A Python package to simplify the deployment process of exported Teachable Machine models into different Robotics, AI and IoT controllers such as: Raspberry Pi, Jetson Nano and any other SBCs using TensorFlowLite framework.
Developed by @MeqdadDev
Image Classification: use exported and quantized TensorFlow Lite model from Teachable Machine platfrom (a model file with tflite extension).
Python >= 3.7
pip install teachable-machine-litenumpy
tflite-runtime
Pillow (PIL)from teachable_machine_lite import TeachableMachineLite
import cv2 as cv
cap = cv.VideoCapture(0)
model_path = 'model.tflite'
image_file_name = "frame.jpg"
labels_path = "labels.txt"
tm_model = TeachableMachineLite(model_path=model_path, labels_file_path=labels_path)
while True:
ret, frame = cap.read()
cv.imshow('Cam', frame)
cv.imwrite(image_file_name, frame)
results = tm_model.classify_frame(image_file_name)
print("results:",results)
k = cv.waitKey(1)
if k% 255 == 27:
# press ESC to close camera view.
break