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‎models/object_detection_nanodet/LICENSE

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‎models/object_detection_nanodet/NanodetPlus.py

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import cv2
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import numpy as np
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class NanoDet():
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def __init__(self, modelPath, prob_threshold=0.35, iou_threshold=0.6):
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self.strides = (8, 16, 32, 64)
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self.image_shape = (416, 416)
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self.reg_max = 7
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self.prob_threshold = prob_threshold
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self.iou_threshold = iou_threshold
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self.project = np.arange(self.reg_max + 1)
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self.mean = np.array([103.53, 116.28, 123.675], dtype=np.float32).reshape(1, 1, 3)
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self.std = np.array([57.375, 57.12, 58.395], dtype=np.float32).reshape(1, 1, 3)
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self.net = cv2.dnn.readNet(modelPath)
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self.anchors_mlvl = []
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for i in range(len(self.strides)):
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featmap_size = (int(self.image_shape[0] / self.strides[i]), int(self.image_shape[1] / self.strides[i]))
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stride = self.strides[i]
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feat_h, feat_w = featmap_size
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shift_x = np.arange(0, feat_w) * stride
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shift_y = np.arange(0, feat_h) * stride
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xv, yv = np.meshgrid(shift_x, shift_y)
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xv = xv.flatten()
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yv = yv.flatten()
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cx = xv + 0.5 * (stride-1)
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cy = yv + 0.5 * (stride - 1)
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#anchors = np.stack((cx, cy), axis=-1)
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anchors = np.column_stack((cx, cy))
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self.anchors_mlvl.append(anchors)
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def pre_process(self, srcimg, keep_ratio=True):
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top, left, newh, neww = 0, 0, self.image_shape[0], self.image_shape[1]
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if keep_ratio and srcimg.shape[0] != srcimg.shape[1]:
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hw_scale = srcimg.shape[0] / srcimg.shape[1]
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if hw_scale > 1:
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newh, neww = self.image_shape[0], int(self.image_shape[1] / hw_scale)
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img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
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left = int((self.image_shape[1] - neww) * 0.5)
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img = cv2.copyMakeBorder(img, 0, 0, left, self.image_shape[1] - neww - left, cv2.BORDER_CONSTANT,
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value=0) # add border
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else:
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newh, neww = int(self.image_shape[0] * hw_scale), self.image_shape[1]
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img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
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top = int((self.image_shape[0] - newh) * 0.5)
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img = cv2.copyMakeBorder(img, top, self.image_shape[0] - newh - top, 0, 0, cv2.BORDER_CONSTANT, value=0)
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else:
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img = cv2.resize(srcimg, self.image_shape, interpolation=cv2.INTER_AREA)
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img = img.astype(np.float32)
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img = (img - self.mean) / self.std
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blob = cv2.dnn.blobFromImage(img)
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return blob
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def infer(self, srcimg):
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blob = self.pre_process(srcimg)
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self.net.setInput(blob)
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outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
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det_bboxes, det_conf, det_classid = self.post_process(outs)
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preds = [det_bboxes, det_conf, det_classid]
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return preds
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def post_process(self, preds):
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cls_scores, bbox_preds = preds[::2], preds[1::2]
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rescale = False
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scale_factor = 1
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bboxes_mlvl = []
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scores_mlvl = []
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for stride, cls_score, bbox_pred, anchors in zip(self.strides, cls_scores, bbox_preds, self.anchors_mlvl):
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if cls_score.ndim==3:
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cls_score = cls_score.squeeze(axis=0)
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if bbox_pred.ndim==3:
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bbox_pred = bbox_pred.squeeze(axis=0)
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x_exp = np.exp(bbox_pred.reshape(-1, self.reg_max + 1))
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x_sum = np.sum(x_exp, axis=1, keepdims=True)
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bbox_pred = x_exp / x_sum
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bbox_pred = np.dot(bbox_pred, self.project).reshape(-1,4)
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bbox_pred *= stride
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nms_pre = 1000
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if nms_pre > 0 and cls_score.shape[0] > nms_pre:
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max_scores = cls_score.max(axis=1)
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topk_inds = max_scores.argsort()[::-1][0:nms_pre]
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anchors = anchors[topk_inds, :]
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bbox_pred = bbox_pred[topk_inds, :]
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cls_score = cls_score[topk_inds, :]
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points = anchors
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distance = bbox_pred
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max_shape=self.image_shape
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x1 = points[:, 0] - distance[:, 0]
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y1 = points[:, 1] - distance[:, 1]
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x2 = points[:, 0] + distance[:, 2]
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y2 = points[:, 1] + distance[:, 3]
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if max_shape is not None:
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x1 = np.clip(x1, 0, max_shape[1])
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y1 = np.clip(y1, 0, max_shape[0])
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x2 = np.clip(x2, 0, max_shape[1])
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y2 = np.clip(y2, 0, max_shape[0])
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#bboxes = np.stack([x1, y1, x2, y2], axis=-1)
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bboxes = np.column_stack([x1, y1, x2, y2])
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bboxes_mlvl.append(bboxes)
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scores_mlvl.append(cls_score)
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bboxes_mlvl = np.concatenate(bboxes_mlvl, axis=0)
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if rescale:
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bboxes_mlvl /= scale_factor
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scores_mlvl = np.concatenate(scores_mlvl, axis=0)
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bboxes_wh = bboxes_mlvl.copy()
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bboxes_wh[:, 2:4] = bboxes_wh[:, 2:4] - bboxes_wh[:, 0:2]
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classIds = np.argmax(scores_mlvl, axis=1)
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confidences = np.max(scores_mlvl, axis=1)
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indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.prob_threshold, self.iou_threshold)
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if len(indices)>0:
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det_bboxes = bboxes_mlvl[indices[:]]
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det_conf = confidences[indices[:]]
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det_classid = classIds[indices[:]]
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else:
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det_bboxes = np.array([])
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det_conf = np.array([])
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det_classid = np.array([])
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return det_bboxes.astype(np.float32), det_conf, det_classid

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