python - OpenCV Python中的内存不足错误-(-215)u!= 0在函数cv::Mat::create中

Question

我正在使用OpenCV + Python进行与人类情绪检测相关的研究项目。我遵循使用CK +数据集进行训练的教程。但是，当我尝试运行代码来训练数据集时，它给出了OutOfMemory错误。我该如何解决这个问题。请帮我。我是OpenCV和Python的初学者。我把错误代码和源代码放在下面。

OpenCV Error: Insufficient memory (Failed to allocate 495880000 bytes) in cv::OutOfMemoryError, file C:\projects\opencv-python\opencv\modules\core\src\alloc.cpp, line 55
OpenCV Error: Assertion failed (u != 0) in cv::Mat::create, file C:\projects\opencv-python\opencv\modules\core\src\matrix.cpp, line 436
Traceback (most recent call last):
  File "D:/Documents/Private/Pycharm/EmotionDetection/training.py", line 71, in <module>
    correct = run_recognizer()
  File "D:/Documents/Private/Pycharm/EmotionDetection/training.py", line 49, in run_recognizer
    fishface.train(training_data, np.asarray(training_labels))
cv2.error: C:\projects\opencv-python\opencv\modules\core\src\matrix.cpp:436: error: (-215) u != 0 in function cv::Mat::create

这是源代码。

   import cv2
    import glob
    import random
    import numpy as np

    emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]  # Emotion list
    fishface = cv2.face.EigenFaceRecognizer_create()  # Initialize fisher face classifier

    data = {}


    def get_files(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
        files = glob.glob("dataset\\%s\\*" % emotion)
        random.shuffle(files)
        training = files[:int(len(files) * 0.8)]  # get first 80% of file list
        prediction = files[-int(len(files) * 0.2):]  # get last 20% of file list
        return training, prediction


    def make_sets():
        training_data = []
        training_labels = []
        prediction_data = []
        prediction_labels = []
        for emotion in emotions:
            training, prediction = get_files(emotion)
            # Append data to training and prediction list, and generate labels 0-7
            for item in training:
                image = cv2.imread(item)  # open image
                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
                training_data.append(gray)  # append image array to training data list
                training_labels.append(emotions.index(emotion))

            for item in prediction:  # repeat above process for prediction set
                image = cv2.imread(item)
                gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                prediction_data.append(gray)
                prediction_labels.append(emotions.index(emotion))

        return training_data, training_labels, prediction_data, prediction_labels


    def run_recognizer():
        training_data, training_labels, prediction_data, prediction_labels = make_sets()

        print("training fisher face classifier")
        print("size of training set is:", len(training_labels), "images")

        fishface.train(training_data, np.asarray(training_labels))

        print("predicting classification set")

        cnt = 0
        correct = 0
        incorrect = 0
        for image in prediction_data:
            pred, conf = fishface.predict(image)
            if pred == prediction_labels[cnt]:
                correct += 1
                cnt += 1
            else:
                cv2.imwrite("difficult\\%s_%s_%s.jpg" % (emotions[prediction_labels[cnt]], emotions[pred], cnt), image)  # <-- this one is new
                incorrect += 1
                cnt += 1
        return (100 * correct) / (correct + incorrect)


    # Now run it
    meta_score = []
    for i in range(0, 10):
        correct = run_recognizer()
        print("got", correct, "percent correct!")
        meta_score.append(correct)

    print("\n\nend score:", np.mean(meta_score), "percent correct!")

Answer 1

如果您使用的是32位系统，则这是不可能的，因为没有足够的内存可以寻址。您的镜像对于构建系统而言太大。如果您具有32位操作系统，请升级到64位，否则您可能正在使用32位构建环境，应该切换到64位构建工具。