Dancing/standing ai

Description: Using the google teachable machine I created an AI that would tell if a person was either standing or dancing. I created a class for a person standing and a person dancing. I then used my computer's webcam to take pictures of my brother and I either standing or dancing. There were 342 pictures taken for dancing and 623 pictures taken for standing. The data was then trained, and afterwards my model was used to distinguish if someone was dancing or standing.

Model:

Code:

import tensorflow.keras

from PIL import Image, ImageOps

import numpy as np

# Disable scientific notation for clarity

np.set_printoptions(suppress=True)

# Load the model

model = tensorflow.keras.models.load_model('keras_model.h5')

# Create the array of the right shape to feed into the keras model

# The 'length' or number of images you can put into the array is

# determined by the first position in the shape tuple, in this case 1.

data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

# Replace this with the path to your image

image = Image.open('test_photo.jpg')

#resize the image to a 224x224 with the same strategy as in TM2:

#resizing the image to be at least 224x224 and then cropping from the center

size = (224, 224)

image = ImageOps.fit(image, size, Image.ANTIALIAS)

#turn the image into a numpy array

image_array = np.asarray(image)

# display the resized image

image.show()

# Normalize the image

normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

# Load the image into the array

data[0] = normalized_image_array

# run the inference

prediction = model.predict(data)

print(prediction)

Video: