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(Chilkat2-Python) Amazon Rekognition - Detect Text in an Image
Detects text in the input image and converts it into machine-readable text. This example passes theimage as base64-encoded image bytes. For more information, see https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectText.html
import sys
import chilkat2
rest = chilkat2.Rest()
authAws = chilkat2.AuthAws()
authAws.AccessKey = "AWS_ACCESS_KEY"
authAws.SecretKey = "AWS_SECRET_KEY"
# Don't forget to change the region to your particular region. (Also make the same change in the call to Connect below.)
authAws.Region = "us-west-2"
authAws.ServiceName = "rekognition"
# SetAuthAws causes Chilkat to automatically add the following headers: Authorization, X-Amz-Date
rest.SetAuthAws(authAws)
# URL: https://rekognition.us-west-2.amazonaws.com/
bTls = True
port = 443
bAutoReconnect = True
# Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
success = rest.Connect("rekognition.us-west-2.amazonaws.com",port,bTls,bAutoReconnect)
if (success != True):
print("ConnectFailReason: " + str(rest.ConnectFailReason))
print(rest.LastErrorText)
sys.exit()
# Note: The above code does not need to be repeatedly called for each REST request.
# The rest object can be setup once, and then many requests can be sent. Chilkat will automatically
# reconnect within a FullRequest* method as needed. It is only the very first connection that is explicitly
# made via the Connect method.
# Load the JPG to be passed as base64 in the JSON.
bdJpg = chilkat2.BinData()
success = bdJpg.LoadFile("qa_data/jpg/monday_keep_smiling.jpg")
if (success != True):
print("Failed to load the input JPG file.")
sys.exit()
# We wish to send the following JSON in the body of our HTTP request:
# {
# "Image": {
# "Bytes": "base64_image_bytes"
# }
# }
# Here is the image we used for testing:
# Convert binary image bytes to base64.
# Note: We are explicitly keeping the data inside Chilkat to avoid having to pass large strings
# as arguments to function calls. This is important for some programming languages.
sbJpg = chilkat2.StringBuilder()
bdJpg.GetEncodedSb("base64",sbJpg)
json = chilkat2.JsonObject()
json.UpdateSb("Image.Bytes",sbJpg)
rest.AddHeader("Content-Type","application/x-amz-json-1.1")
rest.AddHeader("X-Amz-Target","RekognitionService.DetectText")
sbRequestBody = chilkat2.StringBuilder()
json.EmitSb(sbRequestBody)
sbResponseBody = chilkat2.StringBuilder()
success = rest.FullRequestSb("POST","/",sbRequestBody,sbResponseBody)
if (success != True):
print(rest.LastErrorText)
sys.exit()
respStatusCode = rest.ResponseStatusCode
print("response status code = " + str(respStatusCode))
if (respStatusCode >= 400):
print("Response Status Code = " + str(respStatusCode))
print("Response Header:")
print(rest.ResponseHeader)
print("Response Body:")
print(sbResponseBody.GetAsString())
sys.exit()
jResp = chilkat2.JsonObject()
jResp.LoadSb(sbResponseBody)
jResp.EmitCompact = False
print(jResp.Emit())
# Sample JSON response:
# (Sample code for parsing the JSON response is shown below)
# {
# "TextDetections": [
# {
# "Confidence": 95.99308776855469,
# "DetectedText": "( MONDAY IT'S",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.6399821043014526,
# "Left": 0.219133198261261,
# "Top": 0.08677978068590164,
# "Width": 0.7433173656463623
# },
# "Polygon": [
# {
# "X": 0.219133198261261,
# "Y": 0.3588336706161499
# },
# {
# "X": 0.8984103798866272,
# "Y": 0.08677978068590164
# },
# {
# "X": 0.9624505639076233,
# "Y": 0.4547080099582672
# },
# {
# "X": 0.2831733524799347,
# "Y": 0.7267619371414185
# }
# ]
# },
# "Id": 0,
# "Type": "LINE"
# },
# {
# "Confidence": 99.70352172851562,
# "DetectedText": "but keep",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.09703556448221207,
# "Left": 0.6335319876670837,
# "Top": 0.5153074264526367,
# "Width": 0.21070890128612518
# },
# "Polygon": [
# {
# "X": 0.6355597376823425,
# "Y": 0.5153074264526367
# },
# {
# "X": 0.8442409038543701,
# "Y": 0.5266726613044739
# },
# {
# "X": 0.8422132134437561,
# "Y": 0.6123430132865906
# },
# {
# "X": 0.6335319876670837,
# "Y": 0.6009777784347534
# }
# ]
# },
# "Id": 1,
# "Type": "LINE"
# },
# {
# "Confidence": 99.92333984375,
# "DetectedText": "Smiling",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.31578224897384644,
# "Left": 0.5070608258247375,
# "Top": 0.6086956262588501,
# "Width": 0.4795433282852173
# },
# "Polygon": [
# {
# "X": 0.5070608258247375,
# "Y": 0.6298336386680603
# },
# {
# "X": 0.9808917045593262,
# "Y": 0.6086956262588501
# },
# {
# "X": 0.9866041541099548,
# "Y": 0.9033399224281311
# },
# {
# "X": 0.5127732157707214,
# "Y": 0.9244779348373413
# }
# ]
# },
# "Id": 2,
# "Type": "LINE"
# },
# {
# "Confidence": 99.77294158935547,
# "DetectedText": "IT'S",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.09903381764888763,
# "Left": 0.668789803981781,
# "Top": 0.17874395847320557,
# "Width": 0.1449044644832611
# },
# "Polygon": [
# {
# "X": 0.668789803981781,
# "Y": 0.17874395847320557
# },
# {
# "X": 0.8136942386627197,
# "Y": 0.17874395847320557
# },
# {
# "X": 0.8136942386627197,
# "Y": 0.2777777910232544
# },
# {
# "X": 0.668789803981781,
# "Y": 0.2777777910232544
# }
# ]
# },
# "Id": 5,
# "ParentId": 0,
# "Type": "WORD"
# },
# {
# "Confidence": 98.44307708740234,
# "DetectedText": "MONDAY",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.11112251877784729,
# "Left": 0.5541401505470276,
# "Top": 0.3526569902896881,
# "Width": 0.39013487100601196
# },
# "Polygon": [
# {
# "X": 0.5541401505470276,
# "Y": 0.3526569902896881
# },
# {
# "X": 0.9442675113677979,
# "Y": 0.3502415418624878
# },
# {
# "X": 0.9458598494529724,
# "Y": 0.4613526463508606
# },
# {
# "X": 0.5541401505470276,
# "Y": 0.4637681245803833
# }
# ]
# },
# "Id": 4,
# "ParentId": 0,
# "Type": "WORD"
# },
# {
# "Confidence": 99.61898803710938,
# "DetectedText": "but",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.06521739065647125,
# "Left": 0.6353503465652466,
# "Top": 0.5241546034812927,
# "Width": 0.0843949019908905
# },
# "Polygon": [
# {
# "X": 0.6353503465652466,
# "Y": 0.5241546034812927
# },
# {
# "X": 0.7197452187538147,
# "Y": 0.5241546034812927
# },
# {
# "X": 0.7197452187538147,
# "Y": 0.5893719792366028
# },
# {
# "X": 0.6353503465652466,
# "Y": 0.5893719792366028
# }
# ]
# },
# "Id": 6,
# "ParentId": 1,
# "Type": "WORD"
# },
# {
# "Confidence": 99.78804779052734,
# "DetectedText": "keep",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.07971014827489853,
# "Left": 0.7308917045593262,
# "Top": 0.5265700221061707,
# "Width": 0.1114649698138237
# },
# "Polygon": [
# {
# "X": 0.7308917045593262,
# "Y": 0.5265700221061707
# },
# {
# "X": 0.8423566818237305,
# "Y": 0.5265700221061707
# },
# {
# "X": 0.8423566818237305,
# "Y": 0.6062802076339722
# },
# {
# "X": 0.7308917045593262,
# "Y": 0.6062802076339722
# }
# ]
# },
# "Id": 7,
# "ParentId": 1,
# "Type": "WORD"
# },
# {
# "Confidence": 89.76324462890625,
# "DetectedText": "(",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.16401274502277374,
# "Left": 0.27229300141334534,
# "Top": 0.6642512083053589,
# "Width": 0.2850286066532135
# },
# "Polygon": [
# {
# "X": 0.27229300141334534,
# "Y": 0.6642512083053589
# },
# {
# "X": 0.2707006335258484,
# "Y": 0.37922704219818115
# },
# {
# "X": 0.43471336364746094,
# "Y": 0.37922704219818115
# },
# {
# "X": 0.4363057315349579,
# "Y": 0.6642512083053589
# }
# ]
# },
# "Id": 3,
# "ParentId": 0,
# "Type": "WORD"
# },
# {
# "Confidence": 99.92333984375,
# "DetectedText": "Smiling",
# "Geometry": {
# "BoundingBox": {
# "Height": 0.294724702835083,
# "Left": 0.5079618096351624,
# "Top": 0.6304348111152649,
# "Width": 0.4734293222427368
# },
# "Polygon": [
# {
# "X": 0.5079618096351624,
# "Y": 0.6304348111152649
# },
# {
# "X": 0.9808917045593262,
# "Y": 0.6086956262588501
# },
# {
# "X": 0.9856687784194946,
# "Y": 0.9033816456794739
# },
# {
# "X": 0.512738823890686,
# "Y": 0.9227052927017212
# }
# ]
# },
# "Id": 8,
# "ParentId": 2,
# "Type": "WORD"
# }
# ],
# "TextModelVersion": "3.0"
# }
#
#
# Sample code for parsing the JSON response...
# Use the following online tool to generate parsing code from sample JSON:
# Generate Parsing Code from JSON
TextModelVersion = jResp.StringOf("TextModelVersion")
i = 0
count_i = jResp.SizeOfArray("TextDetections")
while i < count_i :
jResp.I = i
Confidence = jResp.StringOf("TextDetections[i].Confidence")
DetectedText = jResp.StringOf("TextDetections[i].DetectedText")
GeometryBoundingBoxHeight = jResp.StringOf("TextDetections[i].Geometry.BoundingBox.Height")
GeometryBoundingBoxLeft = jResp.StringOf("TextDetections[i].Geometry.BoundingBox.Left")
GeometryBoundingBoxTop = jResp.StringOf("TextDetections[i].Geometry.BoundingBox.Top")
GeometryBoundingBoxWidth = jResp.StringOf("TextDetections[i].Geometry.BoundingBox.Width")
Id = jResp.IntOf("TextDetections[i].Id")
v_Type = jResp.StringOf("TextDetections[i].Type")
ParentId = jResp.IntOf("TextDetections[i].ParentId")
j = 0
count_j = jResp.SizeOfArray("TextDetections[i].Geometry.Polygon")
while j < count_j :
jResp.J = j
X = jResp.StringOf("TextDetections[i].Geometry.Polygon[j].X")
Y = jResp.StringOf("TextDetections[i].Geometry.Polygon[j].Y")
j = j + 1
i = i + 1
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