使坐标数据Tensorflow标准化，Tflite-Python

Question

我目前正在开发一个物体检测应用，该应用可以检测轮胎是否损坏。为此，我使用了Google的AutoML edge，它可以导出TFlite模型。现在，我想在我的代码中实现此模型，但显然它预测的坐标已标准化，因此我不得不对它们进行非标准化

在这里查看我的代码：

import tensorflow as tf
import numpy as np
import cv2

MODEL_PATH = 'Resources/model_v1_OD.tflite'
LABEL_PATH = 'Resources/model_v1_OD.txt'


class TFTireModel():
    labels = []
    intepreter = None
    input_details = []
    output_details = []
    height = 0
    width = 0

    def __init__(self):
        with open(LABEL_PATH, 'r') as f:
            self.labels = [line.strip() for line in f.readlines()]

        # Init TFlite interpreter
        self.interpreter = tf.lite.Interpreter(model_path=MODEL_PATH)
        self.interpreter.allocate_tensors()

        # Get input and output tensors.
        self.input_details = self.interpreter.get_input_details()
        self.output_details = self.interpreter.get_output_details()
        # Get input dimensions
        self.height = self.input_details[0]['shape'][1]
        self.width = self.input_details[0]['shape'][2]


    def predict(self, img, threshold=0.3):
        # Resize image to input dimensions
        img = cv2.resize(img, (self.width, self.height))
        img = np.expand_dims(img, axis=0)
        img = (2.0 / 255.0) * img - 1.0
        img = img.astype('uint8')

        # Predict image
        self.interpreter.set_tensor(self.input_details[0]['index'], img)
        self.interpreter.invoke()

        # get results
        boxes = self.interpreter.get_tensor(
            self.output_details[0]['index'])
        print(f"boxes: {boxes}")

        classes = self.interpreter.get_tensor(
            self.output_details[1]['index'])

        scores = self.interpreter.get_tensor(
            self.output_details[2]['index'])

        num = self.interpreter.get_tensor(
            self.output_details[3]['index'])

        # Get output
        output =self._boxes_coordinates(boxes=np.squeeze(boxes[0]),
                            classes=np.squeeze(classes[0]+1).astype(np.int32),
                            scores=np.squeeze(scores[0]),
                            im_width=self.width,
                            im_height=self.height,
                            min_score_thresh=threshold)

        print(f"output: {output}")

        # Format output
        return output

    def _boxes_coordinates(self,
                           boxes,
                           classes,
                           scores,
                           im_width,
                           im_height,
                           max_boxes_to_draw=4,
                           min_score_thresh=0.4):

        print(f"width: {im_width}, height {im_height}" )
        if not max_boxes_to_draw:
            max_boxes_to_draw = boxes.shape[0]
        number_boxes = min(max_boxes_to_draw, boxes.shape[0])
        tire_boxes = []
        # person_labels = []
        for i in range(number_boxes):
            if scores is None or scores[i] > min_score_thresh:
                box = tuple(boxes[i].tolist())
                ymin, xmin, ymax, xmax = box
                xmin, ymin, xmax, ymax = (int(xmin * im_width), int(xmax * im_width), int(ymin * im_height), int(ymax * im_height)) #TODO: DO A LOOP

                #tire_boxes.append([(ymin, xmin, ymax, xmax), scores[i], self.labels[classes[i]]]) #More complete
                tire_boxes.append((xmin, ymin, xmax, ymax))
        return tire_boxes
发生错误的地方：

   boxes = self.interpreter.get_tensor(
            self.output_details[0]['index'])
        print(f"boxes: {boxes}"

boxes: [[[ 0.00263482  0.50020593  0.3734043   0.83953816]
  [ 0.12580797  0.14952084  0.65327024  0.61710536]
  [ 0.13584864  0.38896233  0.6485662   0.85324436]
  [ 0.31914377  0.3945622   0.87147605  0.8458656 ]
  [ 0.01334581  0.03666234  0.46443292  0.55461186]
  [ 0.1018104  -0.08279537  0.6541427   0.37984413]
由于此处的输出已标准化，我不知道如何对其进行标准化。所需的输出是宽度和高度的百分比，如_boxes_coordinates函数中所示。

我目前正在开发一个物体检测应用，该应用可以检测轮胎是否损坏。为此，我使用了Google的AutoML edge，它可以导出TFlite模型。现在我想实现这个...

Answer 1

TFLite的对象检测模型的输出格式为：