【发布时间】:2020-10-02 01:28:17
【问题描述】:
我目前正在尝试使用 OpenCV 4.2.2 训练数据集,我搜索了网络,但只有 2 个参数的示例。 OpenCV 4.2.2 loadDatasetList 需要 4 个参数,但存在一些缺点,我已尽力克服以下缺点。我一开始尝试使用数组,但 loadDatasetList 抱怨数组不可迭代,然后我继续执行下面的代码,但没有运气。非常感谢您的帮助,感谢您抽出宝贵的时间,并希望每个人都平安无事。
没有iter()的数组传入之前的错误
PS E:\MTCNN> python kazemi-train.py 没有给出有效的输入文件,请检查给定的文件名。 回溯(最近一次通话最后): 文件“kazemi-train.py”,第 35 行,在 状态,images_train,landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations,imageFiles,annotationFiles) TypeError: cannot unpack non-iterable bool object
当前的错误是:
PS E:\MTCNN> python kazemi-train.py 回溯(最近一次通话最后): 文件“kazemi-train.py”,第 35 行,在 状态,images_train,landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations,iter(imageFiles),iter(annotationFiles)) SystemError: 返回 NULL 而没有设置错误
import os
import time
import cv2
import numpy as np
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Training of kazemi facial landmark algorithm.')
parser.add_argument('--face_cascade', type=str, help="Path to the cascade model file for the face detector",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)),'models','haarcascade_frontalface_alt2.xml'))
parser.add_argument('--kazemi_model', type=str, help="Path to save the kazemi trained model file",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)),'models','face_landmark_model.dat'))
parser.add_argument('--kazemi_config', type=str, help="Path to the config file for training",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)),'models','config.xml'))
parser.add_argument('--training_images', type=str, help="Path of a text file contains the list of paths to all training images",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)),'train','images_train.txt'))
parser.add_argument('--training_annotations', type=str, help="Path of a text file contains the list of paths to all training annotation files",
default=os.path.join(os.path.dirname(os.path.realpath(__file__)),'train','points_train.txt'))
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
start = time.time()
facemark = cv2.face.createFacemarkKazemi()
if args.verbose:
print("Creating the facemark took {} seconds".format(time.time()-start))
start = time.time()
imageFiles = []
annotationFiles = []
for file in os.listdir("./AppendInfo"):
if file.endswith(".jpg"):
imageFiles.append(file)
if file.endswith(".txt"):
annotationFiles.append(file)
status, images_train, landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations, iter(imageFiles), iter(annotationFiles))
assert(status == True)
if args.verbose:
print("Loading the dataset took {} seconds".format(time.time()-start))
scale = np.array([460.0, 460.0])
facemark.setParams(args.face_cascade,args.kazemi_model,args.kazemi_config,scale)
for i in range(len(images_train)):
start = time.time()
img = cv2.imread(images_train[i])
if args.verbose:
print("Loading the image took {} seconds".format(time.time()-start))
start = time.time()
status, facial_points = cv2.face.loadFacePoints(landmarks_train[i])
assert(status == True)
if args.verbose:
print("Loading the facepoints took {} seconds".format(time.time()-start))
start = time.time()
facemark.addTrainingSample(img,facial_points)
assert(status == True)
if args.verbose:
print("Adding the training sample took {} seconds".format(time.time()-start))
start = time.time()
facemark.training()
if args.verbose:
print("Training took {} seconds".format(time.time()-start))
如果我只使用 2 个参数,则会引发此错误
文件“kazemi-train.py”,第 37 行,状态,images_train,landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations) TypeError: loadDatasetList() missing required argument 'images' (pos 3 )
如果我尝试使用 3 个参数,则会引发此错误
Traceback(最近一次调用最后一次): 文件“kazemi-train.py”,第 37 行,在 状态,images_train,landmarks_train = cv2.face.loadDatasetList(args.training_images,args.training_annotations,iter(imagePaths)) TypeError: loadDatasetList() 缺少必需的参数“注释”(位置 4)
关于 loadDatasetList 的文档
【问题讨论】:
-
检查形状
-
您只需要提供训练图像和注释
-
我删除了我的答案,因为它不起作用
-
rcvaram,感谢您的帮助。