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import numpy as np
import cv2
class YoloX:
def __init__(self, modelPath, confThreshold=0.35, nmsThreshold=0.5, objThreshold=0.5, backendId=0, targetId=0):
self.num_classes = 80
self.net = cv2.dnn.readNet(modelPath)
self.input_size = (640, 640)
self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
self.strides = [8, 16, 32]
self.confThreshold = confThreshold
self.nmsThreshold = nmsThreshold
self.objThreshold = objThreshold
self.backendId = backendId
self.targetId = targetId
self.net.setPreferableBackend(self.backendId)
self.net.setPreferableTarget(self.targetId)
@property
def name(self):
return self.__class__.__name__
def setBackend(self, backenId):
self.backendId = backendId
self.net.setPreferableBackend(self.backendId)
def setTarget(self, targetId):
self.targetId = targetId
self.net.setPreferableTarget(self.targetId)
def preprocess(self, img):
blob = np.transpose(img, (2, 0, 1))
return blob[np.newaxis, :, :, :]
def infer(self, srcimg):
input_blob = self.preprocess(srcimg)
self.net.setInput(input_blob)
outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
predictions = self.postprocess(outs[0])
return predictions
def postprocess(self, outputs):
grids = []
expanded_strides = []
hsizes = [self.input_size[0] // stride for stride in self.strides]
wsizes = [self.input_size[1] // stride for stride in self.strides]
for hsize, wsize, stride in zip(hsizes, wsizes, self.strides):
xv, yv = np.meshgrid(np.arange(hsize), np.arange(wsize))
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
predictions = outputs[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * 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.
# multi-class nms
final_dets = []
for cls_ind in range(scores.shape[1]):
cls_scores = scores[:, cls_ind]
valid_score_mask = cls_scores > self.confThreshold
if valid_score_mask.sum() == 0:
continue
else:
# call nms
indices = cv2.dnn.NMSBoxes(boxes_xyxy.tolist(), cls_scores.tolist(), self.confThreshold, self.nmsThreshold)
classids_ = np.ones((len(indices), 1)) * cls_ind
final_dets.append(
np.concatenate([boxes_xyxy[indices], cls_scores[indices, None], classids_], axis=1)
)
if len(final_dets) == 0:
return np.array([])
return np.concatenate(final_dets, 0)
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