Predicting single image using Tensorflow not being accurate - python-3.x

Question

I'm trying to build a CNN model in order to classify an image, but whenever the training is done and I try to feed it a single image (from the training dataset) it misclassifies this image always.
Please take a look at the code I wrote below.
Thank you in advance.
First, I declared an Image Data Generator for both my training and testing sets:
train_datagen = ImageDataGenerator(rescale = 1./255, rotation_range=20, horizontal_flip = True,
validation_split=0.3)
test_datagen = ImageDataGenerator(rescale = 1./255,validation_split=0.3)
Then, I used the flow_from_directory() function to load the images:
train_generator = train_datagen.flow_from_directory(
data_dir,
shuffle=False,
subset='training',
target_size = (224, 224),
class_mode = 'categorical'
)
test_generator = test_datagen.flow_from_directory(
data_dir,
shuffle=False,
subset='validation',
target_size = (224, 224),
class_mode = 'categorical'
)
I then loaded a pretrained model and added a few layers to build my model:
pretrained_model = VGG16(weights="imagenet", include_top=False,
input_tensor=input_shape)
pretrained_model.trainable = False
model = tf.keras.Sequential([
pretrained_model,
Flatten(name="flatten"),
Dense(3, activation="softmax")
])
I then trained the model :
INIT_LR = 3e-4
EPOCHS = 15
opt = Adam(lr=INIT_LR)
model.compile(loss="categorical_crossentropy", optimizer='Adam', metrics=["accuracy"])
H = model.fit(
train_generator,
validation_data=test_generator,
epochs=EPOCHS,
verbose= 1)
Then came the part to predict a single image:
I chose an image that was part of the training set, I even overfitted the model to make sure the predictions should be correct, but it was giving me wrong results for every image I input to the model.
I tried the following ways:
image = image.load_img(url,target_size = (224, 224))
img = tf.keras.preprocessing.image.img_to_array(image)
img = np.array([img])
img = img.astype('float32') / 255.
img = tf.keras.applications.vgg16.preprocess_input(img)
This didn't work
image = cv2.imread(url)
image = cv2.normalize(image, None,beta=255, dtype=cv2.CV_32F)
image = cv2.resize(image, (224, 224))
image = np.expand_dims(image, axis=0)
This also didn't work, I also tried many other ways to predict a single image, but none worked.
Finally, the only way was that I had to create an Image Data Generator and Flow From Directory for this single image, and it worked, but I believe that's not how it should be done.

Answer 1

The code img = tf.keras.applications.vgg16.preprocess_input(img) scales the pixel
values in the image to values between -1 to +1 assuming the original pixel values are in the range 0 to 255. In the previous line of code
img = img.astype('float32') / 255.
You rescaled the pixels. So remove that line of code. Now to predict a single image you need to expand the dimensions with
img = np.expand_dims(img, axis=0)
In your second code effort be aware the CV2 reads in images as BGR. If your model was trained on RGB images then your predictions will be wrong. Use the code below to convert the image to RGB.
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
As a side note you can replace tf.keras.applications.vgg16.preprocess_input(img) with the function below which will scale the images between -1 to +1
def scalar(img):
return img/127.5 - 1

Answer 2

This answer could be one starting point:
Resnet50 produces different prediction when image loading and resizing is done with OpenCV
These are possible differences (short gist):
RGB vs BGR (OpenCV loads BGR)
The interpolation method used (INTER_LINEAR vs INTER_NEAREST).
img_to_array() transforms the data type into float32 rather than uint8 which is obtained by default when loading with OpenCV.
tf.keras.applications.vgg16.preprocess_input(img). This preprocessing function can actually differ from what you have written above as image preprocessing; it is also notable that, if you do not preprocess it while training in this particular way (preprocess_input()) then it also makes sense to have bad results on the test set, since the preprocessings are different.
Hope these observations shed some light.