如何在 Python 程式中使用 Google Teachable Machine 建立的 model

# Python program to control mouse based on head position # Import necessary modules
import numpy as np
import cv2
import tensorflow as tf

# Using laptop’s webcam as the source of video
cap = cv2.VideoCapture(0)
cap.set(3, 480) # ID 3 = width
cap.set(4, 320) # ID 4 = height

# Labels — The various outcome possibilities
labels = ["Up","Down","Left","Right","Neutral"]

# Loading the model weigths we just downloaded
model = tf.keras.models.load_model("keras_model.h5", compile = False)
while True:
success, image = cap.read()
if success == False:
break

# Necessary to avoid conflict between left and right
image = cv2.flip(image, 1)
cv2.imshow("Frame", image)

# The model takes an image of dimensions (224,224) as input so let’s
# reshape our image to the same.
img = cv2.resize(image, (224, 224))

# Convert the image to a numpy array
img = np.array(img, dtype=np.float32)
img = np.expand_dims(img, axis=0)

# Normalizing input image
img = img / 255

# Predict the class
prediction = model.predict(img)

# Map the prediction to the labels
# Rnp.argmax returns the indices of the maximum values along an axis.
predicted_labels = labels[np.argmax(prediction[0], axis=-1)]
# print(predicted_labels)
print(predicted_labels, np.argmax(prediction[0], axis=-1), prediction[0])

# Close all windows if one second has passed and ‘q’ is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break

# Release open connections
cap.release()
cv2.destroyAllWindows()