How to use TensorFlow lite on a raspberry pi 4 without keras? - tensorflow

Question

Basically I want to convert this code snippet to code that opens a tflite model and does not use keras. I can not install keras on my raspberry pi 4 as it needs Tensorflow 2+.
model = keras.models.load_model( saved_model_path )
image_url = tf.keras.utils.get_file('Court', origin='https://squashvideo.site/share/court3.jpg' )
img = tf.keras.preprocessing.image.load_img(image_url, target_size=( 224, 224 ) )
os.remove(image_url) # Remove the cached file
img_array = tf.keras.preprocessing.image.img_to_array(img)
prediction_scores = model.predict(np.expand_dims(img_array, axis=0)/255)
score = tf.nn.softmax(prediction_scores[0])
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
Here's what I have tried which gives the error below:
from PIL import Image
def classify_image(interpreter, image, top_k=1):
tensor_index = interpreter.get_input_details()[0]['index']
input_tensor = interpreter.tensor(tensor_index)()[0]
input_tensor[:, :] = image
interpreter.invoke()
output_details = interpreter.get_output_details()[0]
output = np.squeeze(interpreter.get_tensor(output_details['index']))
scale, zero_point = output_details['quantization']
output = scale * (output - zero_point)
ordered = np.argpartition(-output, top_k)
return [(i, output[i]) for i in ordered[:top_k]][0]
interpreter = Interpreter('/var/www/html/share/AI/court.tflite')
interpreter.allocate_tensors()
_, height, width, _ = interpreter.get_input_details()[0]['shape']
print("Image Shape (", width, ",", height, ")")
data_folder = "/var/www/html/share/"
image = Image.open(data_folder + "court1.jpg").convert('RGB').resize((width, height))
label_id, prob = classify_image(interpreter, image)
Running gives the error:
squash#court1:/var/www/html/share/AI $ python3 test.py
Image Shape ( 224 , 224 )
Traceback (most recent call last):
File "test.py", line 44, in <module>
label_id, prob = classify_image(interpreter, image)
File "test.py", line 22, in classify_image
interpreter.invoke()
File "/home/squash/.local/lib/python3.7/site-packages/tflite_runtime/interpreter.py", line 539, in invoke
self._ensure_safe()
File "/home/squash/.local/lib/python3.7/site-packages/tflite_runtime/interpreter.py", line 287, in _ensure_safe
data access.""")
RuntimeError: There is at least 1 reference to internal data
in the interpreter in the form of a numpy array or slice. Be sure to
only hold the function returned from tensor() if you are using raw
data access.

Answer 1

The error is in the way you are feeding data to the tflite Interpreter here:
input_tensor = interpreter.tensor(tensor_index)()[0]
input_tensor[:, :] = image
The Image.open function return an Image object. You need to convert it into binary data before feeding it to a tensor. An you should use:
interpreter.set_tensor(0, image_data)
to set the data instead of above assignment.

Answer 2

Think I fixed it by doing this:
img = Image.open( image_url ).convert('RGB').resize((224, 224))
img_array = np.array ( img, dtype=np.float32 )
probs_lite = lite_model( np.expand_dims(img_array, axis=0)/255 )[0]
print ( probs_lite )
print (np.argmax(probs_lite))
score = tf.nn.softmax(probs_lite)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)