# Teachable machine

###### tags: `AI` `python` `Teachable`

# My codes

fetch picture
https://colab.research.google.com/drive/1vH1lqbZVxH6AmHAGu2mT3GMkfYxC0Uwd

Ai identify

https://colab.research.google.com/drive/1mhlTO8Lx16J_X0bT8wHxglouCy2Q_vZ0#scrollTo=kGUvQWv-lNWg

# Data

## Fetch resource by using python

## library

import requests 
url ="https://data.nhi.gov.tw/resource/Nhi_Fst/Fstdata.csv"

Remark: csv-> a file form of Excel

response = requests.get(url)
response

# Model
## Teachable machine


---

Model

main()

from keras.models import load_model
from PIL import Image, ImageOps #Install pillow instead of PIL
import numpy as np

filename = take_photo()


# Disable scientific notation for clarity
np.set_printoptions(suppress=True)

# Load the model
model = load_model('keras_model.h5', compile=False)

# Load the labels
class_names = open('labels.txt', 'r').readlines()

# 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(filename).convert('RGB')

#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.LANCZOS)

#turn the image into a numpy array
image_array = np.asarray(image)

# 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)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

print('Class:', class_name, end='')
print('Confidence score:', confidence_score)


Capture images

Include code of the take photo function



Function Construct

from IPython.display import display, Javascript
from google.colab.output import eval_js
from base64 import b64decode

def take_photo(filename='photo.jpg', quality=0.8):
  js = Javascript('''
    async function takePhoto(quality) {
      const div = document.createElement('div');
      const capture = document.createElement('button');
      capture.textContent = 'Capture';
      div.appendChild(capture);

      const video = document.createElement('video');
      video.style.display = 'block';
      const stream = await navigator.mediaDevices.getUserMedia({video: true});

      document.body.appendChild(div);
      div.appendChild(video);
      video.srcObject = stream;
      await video.play();

      // Resize the output to fit the video element.
      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);

      // Wait for Capture to be clicked.
      await new Promise((resolve) => capture.onclick = resolve);

      const canvas = document.createElement('canvas');
      canvas.width = video.videoWidth;
      canvas.height = video.videoHeight;
      canvas.getContext('2d').drawImage(video, 0, 0);
      stream.getVideoTracks()[0].stop();
      div.remove();
      return canvas.toDataURL('image/jpeg', quality);
    }
    ''')
  display(js)
  data = eval_js('takePhoto({})'.format(quality))
  binary = b64decode(data.split(',')[1])
  with open(filename, 'wb') as f:
    f.write(binary)
  return filename

Take a picture by using web camera

from IPython.display import Image
try:
  filename = take_photo()
  print('Saved to {}'.format(filename))
  
  # Show the image which was just taken.
  display(Image(filename))
except Exception as err:
  # Errors will be thrown if the user does not have a webcam or if they do not
  # grant the page permission to access it.
  print(str(err))

Result





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Sample
[https://drive.google.com/drive/folders/1bRf3qNji5bPKCFMfpPvAvRB5zmAGRudt](https://drive.google.com/drive/folders/1bRf3qNji5bPKCFMfpPvAvRB5zmAGRudt?authuser=2)

Col lab
https://colab.research.google.com/drive/1vH1lqbZVxH6AmHAGu2mT3GMkfYxC0Uwd#scrollTo=cPmKwKpzAJOt

Reference

https://docs.google.com/presentation/d/129JDRkz4k5FScpTzC0GX2hN-omfqOxmHOkSpwhkkvM0/edit#slide=id.g1922aa4ceba_0_361

programe
https://colab.research.google.com/drive/1RY8BEmNy_8UVJIOCoDPBfRe8DDh_lXL2?usp=sharing