32)
oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32)
oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32)
newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1]
] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :]
newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)),
interpolation=cv2.INTER_LINEAR)
return newImg
def transform(point, center, scale, resolution, invert=False):
"""Generate and affine transformation matrix.
Given a set of points, a center, a scale and a targer resolution, the
function generates and affine transformation matrix. If invert is ``True``
it will produce the inverse transformation.
Arguments:
point {torch.tensor} -- the input 2D point
center {torch.tensor or numpy.array} -- the center around which to perform the transformations
scale {float} -- the scale of the face/object
resolution {float} -- the output resolution
Keyword Arguments:
invert {bool} -- define wherever the function should produce the direct or the
inverse transformation matrix (default: {False})
"""
_pt = torch.ones(3)
_pt[0] = point[0]
_pt[1] = point[1]
h = 200.0 * scale
t = torch.eye(3)
t[0, 0] = resolution / h
t[1, 1] = resolution / h
t[0, 2] = resolution * (-center[0] / h + 0.5)
t[1, 2] = resolution * (-center[1] / h + 0.5)
if invert:
t = torch.inverse(t)
new_point = (torch.matmul(t, _pt))[0:2]
return new_point.int()
def transform_np(point, center, scale, resolution, invert=False):
"""Generate and affine transformation matrix.
Given a set of points, a center, a scale and a targer resolution, the
function generates and affine transformation matrix. If invert is ``True``
it will produce the inverse transformation.
Arguments:
point {numpy.array} -- the input 2D point
center {numpy.array} -- the center around which to perform the transformations
scale {float} -- the scale of the face/object
resolution {float} -- the output resolution
Keyword Arguments:
invert {bool} -- define wherever the function should produce the direct or the
inverse transformation matrix (default: {False})
"""
_pt = np.ones(3)
_pt[0] = point[0]
_pt[1] = point[1]
h = 200.0 * scale
t = np.eye(3)
t[0, 0] = resolution / h
t[1, 1] = resolution / h
t[0, 2] = resolution * (-center[0] / h + 0.5)
t[1, 2] = resolution * (-center[1] / h + 0.5)
if invert:
t = np.ascontiguousarray(np.linalg.pinv(t))
new_point = np.dot(t, _pt)[0:2]
return new_point.astype(np.int32)
def _gaussian(
size=3, sigma=0.25, amplitude=1, normalize=False, width=None,
height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5,
mean_vert=0.5):
# handle some defaults
if width is None:
width = size
if height is None:
height = size
if sigma_horz is None:
sigma_horz = sigma
if sigma_vert is None:
sigma_vert = sigma
center_x = mean_horz * width + 0.5
center_y = mean_vert * height + 0.5
gauss = np.empty((height, width), dtype=np.float32)
# generate kernel
for i in range(height):
for j in range(width):
gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / (
sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0))
if normalize:
gauss = gauss / np.sum(gauss)
return gauss |