def get_FPS(self, image, test_interval):
image = np.array(image, np.float32)
im_height, im_width, _ = np.shape(image)
scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]
scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]]
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = letterbox_image(image, [self.input_shape[1], self.input_shape[0]])
else:
self.anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
photo = np.expand_dims(preprocess_input(image),0)
preds = self.retinaface.predict(photo)
results = self.bbox_util.detection_out(preds, self.anchors, confidence_threshold=self.confidence)
if len(results)>0:
results = np.array(results)
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
results = retinaface_correct_boxes(results, np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
results[:,:4] = results[:,:4]*scale
results[:,5:] = results[:,5:]*scale_for_landmarks
t1 = time.time()
for _ in range(test_interval):
preds = self.retinaface.predict(photo)
results = self.bbox_util.detection_out(preds, self.anchors, confidence_threshold=self.confidence)
if len(results)>0:
results = np.array(results)
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
results = retinaface_correct_boxes(results, np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
results[:,:4] = results[:,:4]*scale
results[:,5:] = results[:,5:]*scale_for_landmarks
t2 = time.time()
tact_time = (t2 - t1) / test_interval
return tact_time
def detect_image(self, image):
self.confidence = 0.02
image = np.array(image, np.float32)
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0]]
scale_for_landmarks = [np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0], np.shape(image)[1], np.shape(image)[0],
np.shape(image)[1], np.shape(image)[0]]
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = letterbox_image(image, [self.input_shape[1], self.input_shape[0]])
else:
self.anchors = Anchors(self.cfg, image_size=(im_height, im_width)).get_anchors()
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
photo = np.expand_dims(preprocess_input(image),0)
preds = self.get_pred(photo)
preds = [pred.numpy() for pred in preds]
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.detection_out(preds, self.anchors, confidence_threshold=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results)<=0:
return np.array([])
results = np.array(results)
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
results = retinaface_correct_boxes(results, np.array([self.input_shape[0], self.input_shape[1]]), np.array([im_height, im_width]))
results[:,:4] = results[:,:4]*scale
results[:,5:] = results[:,5:]*scale_for_landmarks
return results
def detect_image(self, image):
#---------------------------------------------------#
# 对输入图像进行一个备份，后面用于绘图
#---------------------------------------------------#
old_image = image.copy()
image = np.array(image, np.float32)
im_height, im_width, _ = np.shape(image)
#---------------------------------------------------#
# 计算scale，用于将获得的预测框转换成原图的高宽
#---------------------------------------------------#
scale = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
#---------------------------------------------------------#
# letterbox_image可以给图像增加灰条，实现不失真的resize
#---------------------------------------------------------#
if self.letterbox_image:
image = letterbox_image(image,
[self.input_shape[1], self.input_shape[0]])
else:
self.anchors = Anchors(self.cfg,
image_size=(im_height,
im_width)).get_anchors()
#-----------------------------------------------------------#
# 图片预处理，归一化。
#-----------------------------------------------------------#
photo = np.expand_dims(preprocess_input(image), 0)
preds = self.get_pred(photo)
preds = [pred.numpy() for pred in preds]
#-----------------------------------------------------------#
# 将预测结果进行解码
#-----------------------------------------------------------#
results = self.bbox_util.detection_out(
preds, self.anchors, confidence_threshold=self.confidence)
#--------------------------------------#
# 如果没有检测到物体，则返回原图
#--------------------------------------#
if len(results) <= 0:
return old_image
results = np.array(results)
#---------------------------------------------------------#
# 如果使用了letterbox_image的话，要把灰条的部分去除掉。
#---------------------------------------------------------#
if self.letterbox_image:
results = retinaface_correct_boxes(
results, np.array([self.input_shape[0], self.input_shape[1]]),
np.array([im_height, im_width]))
results[:, :4] = results[:, :4] * scale
results[:, 5:] = results[:, 5:] * scale_for_landmarks
for b in results:
text = "{:.4f}".format(b[4])
b = list(map(int, b))
# b[0]-b[3]为人脸框的坐标，b[4]为得分
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
print(b[0], b[1], b[2], b[3], b[4])
# b[5]-b[14]为人脸关键点的坐标
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
return old_image
def detect_image(self, image):
old_image = image.copy()
image = np.array(image, np.float32)
im_height, im_width, _ = np.shape(image)
scale = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
scale_for_landmarks = [
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0],
np.shape(image)[1],
np.shape(image)[0]
]
if self.letterbox_image:
image = letterbox_image(image,
[self.input_shape[1], self.input_shape[0]])
else:
self.anchors = Anchors(self.cfg,
image_size=(im_height,
im_width)).get_anchors()
photo = np.expand_dims(preprocess_input(image), 0)
preds = self.retinaface.predict(photo)
results = self.bbox_util.detection_out(
preds, self.anchors, confidence_threshold=self.confidence)
if len(results) <= 0:
return old_image, []
results = np.array(results)
if self.letterbox_image:
results = retinaface_correct_boxes(
results, np.array([self.input_shape[0], self.input_shape[1]]),
np.array([im_height, im_width]))
results[:, :4] = results[:, :4] * scale
results[:, 5:] = results[:, 5:] * scale_for_landmarks
ans = []
for b in results:
confidence = b[4].astype(float)
each_ans = {'box': [0, 0, 0, 0], 'confidence': 0, 'landmarks': []}
text = "{:.4f}".format(b[4])
b = list(map(int, b))
each_ans['box'][0] = b[0]
each_ans['box'][1] = b[1]
each_ans['box'][2] = b[2]
each_ans['box'][3] = b[3]
each_ans['confidence'] = confidence
cv2.rectangle(old_image, (b[0], b[1]), (b[2], b[3]), (0, 0, 255),
2)
cx = b[0]
cy = b[1] + 12
cv2.putText(old_image, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 255))
print(b[0], b[1], b[2], b[3], b[4])
cv2.circle(old_image, (b[5], b[6]), 1, (0, 0, 255), 4)
cv2.circle(old_image, (b[7], b[8]), 1, (0, 255, 255), 4)
cv2.circle(old_image, (b[9], b[10]), 1, (255, 0, 255), 4)
cv2.circle(old_image, (b[11], b[12]), 1, (0, 255, 0), 4)
cv2.circle(old_image, (b[13], b[14]), 1, (255, 0, 0), 4)
landmarks = [
(b[5], b[6]),
(b[7], b[8]),
(b[9], b[10]),
(b[11], b[12]),
(b[13], b[14]),
]
each_ans['landmarks'] = landmarks
ans.append(each_ans)
return old_image, ans