Merge pull request #111 from remove-YoloX-dot-py

Delete YoloX.py (#111):

- Remove the legacy to save git conflicts

models/object_detection_yolox/YoloX.py

-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)
-
-        self.generateAnchors()
-
-    @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):
-        dets = outputs[0]
-
-        dets[:, :2] = (dets[:, :2] + self.grids) * self.expanded_strides
-        dets[:, 2:4] = np.exp(dets[:, 2:4]) * self.expanded_strides
-
-        # get boxes
-        boxes = dets[:, :4]
-        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.
-
-        # get scores and class indices
-        scores = dets[:, 4:5] * dets[:, 5:]
-        max_scores = np.amax(scores, axis=1)
-        max_scores_idx = np.argmax(scores, axis=1)
-
-        # batched-nms, TODO: replace with cv2.dnn.NMSBoxesBatched when OpenCV 4.7.0 is released
-        max_coord = boxes_xyxy.max()
-        offsets = max_scores_idx * (max_coord + 1)
-        boxes_for_nms = boxes_xyxy + offsets[:, None]
-        keep = cv2.dnn.NMSBoxes(boxes_for_nms.tolist(), max_scores.tolist(), self.confThreshold, self.nmsThreshold)
-
-        candidates = np.concatenate([boxes_xyxy, max_scores[:, None], max_scores_idx[:, None]], axis=1)
-        return candidates[keep]
-
-    def generateAnchors(self):
-        self.grids = []
-        self.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)
-            self.grids.append(grid)
-            shape = grid.shape[:2]
-            self.expanded_strides.append(np.full((*shape, 1), stride))
-
-        self.grids = np.concatenate(self.grids, 1)
-        self.expanded_strides = np.concatenate(self.expanded_strides, 1)