问TensorFlow模型正确地预测图像，而不是从实时视频流中预测帧？

EN

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
rescaling_2 (Rescaling)      (None, 180, 180, 3)       0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 180, 180, 16)      448
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 90, 90, 16)        0
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 90, 90, 32)        4640
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 45, 45, 32)        0
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 45, 45, 64)        18496
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 30976)             0
_________________________________________________________________
dense_2 (Dense)              (None, 128)               3965056
_________________________________________________________________
dense_3 (Dense)              (None, 3)                 387
=================================================================
Total params: 3,989,027
Trainable params: 3,989,027
Non-trainable params: 0
_________________________________________________________________
Found 1068 files belonging to 3 classes.

def testModel(imageName):
  import cv2
  from PIL import Image
  from tensorflow.keras.preprocessing import image_dataset_from_directory
  batch_size = 32
  img_height = 180
  img_width = 180
  img = keras.preprocessing.image.load_img(
  imageName,
  target_size=(img_height, img_width),
  interpolation = "bilinear",
  color_mode = 'rgb'
  )
 
  #preprocessing different here
  img_array = keras.preprocessing.image.img_to_array(img)
  img_array = tf.expand_dims(img_array, 0) #Create a batch
 
  predictions = new_model.predict(img_array)
  score = predictions[0]
  classes = ['1', '2','3']
prediction = classes[np.argmax(score)]
 
  print(
      "This image {} most likely belongs to {} with a {:.2f} percent confidence."
      .format(imageName, classes[np.argmax(score)], 100 * np.max(score))
  )
 
  return prediction

#image_dataset_from_directory returns a tf.data.Dataset that yields batches of images from 
#the subdirectories class_a and class_b, together with labels 0 and 1.
from keras.preprocessing import image
directory_test = "/content/test"
tf.keras.utils.image_dataset_from_directory(
    directory_test, labels='inferred', label_mode='int',
    class_names=None, color_mode='rgb', batch_size=32, image_size=(256,
    256), shuffle=True, seed=None, validation_split=None, subset=None,
    interpolation='bilinear', follow_links=False,
    crop_to_aspect_ratio=False
)
 
tf.keras.utils.image_dataset_from_directory(directory_test, labels='inferred')
 
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  directory_test,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)