def predict(img):
"""
加载模型和模型预测
:param img: cv2.imread 图像
:return: 预测的图片中的总人数、其中佩戴口罩的人数
"""
# -------------------------- 实现模型预测部分的代码 ---------------------------
# 将 cv2.imread 图像转化为 PIL.Image 图像，用来兼容测试输入的 cv2 读取的图像（勿删！！！）
# cv2.imread 读取图像的类型是 numpy.ndarray
# PIL.Image.open 读取图像的类型是 PIL.JpegImagePlugin.JpegImageFile
if isinstance(img, np.ndarray):
# 转化为 PIL.JpegImagePlugin.JpegImageFile 类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
recognize = Recognition(model_path)
img, all_num, mask_num = recognize.mask_recognize(img)
# -------------------------------------------------------------------------
return all_num, mask_num
plot_image(img)
for index, (x, labels) in enumerate(train_data_loader):
print(index, "\nfeature:", x[0], "\nlabels:",
labels) # 打印每个batch中第一张图片的feature，和所有label
show_tensor_img(x)
break # 训练集一共有20个batch
pnet_path = "./torch_py/MTCNN/weights/pnet.npy"
rnet_path = "./torch_py/MTCNN/weights/rnet.npy"
onet_path = "./torch_py/MTCNN/weights/onet.npy"
torch.set_num_threads(1)
# 读取测试图片
img = Image.open("test.jpg")
# 加载模型进行识别口罩并绘制方框
recognize = Recognition()
draw = recognize.face_recognize(img)
plot_image(draw)
# 加载 MobileNet 的预训练模型权
device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device(
"cpu") # 选择运算设备
train_data_loader, valid_data_loader = processing_data(data_path=data_path,
height=160,
width=160,
batch_size=32)
modify_x, modify_y = torch.ones((32, 3, 160, 160)), torch.ones((32))
epochs = 1000
model = MobileNetV1(classes=2).to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3) # 优化器：lr即学习率0.001
print('加载完成...')
optimizer.step() # 梯度更新
if loss < best_loss: # 选择最小损失
best_model_weights = copy.deepcopy(model.state_dict())
best_loss = loss
#loss_list.append(loss) # 存储损失函数
print('{{"metric": "loss", "value": {}}}'.format(loss))
# 迭代一轮计算accuracy
# 模型路径
model_path = './results/temp.pth'
# 临时保存net
torch.save(model.state_dict(), model_path)
# 加载网络
temp_net = Recognition(model_path)
correct = 0
total = 0
best_val_loss = 1e9
with torch.no_grad():# 执行的固定操作
for index, (x, labels) in enumerate(valid_data_loader):
print("index:",index)
temp_correct = 0
size = labels.size(0)
print("size:",size)
total += size
# 可视化valid loss
x = x.to(device)
y = labels.to(device)
pred_y = model(x)
resolution_op = 256
first_dir = os.listdir(image_folder)
num = 0
for img in first_dir:
# 二级目录绝对路径
if img.split(".")[-1] == 'jpg' or img.split(".")[-1] == 'JPG':
path_image = image_folder + '/' + str(img)
#if int((img.split(".")[0]).split("_")[-2]) == 1:
image = Image.open(path_image)
#image = cv2.imread(path_image)
num = num + 1
print(num, str(img))
recognize = Recognition()
#draw = recognize.face_recognize(img)
#plot_image(draw)
if not recognize.crop_faces(image):
#face_img = image
print("Failed!!")
continue
#plot_image(image)
else:
face_img = recognize.crop_faces(image)[0]
#plot_image(face_img)
save_path = save_folder + '/' + str(img)
face_img.save(save_path)
#cv2.imwrite(save_path, face_img)
import torch
import cv2
from PIL import Image
import matplotlib.pyplot as plt
from torch_py.Utils import plot_image
from torch_py.MTCNN.detector import FaceDetector
from torch_py.MobileNetV1 import MobileNetV1
from torch_py.FaceRec import Recognition
pnet_path = "./torch_py/MTCNN/weights/pnet.npy"
rnet_path = "./torch_py/MTCNN/weights/rnet.npy"
onet_path = "./torch_py/MTCNN/weights/onet.npy"
img = Image.open("image/test.jpg")
detector = FaceDetector()
recognize = Recognition(model_path='./results/modelV1.pkl')
draw, all_num, mask_nums = recognize.mask_recognize(img)
print("总人数:", all_num, "戴口罩数", mask_nums)
plt.title("total: %d, mask count: %d" % (all_num, mask_nums))
plt.imshow(draw)
plt.show()