├── .gitignore ├── 1-hello-world ├── hello_world.py ├── hello_world_docs.py └── requirements.txt ├── 2-face-detection-retail ├── face-detection-retail-0004.bin ├── face-detection-retail-0004.blob ├── face-detection-retail-0004.py ├── face-detection-retail-0004.xml └── requirements.txt └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | cover/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | db.sqlite3-journal 64 | 65 | # Flask stuff: 66 | instance/ 67 | .webassets-cache 68 | 69 | # Scrapy stuff: 70 | .scrapy 71 | 72 | # Sphinx documentation 73 | docs/_build/ 74 | 75 | # PyBuilder 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 98 | __pypackages__/ 99 | 100 | # Celery stuff 101 | celerybeat-schedule 102 | celerybeat.pid 103 | 104 | # SageMath parsed files 105 | *.sage.py 106 | 107 | # Environments 108 | .env 109 | .venv 110 | env/ 111 | venv/ 112 | ENV/ 113 | env.bak/ 114 | venv.bak/ 115 | 116 | # Spyder project settings 117 | .spyderproject 118 | .spyproject 119 | 120 | # Rope project settings 121 | .ropeproject 122 | 123 | # mkdocs documentation 124 | /site 125 | 126 | # mypy 127 | .mypy_cache/ 128 | .dmypy.json 129 | dmypy.json 130 | 131 | # Pyre type checker 132 | .pyre/ 133 | 134 | # pytype static type analyzer 135 | .pytype/ 136 | 137 | # General 138 | .DS_Store 139 | -------------------------------------------------------------------------------- /1-hello-world/hello_world.py: -------------------------------------------------------------------------------- 1 | # first, import all necessary modules 2 | from pathlib import Path 3 | 4 | import blobconverter 5 | import cv2 6 | import depthai 7 | import numpy as np 8 | 9 | # Pipeline tells DepthAI what operations to perform when running - you define all of the resources used and flows here 10 | pipeline = depthai.Pipeline() 11 | 12 | # First, we want the Color camera as the output 13 | cam_rgb = pipeline.createColorCamera() 14 | cam_rgb.setPreviewSize(300, 300) # 300x300 will be the preview frame size, available as 'preview' output of the node 15 | cam_rgb.setInterleaved(False) 16 | 17 | # Next, we want a neural network that will produce the detections 18 | detection_nn = pipeline.createMobileNetDetectionNetwork() 19 | # Blob is the Neural Network file, compiled for MyriadX. It contains both the definition and weights of the model 20 | # We're using a blobconverter tool to retrieve the MobileNetSSD blob automatically from OpenVINO Model Zoo 21 | detection_nn.setBlobPath(blobconverter.from_zoo(name='mobilenet-ssd', shaves=6)) 22 | # Next, we filter out the detections that are below a confidence threshold. Confidence can be anywhere between <0..1> 23 | detection_nn.setConfidenceThreshold(0.5) 24 | # Next, we link the camera 'preview' output to the neural network detection input, so that it can produce detections 25 | cam_rgb.preview.link(detection_nn.input) 26 | 27 | # XLinkOut is a "way out" from the device. Any data you want to transfer to host need to be send via XLink 28 | xout_rgb = pipeline.createXLinkOut() 29 | # For the rgb camera output, we want the XLink stream to be named "rgb" 30 | xout_rgb.setStreamName("rgb") 31 | # Linking camera preview to XLink input, so that the frames will be sent to host 32 | cam_rgb.preview.link(xout_rgb.input) 33 | 34 | # The same XLinkOut mechanism will be used to receive nn results 35 | xout_nn = pipeline.createXLinkOut() 36 | xout_nn.setStreamName("nn") 37 | detection_nn.out.link(xout_nn.input) 38 | 39 | # Pipeline is now finished, and we need to find an available device to run our pipeline 40 | # we are using context manager here that will dispose the device after we stop using it 41 | with depthai.Device(pipeline) as device: 42 | # From this point, the Device will be in "running" mode and will start sending data via XLink 43 | 44 | # To consume the device results, we get two output queues from the device, with stream names we assigned earlier 45 | q_rgb = device.getOutputQueue("rgb") 46 | q_nn = device.getOutputQueue("nn") 47 | 48 | # Here, some of the default values are defined. Frame will be an image from "rgb" stream, detections will contain nn results 49 | frame = None 50 | detections = [] 51 | 52 | # Since the detections returned by nn have values from <0..1> range, they need to be multiplied by frame width/height to 53 | # receive the actual position of the bounding box on the image 54 | def frameNorm(frame, bbox): 55 | normVals = np.full(len(bbox), frame.shape[0]) 56 | normVals[::2] = frame.shape[1] 57 | return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) 58 | 59 | 60 | # Main host-side application loop 61 | while True: 62 | # we try to fetch the data from nn/rgb queues. tryGet will return either the data packet or None if there isn't any 63 | in_rgb = q_rgb.tryGet() 64 | in_nn = q_nn.tryGet() 65 | 66 | if in_rgb is not None: 67 | # If the packet from RGB camera is present, we're retrieving the frame in OpenCV format using getCvFrame 68 | frame = in_rgb.getCvFrame() 69 | 70 | if in_nn is not None: 71 | # when data from nn is received, we take the detections array that contains mobilenet-ssd results 72 | detections = in_nn.detections 73 | 74 | if frame is not None: 75 | for detection in detections: 76 | # for each bounding box, we first normalize it to match the frame size 77 | bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) 78 | # and then draw a rectangle on the frame to show the actual result 79 | cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2) 80 | # After all the drawing is finished, we show the frame on the screen 81 | cv2.imshow("preview", frame) 82 | 83 | # at any time, you can press "q" and exit the main loop, therefore exiting the program itself 84 | if cv2.waitKey(1) == ord('q'): 85 | break 86 | -------------------------------------------------------------------------------- /1-hello-world/hello_world_docs.py: -------------------------------------------------------------------------------- 1 | # first, import all necessary modules 2 | from pathlib import Path 3 | 4 | import blobconverter 5 | import cv2 6 | import depthai 7 | import numpy as np 8 | 9 | 10 | # [pipeline] 11 | pipeline = depthai.Pipeline() 12 | # [/pipeline] 13 | 14 | # [sources] 15 | # First, we want the Color camera as the output 16 | cam_rgb = pipeline.createColorCamera() 17 | cam_rgb.setPreviewSize(300, 300) # 300x300 will be the preview frame size, available as 'preview' output of the node 18 | cam_rgb.setInterleaved(False) 19 | # [/sources] 20 | 21 | # [detection network] 22 | detection_nn = pipeline.createMobileNetDetectionNetwork() 23 | # Blob is the Neural Network file, compiled for MyriadX. It contains both the definition and weights of the model 24 | # We're using a blobconverter tool to retreive the MobileNetSSD blob automatically from OpenVINO Model Zoo 25 | detection_nn.setBlobPath(blobconverter.from_zoo(name='mobilenet-ssd', shaves=6)) 26 | # Next, we filter out the detections that are below a confidence threshold. Confidence can be anywhere between <0..1> 27 | detection_nn.setConfidenceThreshold(0.5) 28 | # [/detection network] 29 | 30 | # [sinks] 31 | # XLinkOut is a "way out" from the device. Any data you want to transfer to host need to be send via XLink 32 | xout_rgb = pipeline.createXLinkOut() 33 | xout_rgb.setStreamName("rgb") 34 | 35 | xout_nn = pipeline.createXLinkOut() 36 | xout_nn.setStreamName("nn") 37 | # [/sinks] 38 | 39 | # [linking] 40 | cam_rgb.preview.link(xout_rgb.input) 41 | cam_rgb.preview.link(detection_nn.input) 42 | detection_nn.out.link(xout_nn.input) 43 | # [/linking] 44 | 45 | # Pipeline is now finished, and we need to find an available device to run our pipeline 46 | # we are using context manager here that will dispose the device after we stop using it 47 | # [initialization] 48 | with depthai.Device(pipeline) as device: 49 | # [/initialization] 50 | # From this point, the Device will be in "running" mode and will start sending data via XLink 51 | 52 | # [queue init] 53 | # To consume the device results, we get two output queues from the device, with stream names we assigned earlier 54 | q_rgb = device.getOutputQueue("rgb") 55 | q_nn = device.getOutputQueue("nn") 56 | # [/queue init] 57 | # Here, some of the default values are defined. Frame will be an image from "rgb" stream, detections will contain nn results 58 | frame = None 59 | detections = [] 60 | 61 | # Since the detections returned by nn have values from <0..1> range, they need to be multiplied by frame width/height to 62 | # receive the actual position of the bounding box on the image 63 | def frameNorm(frame, bbox): 64 | normVals = np.full(len(bbox), frame.shape[0]) 65 | normVals[::2] = frame.shape[1] 66 | return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) 67 | 68 | 69 | # [reading queues] 70 | while True: 71 | # we try to fetch the data from nn/rgb queues. tryGet will return either the data packet or None if there isn't any 72 | in_rgb = q_rgb.tryGet() 73 | in_nn = q_nn.tryGet() 74 | 75 | if in_rgb is not None: 76 | # If the packet from RGB camera is present, we're retrieving the frame in OpenCV format using getCvFrame 77 | frame = in_rgb.getCvFrame() 78 | 79 | if in_nn is not None: 80 | # when data from nn is received, we take the detections array that contains mobilenet-ssd results 81 | detections = in_nn.detections 82 | 83 | # [/reading queues] 84 | 85 | # [drawing] 86 | if frame is not None: 87 | for detection in detections: 88 | # for each bounding box, we first normalize it to match the frame size 89 | bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) 90 | # and then draw a rectangle on the frame to show the actual result 91 | cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2) 92 | # After all the drawing is finished, we show the frame on the screen 93 | cv2.imshow("preview", frame) 94 | 95 | # at any time, you can press "q" and exit the main loop, therefore exiting the program itself 96 | if cv2.waitKey(1) == ord('q'): 97 | break 98 | # [/drawing] 99 | 100 | 101 | -------------------------------------------------------------------------------- /1-hello-world/requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python==4.5.1.48; platform_machine != "aarch64" 2 | depthai==2.11.1.1 3 | blobconverter==1.2.6 4 | -------------------------------------------------------------------------------- /2-face-detection-retail/face-detection-retail-0004.bin: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luxonis/depthai-tutorials/ea452f061211b661bedf9363d48b5728d796716f/2-face-detection-retail/face-detection-retail-0004.bin -------------------------------------------------------------------------------- /2-face-detection-retail/face-detection-retail-0004.blob: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/luxonis/depthai-tutorials/ea452f061211b661bedf9363d48b5728d796716f/2-face-detection-retail/face-detection-retail-0004.blob -------------------------------------------------------------------------------- /2-face-detection-retail/face-detection-retail-0004.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | import cv2 3 | import depthai 4 | import numpy as np 5 | 6 | pipeline = depthai.Pipeline() 7 | 8 | cam_rgb = pipeline.createColorCamera() 9 | cam_rgb.setPreviewSize(300, 300) 10 | cam_rgb.setInterleaved(False) 11 | 12 | detection_nn = pipeline.createMobileNetDetectionNetwork() 13 | detection_nn.setBlobPath(str((Path(__file__).parent / Path('face-detection-retail-0004.blob')).resolve().absolute())) 14 | detection_nn.setConfidenceThreshold(0.5) 15 | cam_rgb.preview.link(detection_nn.input) 16 | 17 | xout_rgb = pipeline.createXLinkOut() 18 | xout_rgb.setStreamName("rgb") 19 | cam_rgb.preview.link(xout_rgb.input) 20 | 21 | xout_nn = pipeline.createXLinkOut() 22 | xout_nn.setStreamName("nn") 23 | detection_nn.out.link(xout_nn.input) 24 | 25 | # Pipeline is now finished, and we need to find an available device to run our pipeline 26 | # we are using context manager here that will dispose the device after we stop using it 27 | with depthai.Device(pipeline) as device: 28 | q_rgb = device.getOutputQueue("rgb") 29 | q_nn = device.getOutputQueue("nn") 30 | 31 | frame = None 32 | detections = [] 33 | 34 | 35 | def frameNorm(frame, bbox): 36 | normVals = np.full(len(bbox), frame.shape[0]) 37 | normVals[::2] = frame.shape[1] 38 | return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int) 39 | 40 | 41 | while True: 42 | in_rgb = q_rgb.tryGet() 43 | in_nn = q_nn.tryGet() 44 | 45 | if in_rgb is not None: 46 | frame = in_rgb.getCvFrame() 47 | 48 | if in_nn is not None: 49 | detections = in_nn.detections 50 | 51 | if frame is not None: 52 | for detection in detections: 53 | bbox = frameNorm(frame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax)) 54 | cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2) 55 | cv2.imshow("preview", frame) 56 | 57 | if cv2.waitKey(1) == ord('q'): 58 | break 59 | -------------------------------------------------------------------------------- /2-face-detection-retail/face-detection-retail-0004.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 1 9 | 3 10 | 300 11 | 300 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 1 20 | 3 21 | 1 22 | 1 23 | 24 | 25 | 26 | 27 | 28 | 29 | 1 30 | 3 31 | 300 32 | 300 33 | 34 | 35 | 1 36 | 3 37 | 1 38 | 1 39 | 40 | 41 | 42 | 43 | 1 44 | 3 45 | 300 46 | 300 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 1 55 | 3 56 | 1 57 | 1 58 | 59 | 60 | 61 | 62 | 63 | 64 | 1 65 | 3 66 | 300 67 | 300 68 | 69 | 70 | 1 71 | 3 72 | 1 73 | 1 74 | 75 | 76 | 77 | 78 | 1 79 | 3 80 | 300 81 | 300 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 32 90 | 3 91 | 7 92 | 7 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 1 101 | 3 102 | 300 103 | 300 104 | 105 | 106 | 32 107 | 3 108 | 7 109 | 7 110 | 111 | 112 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1201 | 1 1202 | 1203 | 1204 | 1205 | 1206 | 1207 | 1208 | 1 1209 | 64 1210 | 38 1211 | 38 1212 | 1213 | 1214 | 1 1215 | 64 1216 | 1 1217 | 1 1218 | 1219 | 1220 | 1221 | 1222 | 1 1223 | 64 1224 | 38 1225 | 38 1226 | 1227 | 1228 | 1229 | 1230 | 1231 | 1232 | 1 1233 | 64 1234 | 38 1235 | 38 1236 | 1237 | 1238 | 1239 | 1240 | 1 1241 | 64 1242 | 38 1243 | 38 1244 | 1245 | 1246 | 1247 | 1248 | 1249 | 1250 | 1251 | 64 1252 | 16 1253 | 1 1254 | 1 1255 | 1256 | 1257 | 1258 | 1259 | 1260 | 1261 | 1262 | 1 1263 | 16 1264 | 38 1265 | 38 1266 | 1267 | 1268 | 64 1269 | 16 1270 | 1 1271 | 1 1272 | 1273 | 1274 | 1275 | 1276 | 1 1277 | 64 1278 | 38 1279 | 38 1280 | 1281 | 1282 | 1283 | 1284 | 1285 | 1286 | 1287 | 1 1288 | 64 1289 | 1 1290 | 1 1291 | 1292 | 1293 | 1294 | 1295 | 1296 | 1297 | 1 1298 | 64 1299 | 38 1300 | 38 1301 | 1302 | 1303 | 1 1304 | 64 1305 | 1 1306 | 1 1307 | 1308 | 1309 | 1310 | 1311 | 1 1312 | 64 1313 | 38 1314 | 38 1315 | 1316 | 1317 | 1318 | 1319 | 1320 | 1321 | 1 1322 | 64 1323 | 38 1324 | 38 1325 | 1326 | 1327 | 1328 | 1329 | 1 1330 | 64 1331 | 38 1332 | 38 1333 | 1334 | 1335 | 1336 | 1337 | 1338 | 1339 | 1340 | 1 1341 | 64 1342 | 38 1343 | 38 1344 | 1345 | 1346 | 1 1347 | 64 1348 | 38 1349 | 38 1350 | 1351 | 1352 | 1353 | 1354 | 1 1355 | 128 1356 | 38 1357 | 38 1358 | 1359 | 1360 | 1361 | 1362 | 1363 | 1364 | 1365 | 24 1366 | 128 1367 | 1 1368 | 1 1369 | 1370 | 1371 | 1372 | 1373 | 1374 | 1375 | 1376 | 1 1377 | 128 1378 | 38 1379 | 38 1380 | 1381 | 1382 | 24 1383 | 128 1384 | 1 1385 | 1 1386 | 1387 | 1388 | 1389 | 1390 | 1 1391 | 24 1392 | 38 1393 | 38 1394 | 1395 | 1396 | 1397 | 1398 | 1399 | 1400 | 1401 | 1 1402 | 24 1403 | 1 1404 | 1 1405 | 1406 | 1407 | 1408 | 1409 | 1410 | 1411 | 1 1412 | 24 1413 | 38 1414 | 38 1415 | 1416 | 1417 | 1 1418 | 24 1419 | 1 1420 | 1 1421 | 1422 | 1423 | 1424 | 1425 | 1 1426 | 24 1427 | 38 1428 | 38 1429 | 1430 | 1431 | 1432 | 1433 | 1434 | 1435 | 1 1436 | 24 1437 | 38 1438 | 38 1439 | 1440 | 1441 | 1442 | 1443 | 1 1444 | 24 1445 | 38 1446 | 38 1447 | 1448 | 1449 | 1450 | 1451 | 1452 | 1453 | 1454 | 96 1455 | 24 1456 | 3 1457 | 3 1458 | 1459 | 1460 | 1461 | 1462 | 1463 | 1464 | 1465 | 1 1466 | 24 1467 | 38 1468 | 38 1469 | 1470 | 1471 | 96 1472 | 24 1473 | 3 1474 | 3 1475 | 1476 | 1477 | 1478 | 1479 | 1 1480 | 96 1481 | 38 1482 | 38 1483 | 1484 | 1485 | 1486 | 1487 | 1488 | 1489 | 1490 | 1 1491 | 96 1492 | 1 1493 | 1 1494 | 1495 | 1496 | 1497 | 1498 | 1499 | 1500 | 1 1501 | 96 1502 | 38 1503 | 38 1504 | 1505 | 1506 | 1 1507 | 96 1508 | 1 1509 | 1 1510 | 1511 | 1512 | 1513 | 1514 | 1 1515 | 96 1516 | 38 1517 | 38 1518 | 1519 | 1520 | 1521 | 1522 | 1523 | 1524 | 1 1525 | 96 1526 | 38 1527 | 38 1528 | 1529 | 1530 | 1531 | 1532 | 1 1533 | 96 1534 | 38 1535 | 38 1536 | 1537 | 1538 | 1539 | 1540 | 1541 | 1542 | 1543 | 96 1544 | 24 1545 | 1 1546 | 1 1547 | 1548 | 1549 | 1550 | 1551 | 1552 | 1553 | 1554 | 1 1555 | 24 1556 | 38 1557 | 38 1558 | 1559 | 1560 | 96 1561 | 24 1562 | 1 1563 | 1 1564 | 1565 | 1566 | 1567 | 1568 | 1 1569 | 96 1570 | 38 1571 | 38 1572 | 1573 | 1574 | 1575 | 1576 | 1577 | 1578 | 1579 | 1 1580 | 96 1581 | 1 1582 | 1 1583 | 1584 | 1585 | 1586 | 1587 | 1588 | 1589 | 1 1590 | 96 1591 | 38 1592 | 38 1593 | 1594 | 1595 | 1 1596 | 96 1597 | 1 1598 | 1 1599 | 1600 | 1601 | 1602 | 1603 | 1 1604 | 96 1605 | 38 1606 | 38 1607 | 1608 | 1609 | 1610 | 1611 | 1612 | 1613 | 1 1614 | 96 1615 | 38 1616 | 38 1617 | 1618 | 1619 | 1620 | 1621 | 1 1622 | 96 1623 | 38 1624 | 38 1625 | 1626 | 1627 | 1628 | 1629 | 1630 | 1631 | 1632 | 1 1633 | 96 1634 | 38 1635 | 38 1636 | 1637 | 1638 | 1 1639 | 96 1640 | 38 1641 | 38 1642 | 1643 | 1644 | 1645 | 1646 | 1 1647 | 192 1648 | 38 1649 | 38 1650 | 1651 | 1652 | 1653 | 1654 | 1655 | 1656 | 1657 | 24 1658 | 192 1659 | 1 1660 | 1 1661 | 1662 | 1663 | 1664 | 1665 | 1666 | 1667 | 1668 | 1 1669 | 192 1670 | 38 1671 | 38 1672 | 1673 | 1674 | 24 1675 | 192 1676 | 1 1677 | 1 1678 | 1679 | 1680 | 1681 | 1682 | 1 1683 | 24 1684 | 38 1685 | 38 1686 | 1687 | 1688 | 1689 | 1690 | 1691 | 1692 | 1693 | 1 1694 | 24 1695 | 1 1696 | 1 1697 | 1698 | 1699 | 1700 | 1701 | 1702 | 1703 | 1 1704 | 24 1705 | 38 1706 | 38 1707 | 1708 | 1709 | 1 1710 | 24 1711 | 1 1712 | 1 1713 | 1714 | 1715 | 1716 | 1717 | 1 1718 | 24 1719 | 38 1720 | 38 1721 | 1722 | 1723 | 1724 | 1725 | 1726 | 1727 | 1 1728 | 24 1729 | 38 1730 | 38 1731 | 1732 | 1733 | 1734 | 1735 | 1 1736 | 24 1737 | 38 1738 | 38 1739 | 1740 | 1741 | 1742 | 1743 | 1744 | 1745 | 1746 | 96 1747 | 24 1748 | 3 1749 | 3 1750 | 1751 | 1752 | 1753 | 1754 | 1755 | 1756 | 1757 | 1 1758 | 24 1759 | 38 1760 | 38 1761 | 1762 | 1763 | 96 1764 | 24 1765 | 3 1766 | 3 1767 | 1768 | 1769 | 1770 | 1771 | 1 1772 | 96 1773 | 38 1774 | 38 1775 | 1776 | 1777 | 1778 | 1779 | 1780 | 1781 | 1782 | 1 1783 | 96 1784 | 1 1785 | 1 1786 | 1787 | 1788 | 1789 | 1790 | 1791 | 1792 | 1 1793 | 96 1794 | 38 1795 | 38 1796 | 1797 | 1798 | 1 1799 | 96 1800 | 1 1801 | 1 1802 | 1803 | 1804 | 1805 | 1806 | 1 1807 | 96 1808 | 38 1809 | 38 1810 | 1811 | 1812 | 1813 | 1814 | 1815 | 1816 | 1 1817 | 96 1818 | 38 1819 | 38 1820 | 1821 | 1822 | 1823 | 1824 | 1 1825 | 96 1826 | 38 1827 | 38 1828 | 1829 | 1830 | 1831 | 1832 | 1833 | 1834 | 1835 | 96 1836 | 24 1837 | 1 1838 | 1 1839 | 1840 | 1841 | 1842 | 1843 | 1844 | 1845 | 1846 | 1 1847 | 24 1848 | 38 1849 | 38 1850 | 1851 | 1852 | 96 1853 | 24 1854 | 1 1855 | 1 1856 | 1857 | 1858 | 1859 | 1860 | 1 1861 | 96 1862 | 38 1863 | 38 1864 | 1865 | 1866 | 1867 | 1868 | 1869 | 1870 | 1871 | 1 1872 | 96 1873 | 1 1874 | 1 1875 | 1876 | 1877 | 1878 | 1879 | 1880 | 1881 | 1 1882 | 96 1883 | 38 1884 | 38 1885 | 1886 | 1887 | 1 1888 | 96 1889 | 1 1890 | 1 1891 | 1892 | 1893 | 1894 | 1895 | 1 1896 | 96 1897 | 38 1898 | 38 1899 | 1900 | 1901 | 1902 | 1903 | 1904 | 1905 | 1 1906 | 96 1907 | 38 1908 | 38 1909 | 1910 | 1911 | 1912 | 1913 | 1 1914 | 96 1915 | 38 1916 | 38 1917 | 1918 | 1919 | 1920 | 1921 | 1922 | 1923 | 1924 | 1 1925 | 96 1926 | 38 1927 | 38 1928 | 1929 | 1930 | 1 1931 | 96 1932 | 38 1933 | 38 1934 | 1935 | 1936 | 1937 | 1938 | 1 1939 | 192 1940 | 38 1941 | 38 1942 | 1943 | 1944 | 1945 | 1946 | 1947 | 1948 | 1949 | 32 1950 | 192 1951 | 1 1952 | 1 1953 | 1954 | 1955 | 1956 | 1957 | 1958 | 1959 | 1960 | 1 1961 | 192 1962 | 38 1963 | 38 1964 | 1965 | 1966 | 32 1967 | 192 1968 | 1 1969 | 1 1970 | 1971 | 1972 | 1973 | 1974 | 1 1975 | 32 1976 | 38 1977 | 38 1978 | 1979 | 1980 | 1981 | 1982 | 1983 | 1984 | 1985 | 1 1986 | 32 1987 | 1 1988 | 1 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1 1996 | 32 1997 | 38 1998 | 38 1999 | 2000 | 2001 | 1 2002 | 32 2003 | 1 2004 | 1 2005 | 2006 | 2007 | 2008 | 2009 | 1 2010 | 32 2011 | 38 2012 | 38 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 1 2020 | 32 2021 | 38 2022 | 38 2023 | 2024 | 2025 | 2026 | 2027 | 1 2028 | 32 2029 | 38 2030 | 38 2031 | 2032 | 2033 | 2034 | 2035 | 2036 | 2037 | 2038 | 128 2039 | 32 2040 | 3 2041 | 3 2042 | 2043 | 2044 | 2045 | 2046 | 2047 | 2048 | 2049 | 1 2050 | 32 2051 | 38 2052 | 38 2053 | 2054 | 2055 | 128 2056 | 32 2057 | 3 2058 | 3 2059 | 2060 | 2061 | 2062 | 2063 | 1 2064 | 128 2065 | 38 2066 | 38 2067 | 2068 | 2069 | 2070 | 2071 | 2072 | 2073 | 2074 | 1 2075 | 128 2076 | 1 2077 | 1 2078 | 2079 | 2080 | 2081 | 2082 | 2083 | 2084 | 1 2085 | 128 2086 | 38 2087 | 38 2088 | 2089 | 2090 | 1 2091 | 128 2092 | 1 2093 | 1 2094 | 2095 | 2096 | 2097 | 2098 | 1 2099 | 128 2100 | 38 2101 | 38 2102 | 2103 | 2104 | 2105 | 2106 | 2107 | 2108 | 1 2109 | 128 2110 | 38 2111 | 38 2112 | 2113 | 2114 | 2115 | 2116 | 1 2117 | 128 2118 | 38 2119 | 38 2120 | 2121 | 2122 | 2123 | 2124 | 2125 | 2126 | 2127 | 128 2128 | 32 2129 | 1 2130 | 1 2131 | 2132 | 2133 | 2134 | 2135 | 2136 | 2137 | 2138 | 1 2139 | 32 2140 | 38 2141 | 38 2142 | 2143 | 2144 | 128 2145 | 32 2146 | 1 2147 | 1 2148 | 2149 | 2150 | 2151 | 2152 | 1 2153 | 128 2154 | 38 2155 | 38 2156 | 2157 | 2158 | 2159 | 2160 | 2161 | 2162 | 2163 | 1 2164 | 128 2165 | 1 2166 | 1 2167 | 2168 | 2169 | 2170 | 2171 | 2172 | 2173 | 1 2174 | 128 2175 | 38 2176 | 38 2177 | 2178 | 2179 | 1 2180 | 128 2181 | 1 2182 | 1 2183 | 2184 | 2185 | 2186 | 2187 | 1 2188 | 128 2189 | 38 2190 | 38 2191 | 2192 | 2193 | 2194 | 2195 | 2196 | 2197 | 1 2198 | 128 2199 | 38 2200 | 38 2201 | 2202 | 2203 | 2204 | 2205 | 1 2206 | 128 2207 | 38 2208 | 38 2209 | 2210 | 2211 | 2212 | 2213 | 2214 | 2215 | 2216 | 1 2217 | 128 2218 | 38 2219 | 38 2220 | 2221 | 2222 | 1 2223 | 128 2224 | 38 2225 | 38 2226 | 2227 | 2228 | 2229 | 2230 | 1 2231 | 256 2232 | 38 2233 | 38 2234 | 2235 | 2236 | 2237 | 2238 | 2239 | 2240 | 2241 | 32 2242 | 256 2243 | 1 2244 | 1 2245 | 2246 | 2247 | 2248 | 2249 | 2250 | 2251 | 2252 | 1 2253 | 256 2254 | 38 2255 | 38 2256 | 2257 | 2258 | 32 2259 | 256 2260 | 1 2261 | 1 2262 | 2263 | 2264 | 2265 | 2266 | 1 2267 | 32 2268 | 19 2269 | 19 2270 | 2271 | 2272 | 2273 | 2274 | 2275 | 2276 | 2277 | 1 2278 | 32 2279 | 1 2280 | 1 2281 | 2282 | 2283 | 2284 | 2285 | 2286 | 2287 | 1 2288 | 32 2289 | 19 2290 | 19 2291 | 2292 | 2293 | 1 2294 | 32 2295 | 1 2296 | 1 2297 | 2298 | 2299 | 2300 | 2301 | 1 2302 | 32 2303 | 19 2304 | 19 2305 | 2306 | 2307 | 2308 | 2309 | 2310 | 2311 | 1 2312 | 32 2313 | 19 2314 | 19 2315 | 2316 | 2317 | 2318 | 2319 | 1 2320 | 32 2321 | 19 2322 | 19 2323 | 2324 | 2325 | 2326 | 2327 | 2328 | 2329 | 2330 | 128 2331 | 32 2332 | 3 2333 | 3 2334 | 2335 | 2336 | 2337 | 2338 | 2339 | 2340 | 2341 | 1 2342 | 32 2343 | 19 2344 | 19 2345 | 2346 | 2347 | 128 2348 | 32 2349 | 3 2350 | 3 2351 | 2352 | 2353 | 2354 | 2355 | 1 2356 | 128 2357 | 19 2358 | 19 2359 | 2360 | 2361 | 2362 | 2363 | 2364 | 2365 | 2366 | 1 2367 | 128 2368 | 1 2369 | 1 2370 | 2371 | 2372 | 2373 | 2374 | 2375 | 2376 | 1 2377 | 128 2378 | 19 2379 | 19 2380 | 2381 | 2382 | 1 2383 | 128 2384 | 1 2385 | 1 2386 | 2387 | 2388 | 2389 | 2390 | 1 2391 | 128 2392 | 19 2393 | 19 2394 | 2395 | 2396 | 2397 | 2398 | 2399 | 2400 | 1 2401 | 128 2402 | 19 2403 | 19 2404 | 2405 | 2406 | 2407 | 2408 | 1 2409 | 128 2410 | 19 2411 | 19 2412 | 2413 | 2414 | 2415 | 2416 | 2417 | 2418 | 2419 | 128 2420 | 32 2421 | 1 2422 | 1 2423 | 2424 | 2425 | 2426 | 2427 | 2428 | 2429 | 2430 | 1 2431 | 32 2432 | 19 2433 | 19 2434 | 2435 | 2436 | 128 2437 | 32 2438 | 1 2439 | 1 2440 | 2441 | 2442 | 2443 | 2444 | 1 2445 | 128 2446 | 19 2447 | 19 2448 | 2449 | 2450 | 2451 | 2452 | 2453 | 2454 | 2455 | 1 2456 | 128 2457 | 1 2458 | 1 2459 | 2460 | 2461 | 2462 | 2463 | 2464 | 2465 | 1 2466 | 128 2467 | 19 2468 | 19 2469 | 2470 | 2471 | 1 2472 | 128 2473 | 1 2474 | 1 2475 | 2476 | 2477 | 2478 | 2479 | 1 2480 | 128 2481 | 19 2482 | 19 2483 | 2484 | 2485 | 2486 | 2487 | 2488 | 2489 | 1 2490 | 128 2491 | 19 2492 | 19 2493 | 2494 | 2495 | 2496 | 2497 | 1 2498 | 128 2499 | 19 2500 | 19 2501 | 2502 | 2503 | 2504 | 2505 | 2506 | 2507 | 2508 | 1 2509 | 128 2510 | 19 2511 | 19 2512 | 2513 | 2514 | 1 2515 | 128 2516 | 19 2517 | 19 2518 | 2519 | 2520 | 2521 | 2522 | 1 2523 | 256 2524 | 19 2525 | 19 2526 | 2527 | 2528 | 2529 | 2530 | 2531 | 2532 | 2533 | 48 2534 | 256 2535 | 1 2536 | 1 2537 | 2538 | 2539 | 2540 | 2541 | 2542 | 2543 | 2544 | 1 2545 | 256 2546 | 19 2547 | 19 2548 | 2549 | 2550 | 48 2551 | 256 2552 | 1 2553 | 1 2554 | 2555 | 2556 | 2557 | 2558 | 1 2559 | 48 2560 | 19 2561 | 19 2562 | 2563 | 2564 | 2565 | 2566 | 2567 | 2568 | 2569 | 1 2570 | 48 2571 | 1 2572 | 1 2573 | 2574 | 2575 | 2576 | 2577 | 2578 | 2579 | 1 2580 | 48 2581 | 19 2582 | 19 2583 | 2584 | 2585 | 1 2586 | 48 2587 | 1 2588 | 1 2589 | 2590 | 2591 | 2592 | 2593 | 1 2594 | 48 2595 | 19 2596 | 19 2597 | 2598 | 2599 | 2600 | 2601 | 2602 | 2603 | 1 2604 | 48 2605 | 19 2606 | 19 2607 | 2608 | 2609 | 2610 | 2611 | 1 2612 | 48 2613 | 19 2614 | 19 2615 | 2616 | 2617 | 2618 | 2619 | 2620 | 2621 | 2622 | 192 2623 | 48 2624 | 3 2625 | 3 2626 | 2627 | 2628 | 2629 | 2630 | 2631 | 2632 | 2633 | 1 2634 | 48 2635 | 19 2636 | 19 2637 | 2638 | 2639 | 192 2640 | 48 2641 | 3 2642 | 3 2643 | 2644 | 2645 | 2646 | 2647 | 1 2648 | 192 2649 | 19 2650 | 19 2651 | 2652 | 2653 | 2654 | 2655 | 2656 | 2657 | 2658 | 1 2659 | 192 2660 | 1 2661 | 1 2662 | 2663 | 2664 | 2665 | 2666 | 2667 | 2668 | 1 2669 | 192 2670 | 19 2671 | 19 2672 | 2673 | 2674 | 1 2675 | 192 2676 | 1 2677 | 1 2678 | 2679 | 2680 | 2681 | 2682 | 1 2683 | 192 2684 | 19 2685 | 19 2686 | 2687 | 2688 | 2689 | 2690 | 2691 | 2692 | 1 2693 | 192 2694 | 19 2695 | 19 2696 | 2697 | 2698 | 2699 | 2700 | 1 2701 | 192 2702 | 19 2703 | 19 2704 | 2705 | 2706 | 2707 | 2708 | 2709 | 2710 | 2711 | 192 2712 | 48 2713 | 1 2714 | 1 2715 | 2716 | 2717 | 2718 | 2719 | 2720 | 2721 | 2722 | 1 2723 | 48 2724 | 19 2725 | 19 2726 | 2727 | 2728 | 192 2729 | 48 2730 | 1 2731 | 1 2732 | 2733 | 2734 | 2735 | 2736 | 1 2737 | 192 2738 | 19 2739 | 19 2740 | 2741 | 2742 | 2743 | 2744 | 2745 | 2746 | 2747 | 1 2748 | 192 2749 | 1 2750 | 1 2751 | 2752 | 2753 | 2754 | 2755 | 2756 | 2757 | 1 2758 | 192 2759 | 19 2760 | 19 2761 | 2762 | 2763 | 1 2764 | 192 2765 | 1 2766 | 1 2767 | 2768 | 2769 | 2770 | 2771 | 1 2772 | 192 2773 | 19 2774 | 19 2775 | 2776 | 2777 | 2778 | 2779 | 2780 | 2781 | 1 2782 | 192 2783 | 19 2784 | 19 2785 | 2786 | 2787 | 2788 | 2789 | 1 2790 | 192 2791 | 19 2792 | 19 2793 | 2794 | 2795 | 2796 | 2797 | 2798 | 2799 | 2800 | 1 2801 | 192 2802 | 19 2803 | 19 2804 | 2805 | 2806 | 1 2807 | 192 2808 | 19 2809 | 19 2810 | 2811 | 2812 | 2813 | 2814 | 1 2815 | 384 2816 | 19 2817 | 19 2818 | 2819 | 2820 | 2821 | 2822 | 2823 | 2824 | 2825 | 48 2826 | 384 2827 | 1 2828 | 1 2829 | 2830 | 2831 | 2832 | 2833 | 2834 | 2835 | 2836 | 1 2837 | 384 2838 | 19 2839 | 19 2840 | 2841 | 2842 | 48 2843 | 384 2844 | 1 2845 | 1 2846 | 2847 | 2848 | 2849 | 2850 | 1 2851 | 48 2852 | 19 2853 | 19 2854 | 2855 | 2856 | 2857 | 2858 | 2859 | 2860 | 2861 | 1 2862 | 48 2863 | 1 2864 | 1 2865 | 2866 | 2867 | 2868 | 2869 | 2870 | 2871 | 1 2872 | 48 2873 | 19 2874 | 19 2875 | 2876 | 2877 | 1 2878 | 48 2879 | 1 2880 | 1 2881 | 2882 | 2883 | 2884 | 2885 | 1 2886 | 48 2887 | 19 2888 | 19 2889 | 2890 | 2891 | 2892 | 2893 | 2894 | 2895 | 1 2896 | 48 2897 | 19 2898 | 19 2899 | 2900 | 2901 | 2902 | 2903 | 1 2904 | 48 2905 | 19 2906 | 19 2907 | 2908 | 2909 | 2910 | 2911 | 2912 | 2913 | 2914 | 192 2915 | 48 2916 | 3 2917 | 3 2918 | 2919 | 2920 | 2921 | 2922 | 2923 | 2924 | 2925 | 1 2926 | 48 2927 | 19 2928 | 19 2929 | 2930 | 2931 | 192 2932 | 48 2933 | 3 2934 | 3 2935 | 2936 | 2937 | 2938 | 2939 | 1 2940 | 192 2941 | 19 2942 | 19 2943 | 2944 | 2945 | 2946 | 2947 | 2948 | 2949 | 2950 | 1 2951 | 192 2952 | 1 2953 | 1 2954 | 2955 | 2956 | 2957 | 2958 | 2959 | 2960 | 1 2961 | 192 2962 | 19 2963 | 19 2964 | 2965 | 2966 | 1 2967 | 192 2968 | 1 2969 | 1 2970 | 2971 | 2972 | 2973 | 2974 | 1 2975 | 192 2976 | 19 2977 | 19 2978 | 2979 | 2980 | 2981 | 2982 | 2983 | 2984 | 1 2985 | 192 2986 | 19 2987 | 19 2988 | 2989 | 2990 | 2991 | 2992 | 1 2993 | 192 2994 | 19 2995 | 19 2996 | 2997 | 2998 | 2999 | 3000 | 3001 | 3002 | 3003 | 192 3004 | 48 3005 | 1 3006 | 1 3007 | 3008 | 3009 | 3010 | 3011 | 3012 | 3013 | 3014 | 1 3015 | 48 3016 | 19 3017 | 19 3018 | 3019 | 3020 | 192 3021 | 48 3022 | 1 3023 | 1 3024 | 3025 | 3026 | 3027 | 3028 | 1 3029 | 192 3030 | 19 3031 | 19 3032 | 3033 | 3034 | 3035 | 3036 | 3037 | 3038 | 3039 | 1 3040 | 192 3041 | 1 3042 | 1 3043 | 3044 | 3045 | 3046 | 3047 | 3048 | 3049 | 1 3050 | 192 3051 | 19 3052 | 19 3053 | 3054 | 3055 | 1 3056 | 192 3057 | 1 3058 | 1 3059 | 3060 | 3061 | 3062 | 3063 | 1 3064 | 192 3065 | 19 3066 | 19 3067 | 3068 | 3069 | 3070 | 3071 | 3072 | 3073 | 1 3074 | 192 3075 | 19 3076 | 19 3077 | 3078 | 3079 | 3080 | 3081 | 1 3082 | 192 3083 | 19 3084 | 19 3085 | 3086 | 3087 | 3088 | 3089 | 3090 | 3091 | 3092 | 1 3093 | 192 3094 | 19 3095 | 19 3096 | 3097 | 3098 | 1 3099 | 192 3100 | 19 3101 | 19 3102 | 3103 | 3104 | 3105 | 3106 | 1 3107 | 384 3108 | 19 3109 | 19 3110 | 3111 | 3112 | 3113 | 3114 | 3115 | 3116 | 3117 | 36 3118 | 384 3119 | 3 3120 | 3 3121 | 3122 | 3123 | 3124 | 3125 | 3126 | 3127 | 3128 | 1 3129 | 384 3130 | 19 3131 | 19 3132 | 3133 | 3134 | 36 3135 | 384 3136 | 3 3137 | 3 3138 | 3139 | 3140 | 3141 | 3142 | 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3657 | 3658 | 3659 | 3660 | 3661 | 1 3662 | 12996 3663 | 3664 | 3665 | 1 3666 | 6498 3667 | 3668 | 3669 | 1 3670 | 2 3671 | 12996 3672 | 3673 | 3674 | 3675 | 3676 | 1 3677 | 1 3678 | 200 3679 | 7 3680 | 3681 | 3682 | 3683 | 3684 | 3685 | 3686 | 1 3687 | 1 3688 | 200 3689 | 7 3690 | 3691 | 3692 | 3693 | 3694 | 3695 | 3696 | 3697 | 3698 | 3699 | 3700 | 3701 | 3702 | 3703 | 3704 | 3705 | 3706 | 3707 | 3708 | 3709 | 3710 | 3711 | 3712 | 3713 | 3714 | 3715 | 3716 | 3717 | 3718 | 3719 | 3720 | 3721 | 3722 | 3723 | 3724 | 3725 | 3726 | 3727 | 3728 | 3729 | 3730 | 3731 | 3732 | 3733 | 3734 | 3735 | 3736 | 3737 | 3738 | 3739 | 3740 | 3741 | 3742 | 3743 | 3744 | 3745 | 3746 | 3747 | 3748 | 3749 | 3750 | 3751 | 3752 | 3753 | 3754 | 3755 | 3756 | 3757 | 3758 | 3759 | 3760 | 3761 | 3762 | 3763 | 3764 | 3765 | 3766 | 3767 | 3768 | 3769 | 3770 | 3771 | 3772 | 3773 | 3774 | 3775 | 3776 | 3777 | 3778 | 3779 | 3780 | 3781 | 3782 | 3783 | 3784 | 3785 | 3786 | 3787 | 3788 | 3789 | 3790 | 3791 | 3792 | 3793 | 3794 | 3795 | 3796 | 3797 | 3798 | 3799 | 3800 | 3801 | 3802 | 3803 | 3804 | 3805 | 3806 | 3807 | 3808 | 3809 | 3810 | 3811 | 3812 | 3813 | 3814 | 3815 | 3816 | 3817 | 3818 | 3819 | 3820 | 3821 | 3822 | 3823 | 3824 | 3825 | 3826 | 3827 | 3828 | 3829 | 3830 | 3831 | 3832 | 3833 | 3834 | 3835 | 3836 | 3837 | 3838 | 3839 | 3840 | 3841 | 3842 | 3843 | 3844 | 3845 | 3846 | 3847 | 3848 | 3849 | 3850 | 3851 | 3852 | 3853 | 3854 | 3855 | 3856 | 3857 | 3858 | 3859 | 3860 | 3861 | 3862 | 3863 | 3864 | 3865 | 3866 | 3867 | 3868 | 3869 | 3870 | 3871 | 3872 | 3873 | 3874 | 3875 | 3876 | 3877 | 3878 | 3879 | 3880 | 3881 | 3882 | 3883 | 3884 | 3885 | 3886 | 3887 | 3888 | 3889 | 3890 | 3891 | 3892 | 3893 | 3894 | 3895 | 3896 | 3897 | 3898 | 3899 | 3900 | 3901 | 3902 | 3903 | 3904 | 3905 | 3906 | 3907 | 3908 | 3909 | 3910 | 3911 | 3912 | 3913 | 3914 | 3915 | 3916 | 3917 | 3918 | 3919 | 3920 | 3921 | 3922 | 3923 | 3924 | 3925 | 3926 | 3927 | 3928 | 3929 | 3930 | 3931 | 3932 | 3933 | 3934 | 3935 | 3936 | 3937 | 3938 | 3939 | 3940 | 3941 | 3942 | 3943 | 3944 | 3945 | 3946 | 3947 | 3948 | 3949 | 3950 | 3951 | 3952 | 3953 | 3954 | 3955 | 3956 | 3957 | 3958 | 3959 | 3960 | 3961 | 3962 | 3963 | 3964 | 3965 | 3966 | 3967 | -------------------------------------------------------------------------------- /2-face-detection-retail/requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python==4.5.1.48; platform_machine != "aarch64" 2 | depthai==2.11.1.1 3 | blobconverter==1.2.6 -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DepthAI Tutorials Source Code 2 | 3 | This repo contains source code for tutorials published on [docs.luxonis.com](https://docs.luxonis.com). 4 | --------------------------------------------------------------------------------