Hi @Will ,

So I was able to replicate this with the version that was checked out on my iMac. Steps:

cd ~/depthai-experiments/gen2-ocr 
python3 -m pip install -r requirements.txt

Which produced:

Looking in indexes: https://pypi.org/simple, https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local
Requirement already satisfied: numpy==1.19.5 in /Users/leeroy/Library/Python/3.9/lib/python/site-packages (from -r requirements.txt (line 1)) (1.19.5)
Requirement already satisfied: opencv-python==4.5.1.48 in /Users/leeroy/Library/Python/3.9/lib/python/site-packages (from -r requirements.txt (line 2)) (4.5.1.48)
Collecting depthai==0.0.2.1+e88c98c8a7681dd16cd754a832df1165380ac305
  Using cached https://artifacts.luxonis.com/artifactory/luxonis-python-snapshot-local/depthai/depthai-0.0.2.1%2Be88c98c8a7681dd16cd754a832df1165380ac305-cp39-cp39-macosx_10_9_x86_64.whl (4.3 MB)
Installing collected packages: depthai
  Attempting uninstall: depthai
    Found existing installation: depthai 0.4.1.1
    Uninstalling depthai-0.4.1.1:
      Successfully uninstalled depthai-0.4.1.1
Successfully installed depthai-0.0.2.1+e88c98c8a7681dd16cd754a832df1165380ac305

And then I ran:

python3 main.py

Which resulted in:

/Users/leeroy/depthai-experiments/gen2-ocr/main.py:15: DeprecationWarning: setCamId() is deprecated, use setBoardSocket() instead.
  colorCam.setCamId(0)
libc++abi.dylib: terminating with uncaught exception of type std::runtime_error: XLink write error, error message: X_LINK_ERROR
zsh: abort      python3 main.py

I am now updating the repo with git pull on the master branch to check if this improves it.

Same error:

python3 main.py                           
/Users/leeroy/depthai-experiments/gen2-ocr/main.py:15: DeprecationWarning: setCamId() is deprecated, use setBoardSocket() instead.
  colorCam.setCamId(0)
libc++abi.dylib: terminating with uncaught exception of type std::runtime_error: XLink write error, error message: X_LINK_ERROR
zsh: abort      python3 main.py

So I am checking with the team now. The fact that I can reproduce this is good as I think we will now be able to fix it quickly.

Thanks and sorry about the issues here.

-Brandon

a year later

Dear Brandon:

we are using OAK-1 with EAST with text_recognition_0012 as demo video shown:


and source code below :

So far we are satisfy the

we have a request is we want to add ROI function and combine with source codeļ¼Œ then it could judge OCR ok or ng only inside of ROI , of course we can adjust ROI by execute CMD .
Please assist us to realize the above function ,

all the best~~

  • erik replied to this.

    Hello king , your google drive video/source code can't be accessed (it says that we have to request the access). Regarding the ROI, from my understanding you would like to specify the ROI and run text detection/OCR in that region?

      Dear erik:

      Yes~you are right
      as attached picture content is using text_recognition_0012 results that you couldn't seen by google video:

      As we know EAST OCR is 1080p resolution and getting all of text of whole paper.
      but our customer want only setting their own ROI and could adjust size of ROI ,finally running OCR inside ROI.

      for example like COGNEX OCR (shown red ROI):

      We have three ideas:

      1. We would like to use IMREAD_UNCHANGE to cutoff picture ,and imshow

      2. Change the aspect ratio (stretch the image)
        Apply letterboxing to the image,
        as below:
        https://docs.luxonis.com/projects/api/en/latest/tutorials/maximize_fov/

      3. SpatialLocationCalculator
        to create ROI and combine with text_recognition
        https://docs.luxonis.com/projects/api/en/latest/components/nodes/spatial_location_calculator/

      Please assist us which method could be match our goal??

      all the best~~

      • erik replied to this.

        Hello king , let me ask our ML engineer to suggest the best approach for this issue.
        Thanks, Erik

          Hey @king , so if I understand correctly, you do not need actual test detection, only test recognition? And you want to set a custom ROI?

          If that is so, you could use our ImageManip node, where you can set the ROI to some rectangle by providing the points for ROI and then resize the image so that it has correct shape for input to recognition network. Then you would have to link this ImageManip node to a Neural Network node that performs text recognition as in OCR example.

          So you could try editing this part of the code to use custom defined ROI instead of ROI from detections from the whole image. Which means you could skip the first NN node, but keep the rest the same.

          Does this make sense?

          Dear Matija:

          Thanks for your suggestion of custom ROI~ our target is not only text recognition but also ROI settings and running inside of ROI.

          We will follow your ImageManip ndoe and and compile it with text recognition first . maybe there are good results for us.

          all the best~~

          Dear Matija and erik:

          We could create ROI of 256x256 pixels due to east_text_detection_256x256 , but the results will be Influenced in black OCR and it cause incorrect results whc sometimes as below picture red frame :

          could you please assist us how to solve the issue ??

          all the best~~

          Hey, you could manually increase these two lines, so a larger are is cropped: https://github.com/luxonis/depthai-experiments/blob/master/gen2-ocr/main.py#L241-L242.

          I found out that rotate crops are slightly incorrect, but should be fixed with the ImageManip refactor which will be released soon. For now you can try increasing the width and height manually in the above lines, like:

          rr.size.width  = int(rotated_rect[1][0] * 1.1)
rr.size.height = int(rotated_rect[1][1] * 1.1)

          This will increase width and height for 10%.

          Best,
          Matija

          Dear LUXonis partners :
          We have OCR case as google drive as below and picture:

          We want to check each OCR SET/CAN/RES , but results is still fail due to influence by pattern of + and -
          we try every methods as you suggestion before (include rr.size.widthrr.size.height )but`still can't solve our customer needed.
          This case our customer have 10pcs OAK-1 request, if we can solve the issue. maybe create a background pictures with any color code into gen2-OCR and combine with modifying EAST. but we don't know how to modify it yet~~
          If you have a good solution for our case we can discuss more detail cooperation methods~

          all the beat~~

            king If you are limited to a set of these 3 words only, you could just filter them out with some distance metric or just check if the word is contained in the result. SET is in SSET, so you can know that SSET likely refers to SET, similarly CAN is in ICAN and RES in RESE.

            If you are looking at other words as well, it's going to be hard to eliminate this + sign. You could try decreasing the factor by which you increase the width.

            With image refactor that we are working on, I think the rotated squares should be cropped out better, but don't hold my word for it. I'll report some results when I'll be able to test it out. Until then, I propose you use one of the two approaches I mentioned above.

            Best,
            Matija

              8 days later

              Dear Matija:

              Thanks for your support of ROI OCR upgrade in this case ,we sincere that's could solve in this case , also we would like to know how long we could get the upgrade results??

              all the best~~

              Dear all:

              We have another issue is autofocus function about OAK-1 as Picture below:

              the OAK-1 is fixed on WD=150mm and only focus on A123 text ,we import autofocus.py into gen2-ocr
              we set A123 is ok condition and output OK pictures . it shows clear image with OK pictures but also shows blurry image with NG pictures sometimes , we try to modify parameter of autofocus.py and still shows blurry image, could you please assist us how to modify the parameter of autofocus.py and only shows clear image:

              import depthai as dai
import cv2
# Screen adjust for "left" "right" "up" "down"
# Step size ('W','A','S','D' controls)
STEP_SIZE = 8

# Create pipeline
pipeline = dai.Pipeline()

# Define sources and outputs
camRgb = pipeline.create(dai.node.ColorCamera)
videoEncoder = pipeline.create(dai.node.VideoEncoder)
stillEncoder = pipeline.create(dai.node.VideoEncoder)

controlIn = pipeline.create(dai.node.XLinkIn)
configIn = pipeline.create(dai.node.XLinkIn)
videoMjpegOut = pipeline.create(dai.node.XLinkOut)
stillMjpegOut = pipeline.create(dai.node.XLinkOut)
previewOut = pipeline.create(dai.node.XLinkOut)

controlIn.setStreamName('control')
configIn.setStreamName('config')
videoMjpegOut.setStreamName('video')
stillMjpegOut.setStreamName('still')
previewOut.setStreamName('preview')

# Properties
camRgb.setVideoSize(640, 360)
camRgb.setPreviewSize(300, 300)
videoEncoder.setDefaultProfilePreset(camRgb.getFps(), dai.VideoEncoderProperties.Profile.MJPEG)
stillEncoder.setDefaultProfilePreset(1, dai.VideoEncoderProperties.Profile.MJPEG)

# Linking
camRgb.video.link(videoEncoder.input)
camRgb.still.link(stillEncoder.input)
camRgb.preview.link(previewOut.input)
controlIn.out.link(camRgb.inputControl)
configIn.out.link(camRgb.inputConfig)
videoEncoder.bitstream.link(videoMjpegOut.input)
stillEncoder.bitstream.link(stillMjpegOut.input)

# Connect to device and start pipeline
with dai.Device(pipeline) as device:

    # Get data queues
    controlQueue = device.getInputQueue('control')
    configQueue = device.getInputQueue('config')
    previewQueue = device.getOutputQueue('preview')
    videoQueue = device.getOutputQueue('video')
    stillQueue = device.getOutputQueue('still')

    # Max cropX & cropY
    maxCropX = (camRgb.getResolutionWidth() - camRgb.getVideoWidth()) / camRgb.getResolutionWidth()
    maxCropY = (camRgb.getResolutionHeight() - camRgb.getVideoHeight()) / camRgb.getResolutionHeight()

    # Default crop
    cropX = 0
    cropY = 0
    sendCamConfig = True



    while True:
        previewFrames = previewQueue.tryGetAll()
        for previewFrame in previewFrames:
            cv2.imshow('preview', previewFrame.getData().reshape(previewFrame.getHeight(), previewFrame.getWidth(), 3))

        videoFrames = videoQueue.tryGetAll()
        for videoFrame in videoFrames:
            # Decode JPEG
            frame = cv2.imdecode(videoFrame.getData(), cv2.IMREAD_UNCHANGED)
            # Display
            cv2.imshow('video', frame)

            # Send new cfg to camera
            if sendCamConfig:
                cfg = dai.ImageManipConfig()
                cfg.setCropRect(cropX, cropY, 0, 0)
                configQueue.send(cfg)
                print('Sending new crop - x: ', cropX, ' y: ', cropY)
                sendCamConfig = False


        # Update screen (1ms pooling rate)
        key = cv2.waitKey(1)
        if key == ord('q'):
            break

        elif key == ord('t'):
            print("Autofocus trigger (and disable continuous)")
            ctrl = dai.CameraControl()
            ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.AUTO)
            ctrl.setAutoFocusTrigger()
            controlQueue.send(ctrl)
        elif key in [ord('w'), ord('a'), ord('s'), ord('d')]:
            if key == ord('a'):
                cropX = cropX - (maxCropX / camRgb.getResolutionWidth()) * STEP_SIZE
                if cropX < 0: cropX = maxCropX
            elif key == ord('d'):
                cropX = cropX + (maxCropX / camRgb.getResolutionWidth()) * STEP_SIZE
                if cropX > maxCropX: cropX = 0
            elif key == ord('w'):
                cropY = cropY - (maxCropY / camRgb.getResolutionHeight()) * STEP_SIZE
                if cropY < 0: cropY = maxCropY
            elif key == ord('s'):
                cropY = cropY + (maxCropY / camRgb.getResolutionHeight()) * STEP_SIZE
                if cropY > maxCropY: cropY = 0
            sendCamConfig = True

              all the best~~

              • erik replied to this.

                Hello king ,
                If the object is always 150mm away from the camera, I would suggest manually specifying the lens position instead of having autofocus enabled. You can achieve that like this:

                camRgb = pipeline.create(dai.node.ColorCamera)
cam.initialControl.setManualFocus(90)

                You can change the value between 0 and 255. You can see what's the best value by going through all focus values with this example (pressing , and . will change lens position).

                Thanks, Erik

                  Dear Erik:

                  Thanks for your feedback for autofocus function~we are trying to this parameter to check if it could be solved .

                  all the best~~

                  2 years later

                  Hello dear,
                  New on Luxonis and Depthai, my first project is based on OAK-D POE camera and OCR.
                  I am actually playing with experimental GEN 2 OCR, which is working like a charm.
                  But i am fighting with ROI management.
                  Actually the code is using 1024x1024 and preview 256x256 on a 12Mp camera.

                  I am trying to integrate a crop to aim on a dedicated zone of 1024x1024.
                  I am in a working area where i don't want to see people ( they dont want to be seenā€¦ lol)

                  I tried many imagemanip tests without reaching a working result.
                  and i just start in that domain so it is not easy.

                  Is there a way to modify easily the ccode to do 2048x1024?
                  And add a ROI?
                  i already saw some limitations, i can't do 2048x2048x3 but 2048x1024x3 should work
                  After that it is more the relocating which is a pain because it is not a x4 on all axisā€¦

                  If someone did some improvments on it then don't hesitate to share.

                  Thank you

                  Hi @DavidP ,

                  I tried many imagemanip tests without reaching a working result.

                  Could you share some minimal example, so we can look into it?

                    erik
                    Hello Erik, yes of course i prepare that.

                    To explain more before code, the project is outside, near a train cleaning station.
                    And we want to read the train number when it arrives for a wash.
                    But i am parallel of the rails, i just need the bottom of the image but as large as i can to be sure that i will read the number.
                    Unfortunately i can't post pictures of the camera here, but you can imagine more the subject like that.

                    I prepare some code explanations to complete.

                    Thank you

                      This is the screenshot of my OAK-D CM4 POE cam.
                      I want to crop the original image taken from the camera to keep the bottom of the image

                      this is from the original code :
                      pipeline = dai.Pipeline()

                      #version = "2021.2"

                      #pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_2)

                      version = "2022.1"

                      pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2022_1)

                      colorCam = pipeline.create(dai.node.ColorCamera)

                      colorCam.setPreviewSize(256, 256)

                      colorCam.setVideoSize(1024, 1024) # 4 times larger in both axis

                      colorCam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)

                      colorCam.setInterleaved(False)

                      colorCam.setBoardSocket(dai.CameraBoardSocket.RGB)

                      colorCam.setFps(10)

                      controlIn = pipeline.create(dai.node.XLinkIn)

                      controlIn.setStreamName('control')

                      controlIn.out.link(colorCam.inputControl)

                      cam_xout = pipeline.create(dai.node.XLinkOut)

                      cam_xout.setStreamName('video')

                      colorCam.video.link(cam_xout.input)

                      # ---------------------------------------

                      # 1st stage NN - text-detection

                      # ---------------------------------------

                      nn = pipeline.create(dai.node.NeuralNetwork)

                      nn.setBlobPath(blobconverter.from_zoo(name="east_text_detection_256x256",zoo_type="depthai",shaves=6, version=version))

                      colorCam.preview.link(nn.input)

                      nn_xout = pipeline.create(dai.node.XLinkOut)

                      nn_xout.setStreamName('detections')

                      nn.out.link(nn_xout.input)

                      # ---------------------------------------

                      # 2nd stage NN - text-recognition-0012

                      # ---------------------------------------

                      manip = pipeline.create(dai.node.ImageManip)

                      manip.setWaitForConfigInput(True)

                      manip_img = pipeline.create(dai.node.XLinkIn)

                      manip_img.setStreamName('manip_img')

                      manip_img.out.link(manip.inputImage)

                      manip_cfg = pipeline.create(dai.node.XLinkIn)

                      manip_cfg.setStreamName('manip_cfg')

                      manip_cfg.out.link(manip.inputConfig)

                      manip_xout = pipeline.create(dai.node.XLinkOut)

                      manip_xout.setStreamName('manip_out')

                      nn2 = pipeline.create(dai.node.NeuralNetwork)

                      nn2.setBlobPath(blobconverter.from_zoo(name="text-recognition-0012", shaves=6, version=version))

                      nn2.setNumInferenceThreads(2)

                      manip.out.link(nn2.input)

                      manip.out.link(manip_xout.input)

                      nn2_xout = pipeline.create(dai.node.XLinkOut)

                      nn2_xout.setStreamName("recognitions")

                      nn2.out.link(nn2_xout.input)

                      So i am trying to adapt with 2 image manipulators to link to another ROI :

                      pipeline = dai.Pipeline()

                      #version = "2021.2"

                      #pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_2)

                      version = "2022.1"

                      pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2022_1)

                      colorCam = pipeline.create(dai.node.ColorCamera)

                      colorCam.setPreviewSize(256, 256)

                      colorCam.setVideoSize(2048, 2048) # 4 times larger in both axis

                      colorCam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_12_MP)

                      colorCam.setInterleaved(False)

                      colorCam.setBoardSocket(dai.CameraBoardSocket.RGB)

                      colorCam.setFps(10)

                      #MODIFICATION -> creation of the main video Imagemanip

                      manip_video = pipeline.create(dai.node.ImageManip)

                      RrVideo = dai.RotatedRect()

                      RrVideo.center.x,RrVideo.center.y = 0,1024

                      RrVideo.size.width,RrVideo.size.height = 1024,1024

                      manip_video.initialConfig.setCropRotatedRect(RrVideo,False)

                      manip_video.setMaxOutputFrameSize(1024*1024*3)

                      colorCam.video.link(manip_video.inputImage)

                      #MODIFICATION -> creation of the preview video Imagemanip

                      manip_preview= pipeline.create(dai.node.ImageManip)

                      manip_preview.setResize(256,256)

                      manip_preview.setMaxOutputFrameSize(256*256*3)

                      manip_video.out.link(manip_preview.inputImage)

                      controlIn = pipeline.create(dai.node.XLinkIn)

                      controlIn.setStreamName('control')

                      controlIn.out.link(colorCam.inputControl)

                      cam_xout = pipeline.create(dai.node.XLinkOut)

                      cam_xout.setStreamName('video')

                      #MODIFICATION -> link to manip_video output

                      #colorCam.video.link(cam_xout.input)

                      manip_video.out.link(cam_xout.input)

                      # ---------------------------------------

                      # 1st stage NN - text-detection

                      # ---------------------------------------

                      nn = pipeline.create(dai.node.NeuralNetwork)

                      nn.setBlobPath(blobconverter.from_zoo(name="east_text_detection_256x256",zoo_type="depthai",shaves=6, version=version))

                      #MODIFICATION -> link the preview to text detection

                      #colorCam.preview.link(nn.input)

                      manip_preview.out.link(nn.input)

                      nn_xout = pipeline.create(dai.node.XLinkOut)

                      nn_xout.setStreamName('detections')

                      nn.out.link(nn_xout.input)

                      # ---------------------------------------

                      # 2nd stage NN - text-recognition-0012

                      # ---------------------------------------

                      manip = pipeline.create(dai.node.ImageManip)

                      manip.setWaitForConfigInput(True)

                      manip_img = pipeline.create(dai.node.XLinkIn)

                      manip_img.setStreamName('manip_img')

                      manip_img.out.link(manip.inputImage)

                      manip_cfg = pipeline.create(dai.node.XLinkIn)

                      manip_cfg.setStreamName('manip_cfg')

                      manip_cfg.out.link(manip.inputConfig)

                      manip_xout = pipeline.create(dai.node.XLinkOut)

                      manip_xout.setStreamName('manip_out')

                      nn2 = pipeline.create(dai.node.NeuralNetwork)

                      nn2.setBlobPath(blobconverter.from_zoo(name="text-recognition-0012", shaves=6, version=version))

                      nn2.setNumInferenceThreads(2)

                      manip.out.link(nn2.input)

                      manip.out.link(manip_xout.input)

                      nn2_xout = pipeline.create(dai.node.XLinkOut)

                      nn2_xout.setStreamName("recognitions")

                      nn2.out.link(nn2_xout.input)

                      But as i said, i just start from scratch on DepthAI, then i possibly missed some fundamental partsā€¦.

                      Regards