def _to_original_scale(boxes): minmax_boxes = to_minmax(boxes) minmax_boxes[:, 0] *= self.output_width minmax_boxes[:, 2] *= self.output_width minmax_boxes[:, 1] *= self.output_height minmax_boxes[:, 3] *= self.output_height return minmax_boxes.astype(np.int)
def _to_original_scale(boxes): minmax_boxes = to_minmax(boxes) minmax_boxes[:, 0] *= 224 minmax_boxes[:, 2] *= 224 minmax_boxes[:, 1] *= 224 minmax_boxes[:, 3] *= 224 return minmax_boxes.astype(np.int)
def detect(self, image, anchors, net_size=416): image_h, image_w, _ = image.shape new_image = preprocess_input(image, net_size) # 3. predict yolos = self.predict(new_image) boxes_ = postprocess_ouput(yolos, anchors, net_size, image_h, image_w) if len(boxes_) > 0: boxes, probs = boxes_to_array(boxes_) boxes = to_minmax(boxes) labels = np.array([b.get_label() for b in boxes_]) else: boxes, labels, probs = [], [], [] return boxes, labels, probs