#!/usr/bin/env python3 from pathlib import Path import cv2 import numpy as np import depthai as dai import east import blobconverter class HostSeqSync: def __init__(self): self.imfFrames = [] def add_msg(self, msg): self.imfFrames.append(msg) def get_msg(self, target_seq): for i, imgFrame in enumerate(self.imfFrames): if target_seq == imgFrame.getSequenceNum(): self.imfFrames = self.imfFrames[i:] break return self.imfFrames[0] pipeline = dai.Pipeline() version = "2021.2" pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_2) 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) def to_tensor_result(packet): return { name: np.array(packet.getLayerFp16(name)) for name in [tensor.name for tensor in packet.getRaw().tensors] } def to_planar(frame): return frame.transpose(2, 0, 1).flatten() with dai.Device(pipeline) as device: q_vid = device.getOutputQueue("video", 4, blocking=False) # This should be set to block, but would get to some extreme queuing/latency! q_det = device.getOutputQueue("detections", 4, blocking=False) q_rec = device.getOutputQueue("recognitions", 4, blocking=True) q_manip_img = device.getInputQueue("manip_img") q_manip_cfg = device.getInputQueue("manip_cfg") q_manip_out = device.getOutputQueue("manip_out", 4, blocking=False) controlQueue = device.getInputQueue('control') frame = None cropped_stacked = None rotated_rectangles = [] rec_pushed = 0 rec_received = 0 host_sync = HostSeqSync() class CTCCodec(object): """ Convert between text-label and text-index """ def __init__(self, characters): # characters (str): set of the possible characters. dict_character = list(characters) self.dict = {} for i, char in enumerate(dict_character): self.dict[char] = i + 1 self.characters = dict_character #print(self.characters) #input() def decode(self, preds): """ convert text-index into text-label. """ texts = [] index = 0 # Select max probabilty (greedy decoding) then decode index to character preds = preds.astype(np.float16) preds_index = np.argmax(preds, 2) preds_index = preds_index.transpose(1, 0) preds_index_reshape = preds_index.reshape(-1) preds_sizes = np.array([preds_index.shape[1]] * preds_index.shape[0]) for l in preds_sizes: t = preds_index_reshape[index:index + l] # NOTE: t might be zero size if t.shape[0] == 0: continue char_list = [] for i in range(l): # removing repeated characters and blank. if not (i > 0 and t[i - 1] == t[i]): if self.characters[t[i]] != '#': char_list.append(self.characters[t[i]]) text = ''.join(char_list) texts.append(text) index += l return texts characters = '0123456789abcdefghijklmnopqrstuvwxyz#' codec = CTCCodec(characters) ctrl = dai.CameraControl() ctrl.setAutoFocusMode(dai.CameraControl.AutoFocusMode.CONTINUOUS_VIDEO) ctrl.setAutoFocusTrigger() controlQueue.send(ctrl) while True: vid_in = q_vid.tryGet() if vid_in is not None: host_sync.add_msg(vid_in) # Multiple recognition results may be available, read until queue is empty while True: in_rec = q_rec.tryGet() if in_rec is None: break rec_data = bboxes = np.array(in_rec.getFirstLayerFp16()).reshape(30,1,37) decoded_text = codec.decode(rec_data)[0] pos = rotated_rectangles[rec_received] print("{:2}: {:20}".format(rec_received, decoded_text), "center({:3},{:3}) size({:3},{:3}) angle{:5.1f} deg".format( int(pos[0][0]), int(pos[0][1]), pos[1][0], pos[1][1], pos[2])) # Draw the text on the right side of 'cropped_stacked' - placeholder if cropped_stacked is not None: cv2.putText(cropped_stacked, decoded_text, (120 + 10 , 32 * rec_received + 24), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,0), 2) cv2.imshow('cropped_stacked', cropped_stacked) rec_received += 1 if cv2.waitKey(1) == ord('q'): break if rec_received >= rec_pushed: in_det = q_det.tryGet() if in_det is not None: frame = host_sync.get_msg(in_det.getSequenceNum()).getCvFrame().copy() scores, geom1, geom2 = to_tensor_result(in_det).values() scores = np.reshape(scores, (1, 1, 64, 64)) geom1 = np.reshape(geom1, (1, 4, 64, 64)) geom2 = np.reshape(geom2, (1, 1, 64, 64)) bboxes, confs, angles = east.decode_predictions(scores, geom1, geom2) boxes, angles = east.non_max_suppression(np.array(bboxes), probs=confs, angles=np.array(angles)) rotated_rectangles = [ east.get_cv_rotated_rect(bbox, angle * -1) for (bbox, angle) in zip(boxes, angles) ] rec_received = 0 rec_pushed = len(rotated_rectangles) if rec_pushed: print("====== Pushing for recognition, count:", rec_pushed) cropped_stacked = None for idx, rotated_rect in enumerate(rotated_rectangles): # Detections are done on 256x256 frames, we are sending back 1024x1024 # That's why we multiply center and size values by 4 rotated_rect[0][0] = rotated_rect[0][0] * 4 rotated_rect[0][1] = rotated_rect[0][1] * 4 rotated_rect[1][0] = rotated_rect[1][0] * 4 rotated_rect[1][1] = rotated_rect[1][1] * 4 # Draw detection crop area on input frame points = np.int0(cv2.boxPoints(rotated_rect)) print(rotated_rect) cv2.polylines(frame, [points], isClosed=True, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_8) # TODO make it work taking args like in OpenCV: # rr = ((256, 256), (128, 64), 30) rr = dai.RotatedRect() rr.center.x = rotated_rect[0][0] rr.center.y = rotated_rect[0][1] rr.size.width = rotated_rect[1][0] rr.size.height = rotated_rect[1][1] rr.angle = rotated_rect[2] cfg = dai.ImageManipConfig() cfg.setCropRotatedRect(rr, False) cfg.setResize(120, 32) # Send frame and config to device if idx == 0: w,h,c = frame.shape imgFrame = dai.ImgFrame() imgFrame.setData(to_planar(frame)) imgFrame.setType(dai.ImgFrame.Type.BGR888p) imgFrame.setWidth(w) imgFrame.setHeight(h) q_manip_img.send(imgFrame) else: cfg.setReusePreviousImage(True) q_manip_cfg.send(cfg) # Get manipulated image from the device transformed = q_manip_out.get().getCvFrame() rec_placeholder_img = np.zeros((32, 200, 3), np.uint8) transformed = np.hstack((transformed, rec_placeholder_img)) if cropped_stacked is None: cropped_stacked = transformed else: cropped_stacked = np.vstack((cropped_stacked, transformed)) if cropped_stacked is not None: cv2.imshow('cropped_stacked', cropped_stacked) if frame is not None: cv2.imshow('frame', frame) 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)