PowerBuilder
PowerBuilder
Amazon Rekognition - Detect Text in an Image
See more Amazon Rekognition Examples
Detects text in the input image and converts it into machine-readable text. This example passes theimage as base64-encoded image bytes.Chilkat PowerBuilder Downloads
integer li_rc
long li_Success
oleobject loo_Rest
oleobject loo_AuthAws
long li_BTls
long li_Port
long li_BAutoReconnect
oleobject loo_BdJpg
oleobject loo_SbJpg
oleobject loo_Json
oleobject loo_SbRequestBody
oleobject loo_SbResponseBody
long li_RespStatusCode
oleobject loo_JResp
string ls_Confidence
string ls_DetectedText
string ls_GeometryBoundingBoxHeight
string ls_GeometryBoundingBoxLeft
string ls_GeometryBoundingBoxTop
string ls_GeometryBoundingBoxWidth
long li_Id
string ls_V_Type
long li_ParentId
long j
long li_Count_j
string X
string Y
string ls_TextModelVersion
long i
long li_Count_i
li_Success = 0
loo_Rest = create oleobject
li_rc = loo_Rest.ConnectToNewObject("Chilkat.Rest")
if li_rc < 0 then
destroy loo_Rest
MessageBox("Error","Connecting to COM object failed")
return
end if
loo_AuthAws = create oleobject
li_rc = loo_AuthAws.ConnectToNewObject("Chilkat.AuthAws")
loo_AuthAws.AccessKey = "AWS_ACCESS_KEY"
loo_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.)
loo_AuthAws.Region = "us-west-2"
loo_AuthAws.ServiceName = "rekognition"
// SetAuthAws causes Chilkat to automatically add the following headers: Authorization, X-Amz-Date
loo_Rest.SetAuthAws(loo_AuthAws)
// URL: https://rekognition.us-west-2.amazonaws.com/
li_BTls = 1
li_Port = 443
li_BAutoReconnect = 1
// Don't forget to change the region domain (us-west-2.amazonaws.com) to your particular region.
li_Success = loo_Rest.Connect("rekognition.us-west-2.amazonaws.com",li_Port,li_BTls,li_BAutoReconnect)
if li_Success <> 1 then
Write-Debug "ConnectFailReason: " + string(loo_Rest.ConnectFailReason)
Write-Debug loo_Rest.LastErrorText
destroy loo_Rest
destroy loo_AuthAws
return
end if
// 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.
loo_BdJpg = create oleobject
li_rc = loo_BdJpg.ConnectToNewObject("Chilkat.BinData")
li_Success = loo_BdJpg.LoadFile("qa_data/jpg/monday_keep_smiling.jpg")
if li_Success <> 1 then
Write-Debug "Failed to load the input JPG file."
destroy loo_Rest
destroy loo_AuthAws
destroy loo_BdJpg
return
end if
// 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:
// (image:https://example-code.com/images/monday_keep_smiling.jpg/endImage)
// 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.
loo_SbJpg = create oleobject
li_rc = loo_SbJpg.ConnectToNewObject("Chilkat.StringBuilder")
loo_BdJpg.GetEncodedSb("base64",loo_SbJpg)
loo_Json = create oleobject
li_rc = loo_Json.ConnectToNewObject("Chilkat.JsonObject")
loo_Json.UpdateSb("Image.Bytes",loo_SbJpg)
loo_Rest.AddHeader("Content-Type","application/x-amz-json-1.1")
loo_Rest.AddHeader("X-Amz-Target","RekognitionService.DetectText")
loo_SbRequestBody = create oleobject
li_rc = loo_SbRequestBody.ConnectToNewObject("Chilkat.StringBuilder")
loo_Json.EmitSb(loo_SbRequestBody)
loo_SbResponseBody = create oleobject
li_rc = loo_SbResponseBody.ConnectToNewObject("Chilkat.StringBuilder")
li_Success = loo_Rest.FullRequestSb("POST","/",loo_SbRequestBody,loo_SbResponseBody)
if li_Success <> 1 then
Write-Debug loo_Rest.LastErrorText
destroy loo_Rest
destroy loo_AuthAws
destroy loo_BdJpg
destroy loo_SbJpg
destroy loo_Json
destroy loo_SbRequestBody
destroy loo_SbResponseBody
return
end if
li_RespStatusCode = loo_Rest.ResponseStatusCode
Write-Debug "response status code = " + string(li_RespStatusCode)
if li_RespStatusCode >= 400 then
Write-Debug "Response Status Code = " + string(li_RespStatusCode)
Write-Debug "Response Header:"
Write-Debug loo_Rest.ResponseHeader
Write-Debug "Response Body:"
Write-Debug loo_SbResponseBody.GetAsString()
destroy loo_Rest
destroy loo_AuthAws
destroy loo_BdJpg
destroy loo_SbJpg
destroy loo_Json
destroy loo_SbRequestBody
destroy loo_SbResponseBody
return
end if
loo_JResp = create oleobject
li_rc = loo_JResp.ConnectToNewObject("Chilkat.JsonObject")
loo_JResp.LoadSb(loo_SbResponseBody)
loo_JResp.EmitCompact = 0
Write-Debug loo_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
ls_TextModelVersion = loo_JResp.StringOf("TextModelVersion")
i = 0
li_Count_i = loo_JResp.SizeOfArray("TextDetections")
do while i < li_Count_i
loo_JResp.I = i
ls_Confidence = loo_JResp.StringOf("TextDetections[i].Confidence")
ls_DetectedText = loo_JResp.StringOf("TextDetections[i].DetectedText")
ls_GeometryBoundingBoxHeight = loo_JResp.StringOf("TextDetections[i].Geometry.BoundingBox.Height")
ls_GeometryBoundingBoxLeft = loo_JResp.StringOf("TextDetections[i].Geometry.BoundingBox.Left")
ls_GeometryBoundingBoxTop = loo_JResp.StringOf("TextDetections[i].Geometry.BoundingBox.Top")
ls_GeometryBoundingBoxWidth = loo_JResp.StringOf("TextDetections[i].Geometry.BoundingBox.Width")
li_Id = loo_JResp.IntOf("TextDetections[i].Id")
ls_V_Type = loo_JResp.StringOf("TextDetections[i].Type")
li_ParentId = loo_JResp.IntOf("TextDetections[i].ParentId")
j = 0
li_Count_j = loo_JResp.SizeOfArray("TextDetections[i].Geometry.Polygon")
do while j < li_Count_j
loo_JResp.J = j
X = loo_JResp.StringOf("TextDetections[i].Geometry.Polygon[j].X")
Y = loo_JResp.StringOf("TextDetections[i].Geometry.Polygon[j].Y")
j = j + 1
loop
i = i + 1
loop
destroy loo_Rest
destroy loo_AuthAws
destroy loo_BdJpg
destroy loo_SbJpg
destroy loo_Json
destroy loo_SbRequestBody
destroy loo_SbResponseBody
destroy loo_JResp