Hab2Verer
commented
6 months ago Any updates?
Closed fn-hide closed 6 months ago
Hab2Verer
commented
6 months ago Any updates?
fn-hide
commented
6 months ago Sorry, I made a mistake with my own code after exporting to onnx. Yolov8-face.pt has a key point and I need to parse it which I missed in my first code.
Hab2Verer
commented
6 months ago @fn-hide Can you provide the code to export to onnx, and the usage of the onnx model?
fn-hide
commented
6 months ago Like usual, you can export .pt to .onnx with:
model= YOLO('yolov8n-face.pt')
model.export(format='onnx', opset=12)If you're not sure about the model, you can validate your exported model using Netron. Just upload your .onnx model then you can view the architecture.
I got the code to use exported model in hpc230.
Here is the code to use the exported model.
class YOLOv8nFace:
def __init__(self, path, conf_thres=0.45, iou_thres=0.5):
self.conf_threshold = conf_thres
self.iou_threshold = iou_thres
self.class_names = ['face']
self.num_classes = len(self.class_names)
# Initialize model
self.net = cv.dnn.readNet(path)
self.input_height = 640
self.input_width = 640
self.reg_max = 16
self.project = np.arange(self.reg_max)
self.strides = (8, 16, 32)
self.feats_hw = [
(math.ceil(self.input_height / self.strides[i]), math.ceil(self.input_width / self.strides[i]))
for i in range(len(self.strides))
]
self.anchors = self.make_anchors(self.feats_hw)
def make_anchors(self, feats_hw, grid_cell_offset=0.5):
"""Generate anchors from features."""
anchor_points = {}
for i, stride in enumerate(self.strides):
h, w = feats_hw[i]
x = np.arange(0, w) + grid_cell_offset # shift x
y = np.arange(0, h) + grid_cell_offset # shift y
sx, sy = np.meshgrid(x, y)
# sy, sx = np.meshgrid(y, x)
anchor_points[stride] = np.stack((sx, sy), axis=-1).reshape(-1, 2)
return anchor_points
@staticmethod
def softmax(x, axis=1):
x_exp = np.exp(x)
# 如果是列向量，则axis=0
x_sum = np.sum(x_exp, axis=axis, keepdims=True)
s = x_exp / x_sum
return s
def resize_image(self, srcimg, keep_ratio=True):
top, left, newh, neww = 0, 0, self.input_width, self.input_height
if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
hw_scale = srcimg.shape[0] / srcimg.shape[1]
if hw_scale > 1:
newh, neww = self.input_height, int(self.input_width / hw_scale)
img = cv.resize(srcimg, (neww, newh), interpolation=cv.INTER_AREA)
left = int((self.input_width - neww) * 0.5)
img = cv.copyMakeBorder(
img, 0, 0, left, self.input_width - neww - left, cv.BORDER_CONSTANT, value=(0, 0, 0)
) # add border
else:
newh, neww = int(self.input_height * hw_scale), self.input_width
img = cv.resize(srcimg, (neww, newh), interpolation=cv.INTER_AREA)
top = int((self.input_height - newh) * 0.5)
img = cv.copyMakeBorder(
img, top, self.input_height - newh - top, 0, 0, cv.BORDER_CONSTANT, value=(0, 0, 0)
)
else:
img = cv.resize(srcimg, (self.input_width, self.input_height), interpolation=cv.INTER_AREA)
return img, newh, neww, top, left
def detect(self, srcimg):
input_img, newh, neww, padh, padw = self.resize_image(cv.cvtColor(srcimg, cv.COLOR_BGR2RGB))
scale_h, scale_w = srcimg.shape[0] / newh, srcimg.shape[1] / neww
input_img = input_img.astype(np.float32) / 255.0
blob = cv.dnn.blobFromImage(input_img)
self.net.setInput(blob)
outputs = self.net.forward(self.net.getUnconnectedOutLayersNames())
# if isinstance(outputs, tuple):
# outputs = list(outputs)
# if float(cv.__version__[:3])>=4.7:
# outputs = [outputs[2], outputs[0], outputs[1]] ###opencv4.7需要这一步，opencv4.5不需要
# Perform inference on the image
det_bboxes, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw)
# return bounding boxes in xywh format
return det_bboxes, det_conf, det_classid, landmarks
def post_process(self, preds, scale_h, scale_w, padh, padw):
bboxes, scores, landmarks = [], [], []
for i, pred in enumerate(preds):
stride = int(self.input_height / pred.shape[2])
pred = pred.transpose((0, 2, 3, 1))
box = pred[..., :self.reg_max * 4]
cls = 1 / (1 + np.exp(-pred[..., self.reg_max * 4:-15])).reshape((-1, 1))
kpts = pred[..., -15:].reshape((-1, 15)) # x1,y1,score1, ..., x5,y5,score5
# tmp = box.reshape(self.feats_hw[i][0], self.feats_hw[i][1], 4, self.reg_max)
tmp = box.reshape(-1, 4, self.reg_max)
bbox_pred = self.softmax(tmp, axis=-1)
bbox_pred = np.dot(bbox_pred, self.project).reshape((-1, 4))
bbox = self.distance2bbox(self.anchors[stride], bbox_pred,
max_shape=(self.input_height, self.input_width)) * stride
kpts[:, 0::3] = (kpts[:, 0::3] * 2.0 + (self.anchors[stride][:, 0].reshape((-1, 1)) - 0.5)) * stride
kpts[:, 1::3] = (kpts[:, 1::3] * 2.0 + (self.anchors[stride][:, 1].reshape((-1, 1)) - 0.5)) * stride
kpts[:, 2::3] = 1 / (1 + np.exp(-kpts[:, 2::3]))
bbox -= np.array([[padw, padh, padw, padh]]) # 合理使用广播法则
bbox *= np.array([[scale_w, scale_h, scale_w, scale_h]])
kpts -= np.tile(np.array([padw, padh, 0]), 5).reshape((1, 15))
kpts *= np.tile(np.array([scale_w, scale_h, 1]), 5).reshape((1, 15))
bboxes.append(bbox)
scores.append(cls)
landmarks.append(kpts)
bboxes = np.concatenate(bboxes, axis=0)
scores = np.concatenate(scores, axis=0)
landmarks = np.concatenate(landmarks, axis=0)
bboxes_wh = bboxes.copy()
bboxes_wh[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2] # xywh
class_ids = np.argmax(scores, axis=1)
confidences = np.max(scores, axis=1) # max_class_confidence
mask = confidences > self.conf_threshold
bboxes_wh = bboxes_wh[mask] # 合理使用广播法则
confidences = confidences[mask]
class_ids = class_ids[mask]
landmarks = landmarks[mask]
indices = cv.dnn.NMSBoxes(
bboxes_wh.tolist(), confidences.tolist(), self.conf_threshold, self.iou_threshold
)
if len(indices) > 0:
mlvl_bboxes = bboxes_wh[indices]
confidences = confidences[indices]
class_ids = class_ids[indices]
landmarks = landmarks[indices]
return mlvl_bboxes, confidences, class_ids, landmarks
else:
return np.array([]), np.array([]), np.array([]), np.array([])
@staticmethod
def distance2bbox(points, distance, max_shape=None):
x1 = points[:, 0] - distance[:, 0]
y1 = points[:, 1] - distance[:, 1]
x2 = points[:, 0] + distance[:, 2]
y2 = points[:, 1] + distance[:, 3]
if max_shape is not None:
x1 = np.clip(x1, 0, max_shape[1])
y1 = np.clip(y1, 0, max_shape[0])
x2 = np.clip(x2, 0, max_shape[1])
y2 = np.clip(y2, 0, max_shape[0])
return np.stack([x1, y1, x2, y2], axis=-1)
@staticmethod
def draw_detections(image, boxes, scores, kpts):
for box, score, kp in zip(boxes, scores, kpts):
x, y, w, h = box.astype(int)
# Draw rectangle
cv.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), thickness=3)
cv.putText(
image,
"face:" + str(round(score, 2)),
(x, y - 5),
cv.FONT_HERSHEY_SIMPLEX,
1,
(0, 0, 255),
thickness=2
)
print()
print('YOLOv8nFace -> draw_detections()')
for i in range(5):
x, y, kp_conf = int(kp[i * 3]), int(kp[i * 3 + 1]), kp[i * 3 + 2]
print(f"{i}. x: {x}, y: {y}, conf: {kp_conf:.2f}")
cv.circle(image, (x, y), 4, (0, 255, 0), thickness=-1)
cv.putText(
image,
str(i),
(x, y - 10),
cv.FONT_HERSHEY_SIMPLEX,
.5,
(255, 0, 0),
thickness=2
)
else:
print()
return image
if __name__ == '__main__':
tic = time.perf_counter()
# --- YOLOv8nFace Run Example
parser = argparse.ArgumentParser()
parser.add_argument(
'--imgpath',
type=str,
default='images.jpeg',
help="image path"
)
parser.add_argument(
'--modelpath',
type=str,
default='yolov8n-face.onnx',
help="onnx filepath"
)
parser.add_argument('--confThreshold', default=0.45, type=float, help='class confidence')
parser.add_argument('--nmsThreshold', default=0.5, type=float, help='nms iou thresh')
args = parser.parse_args()
# Initialize YOLOv8_Face object detector
YOLOv8_Face_detector = YOLOv8nFace(args.modelpath, conf_thres=args.confThreshold, iou_thres=args.nmsThreshold)
source_image = cv.imread(args.imgpath)
# Detect Objects
result_boxes, result_scores, result_classids, result_kpts = YOLOv8_Face_detector.detect(source_image)
# Draw detections
dstimg = YOLOv8_Face_detector.draw_detections(source_image, result_boxes, result_scores, result_kpts)
# cv.imwrite('result.jpg', dstimg)
window_name = 'YOLOv8n-Face Inference using ONNX with OpenCV DNN'
cv.namedWindow(window_name, 0)
cv.imshow(window_name, dstimg)
cv.waitKey(0)
cv.destroyAllWindows()Hope this help.
Hab2Verer
commented
6 months ago @fn-hide Thanks, this was helpful.
I export official yolov8n-face.pt model to .onnx like this
Then I load my own .onnx after I export the model like this
But, I got an error
Am I wrong in exporting the .pt model to .onnx?