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 š¦
"!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
