Merge pull request #111 from remove-YoloX-dot-py
Browse filesDelete YoloX.py (#111):
- Remove the legacy to save git conflicts
YoloX.py
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import numpy as np
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import cv2
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class YoloX:
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def __init__(self, modelPath, confThreshold=0.35, nmsThreshold=0.5, objThreshold=0.5, backendId=0, targetId=0):
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self.num_classes = 80
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self.net = cv2.dnn.readNet(modelPath)
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self.input_size = (640, 640)
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self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
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self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
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self.strides = [8, 16, 32]
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self.confThreshold = confThreshold
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self.nmsThreshold = nmsThreshold
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self.objThreshold = objThreshold
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self.backendId = backendId
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self.targetId = targetId
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self.net.setPreferableBackend(self.backendId)
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self.net.setPreferableTarget(self.targetId)
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self.generateAnchors()
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@property
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def name(self):
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return self.__class__.__name__
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def setBackend(self, backenId):
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self.backendId = backendId
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self.net.setPreferableBackend(self.backendId)
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def setTarget(self, targetId):
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self.targetId = targetId
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self.net.setPreferableTarget(self.targetId)
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def preprocess(self, img):
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blob = np.transpose(img, (2, 0, 1))
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return blob[np.newaxis, :, :, :]
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def infer(self, srcimg):
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input_blob = self.preprocess(srcimg)
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self.net.setInput(input_blob)
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outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
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predictions = self.postprocess(outs[0])
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return predictions
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def postprocess(self, outputs):
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dets = outputs[0]
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dets[:, :2] = (dets[:, :2] + self.grids) * self.expanded_strides
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dets[:, 2:4] = np.exp(dets[:, 2:4]) * self.expanded_strides
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# get boxes
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boxes = dets[:, :4]
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boxes_xyxy = np.ones_like(boxes)
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boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
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boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
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boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
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boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
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# get scores and class indices
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scores = dets[:, 4:5] * dets[:, 5:]
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max_scores = np.amax(scores, axis=1)
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max_scores_idx = np.argmax(scores, axis=1)
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# batched-nms, TODO: replace with cv2.dnn.NMSBoxesBatched when OpenCV 4.7.0 is released
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max_coord = boxes_xyxy.max()
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offsets = max_scores_idx * (max_coord + 1)
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boxes_for_nms = boxes_xyxy + offsets[:, None]
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keep = cv2.dnn.NMSBoxes(boxes_for_nms.tolist(), max_scores.tolist(), self.confThreshold, self.nmsThreshold)
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candidates = np.concatenate([boxes_xyxy, max_scores[:, None], max_scores_idx[:, None]], axis=1)
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return candidates[keep]
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def generateAnchors(self):
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self.grids = []
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self.expanded_strides = []
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hsizes = [self.input_size[0] // stride for stride in self.strides]
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wsizes = [self.input_size[1] // stride for stride in self.strides]
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for hsize, wsize, stride in zip(hsizes, wsizes, self.strides):
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xv, yv = np.meshgrid(np.arange(hsize), np.arange(wsize))
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grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
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self.grids.append(grid)
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shape = grid.shape[:2]
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self.expanded_strides.append(np.full((*shape, 1), stride))
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self.grids = np.concatenate(self.grids, 1)
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self.expanded_strides = np.concatenate(self.expanded_strides, 1)
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