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retinaface.py
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retinaface.py
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import numpy as np
import cv2
from math import ceil
from itertools import product as product
import face_align
from rknnlite.api import RKNNLite
def letterbox_resize(image, size, bg_color):
"""
letterbox_resize the image according to the specified size
:param image: input image, which can be a NumPy array or file path
:param size: target size (width, height)
:param bg_color: background filling data
:return: processed image
"""
if isinstance(image, str):
image = cv2.imread(image)
target_width, target_height = size
image_height, image_width, _ = image.shape
# Calculate the adjusted image size
aspect_ratio = min(target_width / image_width, target_height / image_height)
new_width = int(image_width * aspect_ratio)
new_height = int(image_height * aspect_ratio)
# Use cv2.resize() for proportional scaling
image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
# Create a new canvas and fill it
result_image = np.ones((target_height, target_width, 3), dtype=np.uint8) * bg_color
offset_x = (target_width - new_width) // 2
offset_y = (target_height - new_height) // 2
result_image[offset_y:offset_y + new_height, offset_x:offset_x + new_width] = image
return result_image, aspect_ratio, offset_x, offset_y
def PriorBox(image_size): #image_size Support (320,320) and (640,640)
anchors = []
min_sizes = [[32, 64], [64, 128], [256, 512]]
steps = [8, 16, 32]
feature_maps = [[ceil(image_size[0] / step), ceil(image_size[1] / step)] for step in steps]
for k, f in enumerate(feature_maps):
min_sizes_ = min_sizes[k]
for i, j in product(range(f[0]), range(f[1])):
for min_size in min_sizes_:
s_kx = min_size / image_size[1]
s_ky = min_size / image_size[0]
dense_cx = [x * steps[k] / image_size[1] for x in [j + 0.5]]
dense_cy = [y * steps[k] / 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]
output = np.array(anchors).reshape(-1, 4)
return output
def box_decode(loc, priors):
"""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
"""
variances = [0.1, 0.2]
boxes = np.concatenate((
priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
priors[:, 2:] * np.exp(loc[:, 2:] * variances[1])), axis=1)
boxes[:, :2] -= boxes[:, 2:] / 2
boxes[:, 2:] += boxes[:, :2]
return boxes
def decode_landm(pre, priors):
"""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
"""
variances = [0.1, 0.2]
landmarks = np.concatenate((
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:]
), axis=1)
return landmarks
def nms(dets, thresh):
"""Pure Python NMS baseline."""
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 get_faces(img):
rknn = RKNNLite(verbose=False)
rknn.load_rknn('retina.rknn')
rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0_1_2)
img_height, img_width, _ = img.shape
model_height, model_width = (320, 320)
letterbox_img, aspect_ratio, offset_x, offset_y = letterbox_resize(img, (model_height,model_width), 114) # letterbox缩放
infer_img = letterbox_img[..., ::-1] # BGR2RGB
ret = []
indata=np.expand_dims(infer_img,axis=0)
outputs = rknn.inference(inputs=[indata])
loc, conf, landmarks = outputs
priors = PriorBox(image_size=(model_height, model_width))
boxes = box_decode(loc.squeeze(0), priors)
scale = np.array([model_width, model_height,
model_width, model_height])
boxes = boxes * scale // 1 # face box
boxes[...,0::2] =np.clip((boxes[...,0::2] - offset_x) / aspect_ratio, 0, img_width) #letterbox
boxes[...,1::2] =np.clip((boxes[...,1::2] - offset_y) / aspect_ratio, 0, img_height) #letterbox
scores = conf.squeeze(0)[:, 1] # face score
landmarks = decode_landm(landmarks.squeeze(
0), priors) # face keypoint data
scale_landmarks = np.array([model_width, model_height, model_width, model_height,
model_width, model_height, model_width, model_height,
model_width, model_height])
landmarks = landmarks * scale_landmarks // 1
landmarks[...,0::2] = np.clip((landmarks[...,0::2] - offset_x) / aspect_ratio, 0, img_width) #letterbox
landmarks[...,1::2] = np.clip((landmarks[...,1::2] - offset_y) / aspect_ratio, 0, img_height) #letterbox
# ignore low scores
inds = np.where(scores > 0.02)[0]
boxes = boxes[inds]
landmarks = landmarks[inds]
scores = scores[inds]
order = scores.argsort()[::-1]
boxes = boxes[order]
landmarks = landmarks[order]
scores = scores[order]
# NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(
np.float32, copy=False)
keep = nms(dets, 0.5)
dets = dets[keep, :]
landmarks = landmarks[keep]
dets = np.concatenate((dets, landmarks), axis=1)
for data in dets:
if data[4] < 0.2:
continue
x1 = int(data[0])
y1 = int(data[1])
x2 = int(data[2])
y2 = int(data[3])
x3 = int(data[5])
y3 = int(data[6])
x4 = int(data[7])
y4 = int(data[8])
leftEyeCenter = np.array([x3, y3])
rightEyeCenter = np.array([x4, y4])
face_aligned, M = face_align.align(img, rightEyeCenter, leftEyeCenter)
faces = {'face' : face_aligned, 'left' : leftEyeCenter, 'right' : rightEyeCenter, 'M' : M}
ret.append(faces)
# Release
rknn.release()
return ret