Object_Detection: NanoDet (GSOC)

models/object_detection_nanodet/LICENSE

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models/object_detection_nanodet/NanodetPlus.py

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+import cv2
+import numpy as np
+
+class NanoDet():
+    def __init__(self, modelPath, prob_threshold=0.35, iou_threshold=0.6):
+        with open('coco.names', 'rt') as f:
+            self.classes = f.read().rstrip('\n').split('\n')
+
+        self.num_classes = len(self.classes)
+        self.strides = (8, 16, 32, 64)
+        self.image_shape = (416, 416)
+        self.reg_max = 7
+        self.prob_threshold = prob_threshold
+        self.iou_threshold = iou_threshold
+        self.project = np.arange(self.reg_max + 1)
+        self.mean = np.array([103.53, 116.28, 123.675], dtype=np.float32).reshape(1, 1, 3)
+        self.std = np.array([57.375, 57.12, 58.395], dtype=np.float32).reshape(1, 1, 3)
+        self.net = cv2.dnn.readNet(modelPath)
+
+        self.anchors_mlvl = []
+        for i in range(len(self.strides)):
+            featmap_size = (int(self.image_shape[0] / self.strides[i]), int(self.image_shape[1] / self.strides[i]))
+            stride = self.strides[i]
+            feat_h, feat_w = featmap_size
+            shift_x = np.arange(0, feat_w) * stride
+            shift_y = np.arange(0, feat_h) * stride
+            xv, yv = np.meshgrid(shift_x, shift_y)
+            xv = xv.flatten()
+            yv = yv.flatten()
+            cx = xv + 0.5 * (stride-1)
+            cy = yv + 0.5 * (stride - 1)
+            #anchors = np.stack((cx, cy), axis=-1)
+            anchors = np.column_stack((cx, cy))
+            self.anchors_mlvl.append(anchors)
+
+    def softmax_func(self,x, axis=0):
+        x_exp = np.exp(x)
+        x_sum = np.sum(x_exp, axis=axis, keepdims=True)
+        s = x_exp / x_sum
+        return s
+
+    def pre_process(self, img):
+        img = img.astype(np.float32)
+        img = (img - self.mean) / self.std
+        blob = cv2.dnn.blobFromImage(img)
+        return blob
+
+    def infer(self, srcimg, keep_ratio=True):
+        top, left, newh, neww = 0, 0, self.image_shape[0], self.image_shape[1]
+        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.image_shape[0], int(self.image_shape[1] / hw_scale)
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                left = int((self.image_shape[1] - neww) * 0.5)
+                img = cv2.copyMakeBorder(img, 0, 0, left, self.image_shape[1] - neww - left, cv2.BORDER_CONSTANT,
+                                         value=0)  # add border
+            else:
+                newh, neww = int(self.image_shape[0] * hw_scale), self.image_shape[1]
+                img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA)
+                top = int((self.image_shape[0] - newh) * 0.5)
+                img = cv2.copyMakeBorder(img, top, self.image_shape[0] - newh - top, 0, 0, cv2.BORDER_CONSTANT, value=0)
+        else:
+            img = cv2.resize(srcimg, self.image_shape, interpolation=cv2.INTER_AREA)
+
+        blob = self.pre_process(img)
+        self.net.setInput(blob)
+        outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+        det_bboxes, det_conf, det_classid = self.post_process(outs)
+        ratioh,ratiow = srcimg.shape[0]/newh,srcimg.shape[1]/neww
+
+        return left, top, ratioh, ratiow, det_bboxes, det_conf, det_classid
+
+
+    def post_process(self, preds):
+        cls_scores, bbox_preds = preds[::2], preds[1::2]
+        rescale = False
+        scale_factor = 1
+        bboxes_mlvl = []
+        scores_mlvl = []
+        for stride, cls_score, bbox_pred, anchors in zip(self.strides, cls_scores, bbox_preds, self.anchors_mlvl):
+            if cls_score.ndim==3:
+                cls_score = cls_score.squeeze(axis=0)
+            if bbox_pred.ndim==3:
+                bbox_pred = bbox_pred.squeeze(axis=0)
+            bbox_pred = self.softmax_func(bbox_pred.reshape(-1, self.reg_max + 1), axis=1)
+            bbox_pred = np.dot(bbox_pred, self.project).reshape(-1,4)
+            bbox_pred *= stride
+
+            nms_pre = 1000
+            if nms_pre > 0 and cls_score.shape[0] > nms_pre:
+                max_scores = cls_score.max(axis=1)
+                topk_inds = max_scores.argsort()[::-1][0:nms_pre]
+                anchors = anchors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                cls_score = cls_score[topk_inds, :]
+
+            points = anchors
+            distance = bbox_pred
+            max_shape=self.image_shape
+            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])
+
+            #bboxes = np.stack([x1, y1, x2, y2], axis=-1)
+            bboxes = np.column_stack([x1, y1, x2, y2])
+            bboxes_mlvl.append(bboxes)
+            scores_mlvl.append(cls_score)
+
+        bboxes_mlvl = np.concatenate(bboxes_mlvl, axis=0)
+        if rescale:
+            bboxes_mlvl /= scale_factor
+        scores_mlvl = np.concatenate(scores_mlvl, axis=0)
+        bboxes_wh = bboxes_mlvl.copy()
+        bboxes_wh[:, 2:4] = bboxes_wh[:, 2:4] - bboxes_wh[:, 0:2]
+        classIds = np.argmax(scores_mlvl, axis=1)
+        confidences = np.max(scores_mlvl, axis=1)
+
+        indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.prob_threshold, self.iou_threshold)
+
+        if len(indices)>0:
+            det_bboxes = bboxes_mlvl[indices[:]]
+            det_conf = confidences[indices[:]]
+            det_classid = classIds[indices[:]]
+
+        else:
+            det_bboxes = np.array([])
+            det_conf = np.array([])
+            det_classid = np.array([])
+
+        return det_bboxes.astype(np.float32), det_conf, det_classid