class FaceGenderAge(object):
WRN_WEIGHTS_PATH = "https://github.com/Tony607/Keras_age_gender/releases/download/V1.0/weights.18-4.06.hdf5"
def __init__(self, depth=16, width=8, face_size=64):
self.face_size = face_size
self.model = WideResNet(face_size, depth=depth, k=width)()
model_dir = os.path.join(os.getcwd(), "models")
fpath = get_file('weights.18-4.06.hdf5',
self.WRN_WEIGHTS_PATH,
cache_subdir=model_dir)
self.model.load_weights(fpath)
self.model._make_predict_function()
def predict_agge(self, frame):
inference_frame = cv2.resize(frame, (self.face_size, self.face_size))
inference_frame = inference_frame[np.newaxis, :, :, :]
results = self.model.predict(inference_frame)
predicted_genders = results[0]
ages = np.arange(0, 101).reshape(101, 1)
predicted_ages = results[1].dot(ages).flatten()
label = "Age: {}, Gender: {}".format(
int(predicted_ages), "F" if predicted_genders[0][0] > 0.5 else "M")
return label
def detection(self):
depth = 16
width = 8
img_size = 64
model = WideResNet(img_size, depth=depth, k=width)()
model.load_weights(r'models/weights.hdf5')
detector = dlib.get_frontal_face_detector()
image_np = cv2.imdecode(np.fromfile(self.fname, dtype=np.uint8), -1)
img_h = image_np.shape[0]
img_w = image_np.shape[1]
detected = detector(image_np, 1)
gender_faces = []
labels = []
original_faces = []
photo_position = []
change_male_to_female_path = r'models/netG_A2B.pth'
change_female_to_male_path = r'models/netG_B2A.pth'
# 加载CycleGAN模型
netG_male2female = Generator(3, 3)
netG_female2male = Generator(3, 3)
netG_male2female.load_state_dict(
torch.load(change_male_to_female_path, map_location='cpu'))
netG_female2male.load_state_dict(
torch.load(change_female_to_male_path, map_location='cpu'))
# 设置模型为预测模式
netG_male2female.eval()
netG_female2male.eval()
transform_list = [
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
transform = transforms.Compose(transform_list)
"""
这段内容为图片数据处理frame
"""
if len(detected) > 0:
for i, d in enumerate(detected):
# weight和height表示原始图片的宽度和高度，因为我们需要不停地resize处理图片
# 最后要贴回到原始图片中去，w和h就用来做最后的resize
x0, y0, x1, y1, w, h = d.left(), d.top(), d.right(), d.bottom(
), d.width(), d.height()
x0 = max(int(x0 - 0.25 * w), 0)
y0 = max(int(y0 - 0.45 * h), 0)
x1 = min(int(x1 + 0.25 * w), img_w - 1)
y1 = min(int(y1 + 0.05 * h), img_h - 1)
w = x1 - x0
h = y1 - y0
if w > h:
x0 = x0 + w // 2 - h // 2
w = h
x1 = x0 + w
else:
y0 = y0 + h // 2 - w // 2
h = w
y1 = y0 + h
original_faces.append(
cv2.resize(image_np[y0:y1, x0:x1, :], (256, 256)))
gender_faces.append(
cv2.resize(image_np[y0:y1, x0:x1, :],
(img_size, img_size)))
photo_position.append([y0, y1, x0, x1, w, h])
gender_faces = np.array(gender_faces)
results = model.predict(gender_faces)
predicted_genders = results[0]
for i in range(len(original_faces)):
labels.append('F' if predicted_genders[i][0] > 0.5 else 'M')
for i, gender in enumerate(labels):
# 这几个变量用于接下来图片缩放和替换
y0, y1, x0, x1, w, h = photo_position[i]
# 将数据转换成Pytorch可以处理的格式
picture = transform(original_faces[i])
picture = Variable(picture)
input_picture = picture.unsqueeze(0)
if gender == "M":
fake_female = 0.5 * (netG_male2female(input_picture).data +
1.0)
out_img = fake_female.detach().squeeze(0)
else:
fake_male = 0.5 * (netG_female2male(input_picture).data +
1.0)
out_img = fake_male.detach().squeeze(0)
# 需要将Pytorch处理之后得到的数据，转换为OpenCV可以处理的格式
# 下面代码就是转换代码
image_numpy = out_img.float().numpy()
image_numpy = (np.transpose(image_numpy,
(1, 2, 0)) + 1) / 2.0 * 255.0
image_numpy = image_numpy.clip(0, 255)
image_numpy = image_numpy.astype(np.uint8)
# 将转换好的性别图片替换到原始图片中去
# 使用泊松融合使生成图像和背景图像浑然一体
# 使用方法：cv2.seamlessClone(src, dst, mask, center, flags)
generate_face = cv2.resize(image_numpy, (w, h))
# Create an all white mask， 感兴趣的需要替换的目标区域，精确地mask可以更好的替换，这里mask就是生成图片的大小
mask = 255 * np.ones((w, h), image_np.dtype)
# center是目标影像的中心在背景图像上的坐标！
center_y = y0 + h // 2
center_x = x0 + w // 2
center = (center_x, center_y)
output_face = cv2.seamlessClone(generate_face, image_np, mask,
center, cv2.NORMAL_CLONE)
self.out_img = output_face
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
# race_face = np.expand_dims(race_face, 0)
# race_face = np.expand_dims(race_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
rgb_face = np.expand_dims(rgb_face, 0)
age_prediction = age_gender_classifier.predict(rgb_face)
age_text = int(age_prediction[1].dot(np.arange(0, 101).reshape(101, 1)).flatten())
gender_text = gender_labels[np.argmax(age_prediction[0][0])]
# race_text = race_labels[np.argmax(race_classifier.predict(race_face))]
gender_window.append(gender_text+' '+str(age_text)) #+':'+race_text
if len(gender_window) > frame_window:
emotion_window.pop(0)
gender_window.pop(0)
try:
emotion_mode = mode(emotion_window)
gender_mode = mode(gender_window)
except:
continue