def compute_features(self, register=False):
img, src_point, self.roi = self.LandmarkDetector.detect_landmarks(
0.4, return_img=True)
self.max_score = 0
self.id = -1
if src_point:
# align face to standard position
a = img.pix_to_ai()
T = image.get_affine_transform(src_point, self.dst_point)
a = image.warp_affine_ai(img, self.img_face, T)
a = self.img_face.ai_to_pix()
if register:
reg_img = image.Image('logo.jpg')
a = reg_img.draw_image(
self.img_face,
(lcd.width() // 2 - 68, lcd.height() // 2 - 20))
a = reg_img.draw_string(30,
lcd.height() // 2 - 48,
"Registring face",
color=(0, 255, 0),
scale=2,
mono_space=False)
a = lcd.display(reg_img)
del (reg_img)
time.sleep(2)
a = self.img_face.pix_to_ai()
# calculate face feature vector
fmap = kpu.forward(self.fe_task, self.img_face)
#print(fmap[:])
vector = list(map(lambda x: x / 256, fmap[:]))
self.feature = kpu.face_encode(vector)
for id in self.db.keys():
entry = _unhexlify(self.db[id]['vector'])
score = kpu.face_compare(entry, self.feature)
if score > self.max_score:
self.max_score = score
name = self.db[id]['name']
self.id = id
if not self.max_score > self.threshold:
name = 'Unknown'
a = img.draw_rectangle(self.roi,
color=(0x1c, 0xa2, 0xff),
thickness=2)
a = img.draw_string(self.roi[0],
self.roi[1] - 14,
("%s %%: %.2f" % (name, self.max_score)),
color=(255, 255, 255),
scale=1.5,
mono_space=False)
a = lcd.display(img)
del (img)
# align face to standard position
src_point = [le, re, nose, lm, rm] # 图片中 5 坐标的位置
T = image.get_affine_transform(
src_point, dst_point) # 根据获得的5点坐标与标准正脸坐标获取仿射变换矩阵
a = image.warp_affine_ai(img, img_face,
T) #对原始图片人脸图片进行仿射变换，变换为正脸图像
a = img_face.ai_to_pix() # 将正脸图像转为kpu格式
#a = img.draw_image(img_face, (128,0))
del (face_cut_128) # 释放裁剪人脸部分图片
# calculate face feature vector
fmap = kpu.forward(task_fe, img_face) # 计算正脸图片的196维特征值
feature = kpu.face_encode(fmap[:]) #获取计算结果
reg_flag = False
scores = [] # 存储特征比对分数
for j in range(len(record_ftrs)): #迭代已存特征值
score = kpu.face_compare(record_ftrs[j],
feature) #计算当前人脸特征值与已存特征值的分数
scores.append(score) #添加分数总表
max_score = 0
index = 0
for k in range(len(scores)): #迭代所有比对分数，找到最大分数和索引值
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > 80: # 如果最大分数大于85， 可以被认定为同一个人
a = img.draw_string(i.x(),
i.y(),
("%s:"
"\n"
"%2.1f" % (names[index], max_score)),
color=(0, 255, 0),
scale=2) # 显示人名 与 分数
a = img.draw_circle(lm[0], lm[1], 4)
a = img.draw_circle(rm[0], rm[1], 4)
# align face to standard position
src_point = [le, re, nose, lm, rm]
T = image.get_affine_transform(src_point, dst_point)
a = image.warp_affine_ai(img, img_face, T)
a = img_face.ai_to_pix()
# a = img.draw_image(img_face, (128,0))
del (face_cut_128)
# calculate face feature vector
fmap = kpu.forward(task_fe, img_face)
feature = kpu.face_encode(fmap[:])
reg_flag = False
scores = []
for j in range(len(record_ftrs)):
score = kpu.face_compare(record_ftrs[j], feature)
scores.append(score)
max_score = 0
index = 0
for k in range(len(scores)):
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > ACCURACY:
a = img.draw_string(i.x(),
i.y(),
("%s :%2.1f" % (names[index], max_score)),
color=(0, 255, 0),
scale=2)
else:
a = img.draw_string(i.x(),
def run(self,
on_detect,
on_img,
on_clear,
on_people=None,
always_show_img=False):
img = sensor.snapshot()
try:
code = kpu.run_yolo2(self._m_fd, img)
except Exception:
return
if code:
for i in code:
face_cut = img.cut(i.x(), i.y(), i.w(), i.h())
face_cut_128 = face_cut.resize(128, 128)
a = face_cut_128.pix_to_ai()
#a = img.draw_image(face_cut_128, (0,0))
# Landmark for face 5 points
try:
fmap = kpu.forward(self._m_ld, face_cut_128)
except Exception:
continue
plist = fmap[:]
le = (i.x() + int(plist[0] * i.w() - 10),
i.y() + int(plist[1] * i.h()))
re = (i.x() + int(plist[2] * i.w()),
i.y() + int(plist[3] * i.h()))
nose = (i.x() + int(plist[4] * i.w()),
i.y() + int(plist[5] * i.h()))
lm = (i.x() + int(plist[6] * i.w()),
i.y() + int(plist[7] * i.h()))
rm = (i.x() + int(plist[8] * i.w()),
i.y() + int(plist[9] * i.h()))
a = img.draw_circle(le[0], le[1], 4)
a = img.draw_circle(re[0], re[1], 4)
a = img.draw_circle(nose[0], nose[1], 4)
a = img.draw_circle(lm[0], lm[1], 4)
a = img.draw_circle(rm[0], rm[1], 4)
# align face to standard position
src_point = [le, re, nose, lm, rm]
T = image.get_affine_transform(src_point, self._dst_point)
a = image.warp_affine_ai(img, self.img_face, T)
a = self.img_face.ai_to_pix()
#a = img.draw_image(img_face, (128,0))
del (face_cut_128)
# calculate face feature vector
try:
fmap = kpu.forward(self._m_fe, self.img_face)
except Exception:
continue
feature = kpu.face_encode(fmap[:])
scores = []
for j in range(len(self.features)):
score = kpu.face_compare(self.features[j], feature)
scores.append(score)
max_score = 0
index = 0
for k in range(len(scores)):
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > 85:
a = img.draw_rectangle(i.rect(), color=(0, 255, 0))
a = img.draw_string(
i.x(),
i.y(), ("%s :%2.1f" % (self.names[index], max_score)),
color=(0, 255, 0),
scale=2)
on_detect(self.names[index], feature, max_score, img)
else:
a = img.draw_rectangle(i.rect(), color=(255, 0, 0))
# a = img.draw_string(i.x(),i.y(), ("X :%2.1f" % (max_score)), color=(255,0,0),scale=2)
on_img(img)
if on_people:
on_people(feature, img)
self._show_img_t = time.ticks_ms() / 1000.0
else:
if always_show_img:
on_img(img)
else:
if time.ticks_ms(
) - self._show_img_t * 1000 < self.show_img_timeout * 1000:
on_img(img)
else:
on_clear()
def work(img):
img.pix_to_ai()
code = kpu.run_yolo2(FaceReco.task_fd, img)
if code:
#for i in code:
start = 0
for pos in range(len(code)):
i = code[pos]
#print(i)
# Cut face and resize to 128x128
a = img.draw_rectangle(i.x(), i.y(), i.w(), i.h())
face_cut = img.cut(i.x(), i.y(), i.w(), i.h())
face_cut_128 = face_cut.resize(128, 128)
a = face_cut_128.pix_to_ai()
#a = ui.canvas.draw_image(face_cut, (320,0))
#print(i)
# Landmark for face 5 points
fmap = kpu.forward(FaceReco.task_ld, face_cut_128)
plist = fmap[:]
le = (int(i.x() + plist[0]*i.w() - 5),
int(i.y() + plist[1]*i.h()))
re = (int(i.x() + plist[2]*i.w()), int(i.y() + plist[3]*i.h()))
nose = (int(i.x() + plist[4]*i.w()),
int(i.y() + plist[5]*i.h()))
lm = (int(i.x() + plist[6]*i.w()), int(i.y() + plist[7]*i.h()))
rm = (int(i.x() + plist[8]*i.w()), int(i.y() + plist[9]*i.h()))
#print(le, re, nose, lm, rm)
a = img.draw_circle(int(le[0]), int(le[1]), 2)
a = img.draw_circle(int(re[0]), int(re[1]), 2)
a = img.draw_circle(int(nose[0]), int(nose[1]), 2)
a = img.draw_circle(int(lm[0]), int(lm[1]), 2)
a = img.draw_circle(int(rm[0]), int(rm[1]), 2)
# align face to standard position
src_point = [le, re, nose, lm, rm]
T = image.get_affine_transform(src_point, FaceReco.dst_point)
a = image.warp_affine_ai(img, FaceReco.img_face, T)
a = FaceReco.img_face.ai_to_pix()
#a = ui.canvas.draw_image(FaceReco.img_face, (320,128))
if ui.height > 240:
gc.collect()
tmp = face_cut_128.resize(80, 80)
#a = ui.canvas.draw_image(face_cut_128, (start, 240))
ui.canvas.draw_image(
tmp, 320 + int((pos % 2)*80), int((pos // 2)*80))
del(tmp)
#start = start + 80
del(face_cut_128)
# calculate face feature vector
fmap = kpu.forward(FaceReco.task_fe, FaceReco.img_face)
feature = kpu.face_encode(fmap[:])
reg_flag = False
scores = []
for j in range(len(FaceReco.record_ftrs)):
score = kpu.face_compare(FaceReco.record_ftrs[j], feature)
scores.append(score)
max_score = 0
index = 0
for k in range(len(scores)):
if max_score < scores[k]:
max_score = scores[k]
index = k
if max_score > 85:
a = img.draw_string(i.x(), i.y(), ("%s:%2.1f" % (
FaceReco.names[index], max_score)), color=(0, 255, 0), scale=2)
if ui.height > 240:
a = ui.canvas.draw_string(100 * (pos % 3), 240 + 30 * (pos // 3), ("%s :%2.1f" % (
FaceReco.names[index], max_score)), color=(0, 255, 0), scale=2)
else:
a = img.draw_string(i.x(), i.y(), ("X:%2.1f" % (
max_score)), color=(255, 0, 0), scale=2)
if ui.height > 240:
a = ui.canvas.draw_string(100 * (pos % 3), 240 + 30 * (pos // 3), ("X :%2.1f" % (
max_score)), color=(255, 0, 0), scale=2)
if FaceReco.start_processing:
FaceReco.record_ftr = feature
if len(FaceReco.record_ftrs) == len(FaceReco.names):
FaceReco.record_ftrs = []
FaceReco.record_ftrs.append(FaceReco.record_ftr)
FaceReco.start_processing = False