Page Comparison

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import requests
import os

request_session = requests.Session()

for image in os.listdir('images_folder'):
    with open(os.path.join('images_folder', image), 'rb') as image:
        response = requests.post(
            url='http://127.0.0.1:8000/analyze_image?api_key=728a9180-8357-11ec-b645-e917eb5f5d27',
            data=image.read(),
            headers={'Content-Type': 'application/octet-stream'}
        )
        
        print(response.json())

Response type

Query parameter ‘response_type’ determines what content will be sent in the response that is returned from PEKAT VISION after processing the request. The request url changes in the following way:

response = requests.post(
            url='http://127.0.0.1:8000/analyze_image?response_type=annotated_image',
            data=image.read(),
            headers={'Content-Type': 'application/octet-stream'}
        )

If there are multiple query parameters, connect with '&', e.g. ‘context’ with API key:

url='http://127.0.0.1:8000/analyze_image?api_key=728a9180-8357-11ec-b645-e917eb5f5d27&response_type=annotated_image'

Context

context – a serialized context in JSON format. The contents are explained on the Context page. context will be always there by result

context starts empty by the deafult, JSON dictonary.. also contatins images

priintscreen

‘image’ You can access the context json from response using:

j = response.json()

Images

Further options return a PNG image in binary form in the response. It needs to be decoded afterwards to further work with it as an image.

Example of decoding and showing the image with PIL library:

from PIL import Image
from io import BytesIO

img = Image.open(BytesIO(response.content))
img.show()

Example of decoding and showing the image with OpenCV library:

import cv2
import numpy as np

img = cv2.imdecode(np.frombuffer(response.content, np.uint8), 1)
cv2.imshow("Image", img)
cv2.waitKey(0)

image – processed image - output image that passed through the flow…..‘annotated_image’ flow, but without any heatmaps or rectangles (e.g. if the image is scaled during the flow, it returns scaled image).

annotated_image – processed image with annotations - heatmaps over the image….image

‘heatmap’ heatmap - response is heatmap ‘.png’ image format - contains only the heat maps over the image (without the image!)…….

For image and annotated_image all those image response types, the serialized context is added to the header headers with the header title 'ContextBase64utf'. To get it to json form, you can use this code:

import base64
import json

# Decode from Base64
context_str = base64.b64decode(response.headers['ContextBase64utf'])
# Load json from string
context = json.loads(context_str)