如何在我的代码的for循环期间释放内存？

Question

我使用cv2和plt使用嵌套循环和函数来生成图像。在程序运行期间，直到计算机崩溃之前，内存使用量一直在增加。

我在循环和函数结束时尝试了del和gc.collection()，但是没有用。我想知道问题出在哪里？

def seg(mat_data):
    data = np.array(mat_data)
    signals = []
    peaks =  biosppy.signals.ecg.christov_segmenter(signal=data, sampling_rate = 500)[0]
    for i in range(2,len(peaks)-1):

        left_diff = abs(peaks[i-1]-peaks[i])//2
        right_diff = abs(peaks[i+1]-peaks[i])//2

        x = peaks[i]-left_diff
        y = peaks[i]+right_diff
        signal = data[x:y]
        signals.append(signal)
    return signals,peaks

def sig2img(signals,file_name,label,channel):
    if label == 'null':
        for i, signal in enumerate(signals):

            fig = plt.figure(frameon=False)
            plt.plot(signal, linewidth=3.5)
            plt.xticks([]), plt.yticks([])
            for spine in plt.gca().spines.values():
                spine.set_visible(False)

            filename = 'test_img' + '/' +file_name+'_'+str(label)+'_'+str(channel)+'_'+str(i)+'.png'
            fig.savefig(filename)
            plt.close(fig)
            im_gray = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
            im_gray = cv2.resize(im_gray, (128, 128), interpolation = cv2.INTER_LANCZOS4)
            cv2.imwrite(filename, im_gray) 
        return 
    for i, signal in enumerate(signals):

        fig = plt.figure(frameon=False)
        plt.plot(signal, linewidth=3.5)
        plt.xticks([]), plt.yticks([])
        for spine in plt.gca().spines.values():
            spine.set_visible(False)

        filename = 'train_img' + '/' +file_name+'_'+str(label)+'_'+str(channel)+'_'+str(i)+'.png'
        fig.savefig(filename)
        plt.close(fig)
        im_gray = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
        im_gray = cv2.resize(im_gray, (128, 128), interpolation = cv2.INTER_LANCZOS4)
        cv2.imwrite(filename, im_gray) 


def generate_images(train = True,cut=5000):
    if train == False:
        data_path = 'data/test'
        test_name = os.listdir(data_path)
        file_raw = [(scio.loadmat(data_path+'/'+m),) for m in test_name]
        data = [ normalize(d[0]['data']) for d in file_raw]
        for i in range(len(test_name)):
            print(test_name[i])
            print(data[i].shape)
            label = 'null'
            for j in range(data[i].shape[0]):
                print(data[i][j,:cut].shape)
                signals,peaks = seg(data[i][j,:cut])
                channel = j+1
                sig2img(signals,test_name[i],label,channel)

    data_path = 'data/train'
    reference_path = 'data/reference.txt'     
    mat_label = pd.read_csv(reference_path,sep='\t',header = None)
    mat = mat_label[0]
    file_raw = [(scio.loadmat(data_path+'/'+m),) for m in mat]
    train_name = [m for m in mat]
    label =  mat_label[1]
    data = [ normalize(d[0]['data']) for d in file_raw]  #data shape: 12x5000
    for i in range(len(label)):
        print(train_name[i])
        print(data[i].shape)
        print(label[i])
        for j in range(data[i].shape[0]):
            print(data[i][j,:cut].shape)
            signals,peaks = seg(data[i][j,:cut])     #peaks is a list with length of 6~10
            channel = j+1                      #signals is numpy array with shape of (500,)
            sig2img(signals,train_name[i],label[i],channel)

我想知道应该在循环运行期间在何处添加del或gc，或通过其他方式释放痛苦。