I was building a model and I wanted to test its performance, thus I imported a local file and load it and try to predict its label with the following code:
from tensorflow.preprocessing import image
# Other imports for tensorlfow etc.
#...
# Sample image
img_path = "./Model/data/brain/train/Glioma/images/gg (2).jpg"
img = image.load_img(img_path,target_size=(256,256))
arr = image.img_to_array(img)
t_img = tf.convert_to_tensor(arr)
print(t_img.shape) # Returns (256,256,3)
# Client testing
client = Client("brain") # Custom Class. Contains model: Sequential (compiled and trained)
client.predict(img=t_img) # Calls self.model.predict(t_img)
However I get the following error:
Invalid input shape for input Tensor("data:0", shape=(32, 256, 3), dtype=float32). Expected shape (None, 256, 256, 3), but input has incompatible shape (32, 256, 3)
I have an input layer in the trained model which has input_shape=[256,256,3] (comes from image width, height, and rgb values)
Can you help me understand the issue and solve it?
Dr. Snoopy already gave the answer in the comments, but for the sake of completeness a short solution copied from the TF load_image page:
image = keras.utils.load_img(image_path)
input_arr = keras.utils.img_to_array(image)
input_arr = np.array([input_arr]) # Convert single image to a batch.
predictions = model.predict(input_arr)
model.predict() expects batches of images. This solultion would transform your (256, 256, 3) shape to (1, 256, 256, 3). There are also other solutions, e.g. with tf.expand_dims(image, 0) if you rather want to work with tensors directly instead of arrays.