Question

我知道类似的问题已经问过很多遍了，但是我从他们那里找不到太多帮助。我正在研究验证码断路器。我已经从Kaggle开发了一个数据集，现在正在开发自己的数据集。代码运行：

from keras import layers
from keras.models import Model
from keras.models import load_model
from keras import callbacks
import os
import cv2
import string
import numpy as np

#Init main values
symbols = string.ascii_lowercase + "0123456789" # All symbols captcha can contain
num_symbols = len(symbols)
img_shape = (60, 200, 4)

数据预处理已完成：

def preprocess_data():
    n_samples = len(os.listdir(r'C:\Users\Winter\Desktop\AtlanTen\captchas\captchasML'))
    X = np.zeros((n_samples, 60, 200, 4)) #1070*50*200
    y = np.zeros((5, n_samples, num_symbols)) #5*1070*36

    for i, pic in enumerate(os.listdir(r'C:\Users...\captchas\captchasML')):
        # Read image as grayscale
        img = cv2.imread(os.path.join(r'C:\Users\...'
        pic_target = pic[:-4]
        if len(pic_target) < 6:
            # Scale and reshape image
            img = img / 255.0
            img = np.reshape(img, (60, 200, 4))
            # Define targets and code them using OneHotEncoding
            targs = np.zeros((5, num_symbols))
            for j, l in enumerate(pic_target):
                ind = symbols.find(l)
                targs[j, ind] = 1
            X[i] = img
            y[:, i] = targs
    
    # Return final data
    return X, y

X, y = preprocess_data()
X_train, y_train = X[:970], y[:, :970]
X_test, y_test = X[970:], y[:, 970:]

我没有写有关网络的信息，因为那里没有发生错误。任何随机验证码的预测代码为：

# Define function to predict captcha
def predict(filepath):
    img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
    if img is not None:
        img = img / 255.0
    else:
        print("Not detected");
    res = np.array(model.predict(img[np.newaxis, :, :, np.newaxis]))
    ans = np.reshape(res, (5, 36))
    l_ind = []
    probs = []
    for a in ans:
        l_ind.append(np.argmax(a))
        #probs.append(np.max(a))

    capt = ''
    for l in l_ind:
        capt += symbols[l]
    return capt#, sum(probs) / 5

整个代码取自Kaggle，并做了一些修改。但是当我尝试时：

print("Predicted Captcha =",predict(r'C:\Users...\captchas\captchasML\2D429.jfif'))

这表明：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-ac96e27d8d00> in <module>
----> 1 print("Predicted Captcha =",predict(r'C:\Users\Winter\Desktop\AtlanTen\captchas\captchasML\2D429.jfif'))

<ipython-input-8-ea3eaf0819e7> in predict(filepath)
      6     else:
      7         print("Not detected");
----> 8     res = np.array(model.predict(img[np.newaxis, :, :, np.newaxis]))
      9     ans = np.reshape(res, (5, 36))
     10     l_ind = []

~\anaconda3\lib\site-packages\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   1439 
   1440         # Case 2: Symbolic tensors or Numpy array-like.
-> 1441         x, _, _ = self._standardize_user_data(x)
   1442         if self.stateful:
   1443             if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:

~\anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    577             feed_input_shapes,
    578             check_batch_axis=False,  # Don't enforce the batch size.
--> 579             exception_prefix='input')
    580 
    581         if y is not None:

~\anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    143                             ': expected ' + names[i] + ' to have shape ' +
    144                             str(shape) + ' but got array with shape ' +
--> 145                             str(data_shape))
    146     return data
    147 

ValueError: Error when checking input: expected input_1 to have shape (60, 200, 4) but got array with shape (60, 200, 1)

我以为我已经照顾好了图像的大小。但是我想我缺少了一些东西。有人可以帮忙吗？

ValueError：检查输入时出错：预期input_1具有形状（60，200，4），但数组具有形状（60，200，1）

0 个答案: