Virtual Pets ->Make -> Python

[centralparksteam]

The suggested Python code for Virtual Pets for an image on the internet returns an error using Python3:

TypeError: Object of type bytes is not JSON serializable

Made these changes for string from bytes using str with utf-8 and it works now.

import requests, base64

# Gets the contents of an image on the Internet to be
# sent to the machine learning model for classifying
def getImageUrlData(wwwLocationOfImage):
    #base64.b64encode(requests.get(wwwLocationOfImage).content)
    byte_data = base64.b64encode(requests.get(wwwLocationOfImage).content)
    string_data = str(byte_data, 'utf-8')
    return string_data


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

    response = requests.post(url, json={ "data" : getImageUrlData(imageurl) })

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


# CHANGE THIS to the URL of the image you want to classify
demo = classify("http://www.cityfood.com/media/resampled/articleElement/216/resampled_CUTIE.jpg")

label = demo["class_name"]
confidence = demo["confidence"]


# CHANGE THIS to do something different with the result
print ("result: '%s' with %d%% confidence" % (label, confidence))




Similar error with the Python3 code for image from webcam:

import cv2, requests, base64

# Gets an image from the webcam
def getWebcamImageData():
    cam = cv2.VideoCapture(0)
    try:
        ok, image = cam.read()
        if ok != True:
            raise ValueError("Problem using the webcam")
        ok, data = cv2.imencode('.jpg', image)
        if ok != True:
            raise ValueError("Problem getting image data")
        #return base64.b64encode(data)
        byte_data = base64.b64encode(data)
        string_data = str(byte_data, 'utf-8')
        return string_data
    finally:
        cam.release()


# This function will pass your image to the machine learning model
# and return the top result with the highest confidence
def classify():
    key = "6e614570-5431-11e9-8a47-c5f6138f27983082d58f-fe8a-4f16-8b69-0e9c1d30c7da"
    url = "https://machinelearningforkids.co.uk/api/scratch/"+ key + "/classify"

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

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


demo = classify()

label = demo["class_name"]
confidence = demo["confidence"]


# CHANGE THIS to do something different with the result
print ("result: '%s' with %d%% confidence" % (label, confidence))




Then for the Python code for using a local file, it gave an error:
AttributeError: 'bytes' object has no attribute 'encode'

So changed it to this:

import requests, base64
#import requests

# 3/31/19 Modified the data encoding to use base64



# 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")
        byte_version = base64.b64encode(data)
        string_version = str(byte_version, 'utf-8')
        return string_version
        


# This function will pass your image to the machine learning model
# and return the top result with the highest confidence
def classify(imagefile):
    key = "6e614570-5431-11e9-8a47-c5f6138f27983082d58f-fe8a-4f16-8b69-0e9c1d30c7da"
    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()


# CHANGE THIS to the name of the image file you want to classify
demo = classify("heart.jpg")

label = demo["class_name"]
confidence = demo["confidence"]


# CHANGE THIS to do something different with the result
print ("result: '%s' with %d%% confidence" % (label, confidence))