Page Comparison

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'http://127.0.0.1:8000/analyze_image?data=SomeInfo'

In the Code module, you can access this string using:

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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 URL changes in the following way (example for annotated_image response type):

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

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

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context – a serialized context in JSON format. The contents are explained on the Context page. You can access the context json from the response using:

j = response.json()

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

Example of decoding and showing the image with the PIL library:

from PIL import Image
from io import BytesIO

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

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

annotated_image – processed image with annotations - heatmaps over the image

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For all those image response types, the serialized context is by default added to the headers with the header title 'ContextBase64utf'. To get it to json 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)

Context in body

When gettting getting an image in the response, by default the context is sent in headers. However, with a very large number of defects, the maximum header length limit can be reached. To solve this, you can use the option to send context in the body of the response together with the image and get the image length in a header 'ImageLen' to be able to divide the two parts after. To activate this mode, add the following to the request:

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Example Python code of the usage when getting the last image:

import requests
import cv2
import numpy as np
import json

port = '8000'

request_session = requests.Session()

response = requests.get(
    url=f'http://127.0.0.1:{port}/last_image?response_type=annotated_image&context_in_body=t',
    headers={'Content-Type': 'application/octet-stream'}
)

# Get image length
img_len = int(response.headers['ImageLen'])

# Decode and show image with heatmap
img = cv2.imdecode(np.frombuffer(response.content[:img_len], np.uint8), 1)
cv2.imshow("Image", img)
cv2.waitKey(0)

# Get and print context
context = json.loads(response.content[img_len:])
print(context)

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If you have a running project on a port e.g. 8000, you can test the API by accessing this address in your browser (if PEKAT is running on the local computer, otherwise replace localhost with the IP address of the remote PC):

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This displays the testing app, where you can choose to display the last processed image or send a new image to analyze. You can choose the Response type - Context, Image, Annotated image (image with a heatmap), or Heatmap. If the project has Secure image analyze enabled, you also need to enter the API key.

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If Analyze image is chosen, you can also select the image that will be sent through the API. If the response type is different than the Context, the default behavior is that the image is sent in the body of the response and the context is sent in the headers - to change this and send both in the body, check the box with Context in the body. The detail of the request structure will be displayed.

After you click Send, you will also see the structure of the response in the blue info box, context (as you would see it in Inspection), and optionally an image based on the response type.

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