retinaface / utils.py
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import numpy as np
import cv2
from itertools import product as product
from math import ceil
import torch
import torch.nn.functional as F
class PriorBox(object):
def __init__(self, cfg, image_size=None, phase="train"):
super(PriorBox, self).__init__()
self.min_sizes = cfg["min_sizes"]
self.steps = cfg["steps"]
self.clip = cfg["clip"]
self.image_size = image_size
self.feature_maps = [
[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)]
for step in self.steps
]
def forward(self):
anchors = []
for k, f in enumerate(self.feature_maps):
min_sizes = self.min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes:
s_kx = min_size / self.image_size[1]
s_ky = min_size / self.image_size[0]
dense_cx = [
x * self.steps[k] / self.image_size[1] for x in [j + 0.5]
]
dense_cy = [
y * self.steps[k] / self.image_size[0] for y in [i + 0.5]
]
for cy, cx in product(dense_cy, dense_cx):
anchors += [cx, cy, s_kx, s_ky]
# back to torch land
output = torch.Tensor(anchors).view(-1, 4)
if self.clip:
output.clamp_(max=1, min=0)
return output
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline.
Args:
dets: detections before nms
thresh: nms threshold
Return:
keep: index after nms
"""
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def decode(loc, priors, variances):
"""Decode locations from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
loc (tensor): location predictions for loc layers,
Shape: [num_priors,4]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded bounding box predictions
"""
boxes = torch.cat(
(
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1]),
),
1,
)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
def decode_landm(pre, priors, variances):
"""Decode landm from predictions using priors to undo
the encoding we did for offset regression at train time.
Args:
pre (tensor): landm predictions for loc layers,
Shape: [num_priors,10]
priors (tensor): Prior boxes in center-offset form.
Shape: [num_priors,4].
variances: (list[float]) Variances of priorboxes
Return:
decoded landm predictions
"""
landms = torch.cat(
(
priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
),
dim=1,
)
return landms
def pad_image(image, h, w, size, padvalue):
pad_image = image.copy()
pad_h = max(size[0] - h, 0)
pad_w = max(size[1] - w, 0)
if pad_h > 0 or pad_w > 0:
pad_image = cv2.copyMakeBorder(image, 0, pad_h, 0,
pad_w, cv2.BORDER_CONSTANT,
value=padvalue)
return pad_image
def resize_image(image, re_size, keep_ratio=True):
"""Resize image
Args:
image: origin image
re_size: resize scale
keep_ratio: keep aspect ratio. Default is set to true.
Returns:
re_image: resized image
resize_ratio: resize ratio
"""
if not keep_ratio:
re_image = cv2.resize(image, (re_size[0], re_size[1])).astype('float32')
return re_image, 0, 0
ratio = re_size[0] * 1.0 / re_size[1]
h, w = image.shape[0:2]
if h * 1.0 / w <= ratio:
resize_ratio = re_size[1] * 1.0 / w
re_h, re_w = int(h * resize_ratio), re_size[1]
else:
resize_ratio = re_size[0] * 1.0 / h
re_h, re_w = re_size[0], int(w * resize_ratio)
re_image = cv2.resize(image, (re_w, re_h)).astype('float32')
re_image = pad_image(re_image, re_h, re_w, re_size, (0.0, 0.0, 0.0))
return re_image, resize_ratio
def preprocess(img_raw, input_size, device):
"""preprocess
Args:
img_raw: origin image
Returns:
img: resized image
scale: resized image scale
resize: resize ratio
"""
img = np.float32(img_raw)
# resize image
img, resize = resize_image(img, input_size)
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.numpy()
scale = scale.to(device)
return img, scale, resize
def postprocess(cfg, img, outputs, scale, resize, confidence_threshold, nms_threshold, device):
"""post_process
Args:
img: resized image
outputs: forward outputs
scale: resized image scale
resize: resize ratio
confidence_threshold: confidence threshold
nms_threshold: non-maximum suppression threshold
Returns:
detetcion results
"""
_, im_height, im_width, _= img.shape
loc = torch.from_numpy(outputs[0])
conf = torch.from_numpy(outputs[1])
landms = torch.from_numpy(outputs[2])
# softmax
conf = F.softmax(conf, dim=-1)
priorbox = PriorBox(cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(device)
prior_data = priors.data
boxes = decode(loc.squeeze(0), prior_data, cfg["variance"])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.squeeze(0), prior_data, cfg["variance"])
scale1 = torch.Tensor(
[img.shape[2], img.shape[1], img.shape[2], img.shape[1], img.shape[2],
img.shape[1], img.shape[2], img.shape[1], img.shape[2], img.shape[1],]
)
scale1 = scale1.to(device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, nms_threshold)
dets = dets[keep, :]
landms = landms[keep]
dets = np.concatenate((dets, landms), axis=1)
return dets