set up

from pathlib import Path

import numpy as np

import cv2

import depthai as dai

import time

def preproc(image, input_size, mean, std, swap=(2, 0, 1)):

if len(image.shape) == 3:

padded_img = np.ones((input_size[0], input_size[1], 3)) * 114.0

else:

padded_img = np.ones(input_size) * 114.0

img = np.array(image)

r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])

# print(len(float r))

resized_img = cv2.resize(

img,

(int(img.shape [1] * r), int(img.shape[0] * r)),

interpolation=cv2.INTER_LINEAR,

).astype(np.float32)

padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img

padded_img = padded_img[:, :, ::-1]

padded_img /= 255.0

if mean is not None:

padded_img -= mean

if std is not None:

padded_img /= std

padded_img = padded_img.transpose(swap)

padded_img = np.ascontiguousarray(padded_img, dtype=np.float16)

#print(padded_img)

return padded_img, r

def nms(boxes, scores, nms_thr):

"""Single class NMS implemented in Numpy."""

x1 = boxes[:, 0]

y1 = boxes[:, 1]

x2 = boxes[:, 2]

y2 = boxes[:, 3]

areas = (x2 - x1 + 1) * (y2 - y1 + 1)

order = scores.argsort()[::-1]

keep = []

while order.size > 0:

i = order[0]

keep.append(i)

xx1 = np.maximum(x1, x1[order[1:]])

yy1 = np.maximum(y1, y1[order[1:]])

xx2 = np.minimum(x2, x2[order[1:]])

yy2 = np.minimum(y2, y2[order[1:]])

w = np.maximum(0.0, xx2 - xx1 + 1)

h = np.maximum(0.0, yy2 - yy1 + 1)

inter = w * h

ovr = inter / (areas + areas[order[1:]] - inter)

inds = np.where(ovr <= nms_thr)[0]

order = order[inds + 1]

return keep

def multiclass_nms(boxes, scores, nms_thr, score_thr):

"""Multiclass NMS implemented in Numpy"""

final_dets = []

num_classes = scores.shape[1]

for cls_ind in range(num_classes):

cls_scores = scores[:, cls_ind]

valid_score_mask = cls_scores > score_thr

if valid_score_mask.sum() == 0:

continue

else:

valid_scores = cls_scores[valid_score_mask]

valid_boxes = boxes[valid_score_mask]

keep = nms(valid_boxes, valid_scores, nms_thr)

if len(keep) > 0:

cls_inds = np.ones((len(keep), 1)) * cls_ind

dets = np.concatenate(

[valid_boxes[keep], valid_scores[keep, None], cls_inds], 1

)

final_dets.append(dets)

if len(final_dets) == 0:

return None

return np.concatenate(final_dets, 0)

def demo_postprocess(outputs, img_size, p6=False):

grids = []

expanded_strides = []

if not p6:

strides = [8, 16, 32]

else:

strides = [8, 16, 32, 64]

hsizes = [img_size[0] // stride for stride in strides]

wsizes = [img_size[1] // stride for stride in strides]

for hsize, wsize, stride in zip(hsizes, wsizes, strides):

xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))

grid = np.stack((xv, yv), 2).reshape(1, -1, 2)

grids.append(grid)

shape = grid.shape[:2]

expanded_strides.append(np.full((*shape, 1), stride))

grids = np.concatenate(grids, 1)

expanded_strides = np.concatenate(expanded_strides, 1)

outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides

outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides

return outputs

syncNN = False

SHAPE = 416

labelMap = ["face"]

p = dai.Pipeline()

p.setOpenVINOVersion(dai.OpenVINO.VERSION_2022_1)

class FPSHandler:

def init(self, cap=None):

self.timestamp = time.time()

self.start = time.time()

self.frame_cnt = 0

def next_iter(self):

self.timestamp = time.time()

self.frame_cnt += 1

def fps(self):

return self.frame_cnt / (self.timestamp - self.start)

camera = p.create(dai.node.ColorCamera)

camera.setPreviewSize(SHAPE, SHAPE)

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

camera.setInterleaved(False)

camera.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)

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

#nn.setBlobPath(str(Path("yolov8_openvino_blob2022.1_6shave.blob").resolve().absolute()))

nn.setBlobPath(str(Path("1yolov8_openvino_blob2022.1_6shave.blob").resolve().absolute()))

nn.setNumInferenceThreads(2)

nn.input.setBlocking(True)

# Send camera frames to the host

camera_xout = p.create(dai.node.XLinkOut)

camera_xout.setStreamName("camera")

camera.preview.link(camera_xout.input)

# Send converted frames from the host to the NN

nn_xin = p.create(dai.node.XLinkIn)

nn_xin.setStreamName("nnInput")

nn_xin.out.link(nn.input)

# Send bounding boxes from the NN to the host via XLink

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

nn_xout.setStreamName("nn")

nn.out.link(nn_xout.input)

# Pipeline is defined, now we can connect to the device

with dai.Device(p) as device:

qCamera = device.getOutputQueue(name="camera", maxSize=4, blocking=False)

qNnInput = device.getInputQueue("nnInput", maxSize=4, blocking=False)

qNn = device.getOutputQueue(name="nn", maxSize=4, blocking=True)

fps = FPSHandler()

while True:

inRgb = qCamera.get()

frame = inRgb.getCvFrame()

# Set these according to your dataset

mean = (0.485, 0.456, 0.406)

std = (0.229, 0.224, 0.225)

image, ratio = preproc(frame, (SHAPE, SHAPE), mean, std)

# NOTE: The model expects an FP16 input image, but ImgFrame accepts a list of ints only. I work around this by

# spreading the FP16 across two ints

image = list(image.tobytes())

dai_frame = dai.ImgFrame()

dai_frame.setHeight(SHAPE)

dai_frame.setWidth(SHAPE)

dai_frame.setData(image)

qNnInput.send(dai_frame)

if syncNN:

in_nn = qNn.get()

else:

in_nn = qNn.tryGet()

if in_nn is not None:

fps.next_iter()

cv2.putText(frame, "Fps: {:.2f}".format(fps.fps()), (2, SHAPE - 4), cv2.FONT_HERSHEY_TRIPLEX, 0.4, color=(255, 255, 255))

data = np.array(in_nn.getLayerFp16('output')).reshape(1, 3549, 85)

# print(len(in_nn.getLayerFp16('output')))

output_array = in_nn.getLayerFp16('output')

print("Size of output array:", output_array.size)

print("Contents of output array:", output_array)

predictions = demo_postprocess(data, (SHAPE, SHAPE), p6=False)[0]

boxes = predictions[:, :4]

scores = predictions[:, 4, None] * predictions[:, 5:]

boxes_xyxy = np.ones_like(boxes)

boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.

boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.

boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.

boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.

dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.3)

if dets is not None:

final_boxes = dets[:, :4]

final_scores, final_cls_inds = dets[:, 4], dets[:, 5]

for i in range(len(final_boxes)):

bbox = final_boxes

score = final_scores

class_name = labelMap[int(final_cls_inds)]

if score >= 0.1:

# Limit the bounding box to 0..SHAPE

bbox[bbox > SHAPE - 1] = SHAPE - 1

bbox[bbox < 0] = 0

xy_min = (int(bbox[0]), int(bbox[1]))

xy_max = (int(bbox[2]), int(bbox[3]))

# Display detection's BB, label and confidence on the frame

cv2.rectangle(frame, xy_min , xy_max, (255, 0, 0), 2)

cv2.putText(frame, class_name, (xy_min[0] + 10, xy_min[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

cv2.putText(frame, f"{int(score * 100)}%", (xy_min[0] + 10, xy_min[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)

cv2.imshow("rgb", frame)

if cv2.waitKey(1) == ord('q'):

break

this is the code am using for training

"!yolo train model=yolov8n.pt data=/home/thasni/Downloads/face_yolov8/data.yaml epochs=20 imgsz=416 batch=32 lr0=0.01 device=0 "

]

},

{

[

"!yolo val model=/home/thasni/jupy_oak/ultralytics/runs/detect/train16/weights/best.pt data=/home/thasni/Downloads/face_yolov8/data.yaml\n",



"!yolo predict model=/home/thasni/jupy_oak/ultralytics/runs/detect/train16/weights/best.pt source='/home/thasni/jupy_oak/ultralytics/runs/detect/val15/2.jpg'\n"

]

},


}

],

"!yolo task=detect mode=predict model=/home/thasni/jupy_oak/ultralytics/runs/detect/train16/weights/best.pt show=True conf=0.5 source='/home/thasni/Downloads/face_yolov8/train/images/1.jpg'\n",


},

{

"!cp /home/thasni/jupy_oak/ultralytics/runs/detect/train16/weights/best.pt runs/detect/train/weights/yolov8ntrained.pt"

"!yolo task=detect mode=export model= /home/thasni/jupy_oak/ultralytics/runs/detect/train16/weights/best.pt format=onnx imgsz=416,416 "

]




error is

erik

@jakaskerl

Kindly tell the procedure to obtain blob file for YOLOV3 and V4. I have already referred this link: https://github.com/luxonis/depthai-ml-training/blob/master/colab-notebooks/YoloV3_V4_tiny_training.ipynb.

I am getting error when executing this. Say me the procedure for generating blob