segmenting.py

import requests
import image_slicer


# Gets the contents of an image file to be sent to the
# machine learning model for classifying
def getImageFileData(locationOfImageFile):
    with open(locationOfImageFile, "rb") as f:
        data = f.read()
        return data.encode("base64")


# This function will pass your image to the machine learning model
# and return the top result with the highest confidence
def classify(imagefile):
    key = "MY-API-KEY"
    url = "https://machinelearningforkids.co.uk/api/scratch/"+ key + "/classify"

    response = requests.post(url, json={ "data" : getImageFileData(imagefile) })

    if response.ok:
        responseData = response.json()
        topMatch = responseData[0]
        return topMatch
    else:
        response.raise_for_status()


def chopImageIntoBits(imagefile):
    return image_slicer.slice(imagefile, 14)



imageSegments = chopImageIntoBits("my-image-file.jpg")

for imageSegment in imageSegments:
    results = classify(imageSegment.filename)

    topleftX = imageSegment.coords[0]
    topleftY = imageSegment.coords[1]
    width = imageSegment.image.size[0]
    height = imageSegment.image.size[1]

    label = results["class_name"]
    confidence = results["confidence"]

    print ("section of image %dx%d starting at %d,%d (file name %s)" % (width, height, topleftX, topleftY, imageSegment.filename))
    print ("      result : '%s' with %d%% confidence" % (label, confidence))