YOLOV1 from scratch using Keras Tensorflow 2.0

这个网址的训练程序我都明白了，写一个test(predict)程序，达到可视化检测的效果呢
https://www.maskaravivek.com/post/yolov1/
就是输入dog.jpg 显示下面图像的程序（我有gpt，不要用gpt回答）

【以下回答由 GPT 生成】

我可以回答关于IT方面的问题。请问您有任何具体的问题需要我解答吗？

如果你已经解决了该问题, 非常希望你能够分享一下解决方案, 写成博客, 将相关链接放在评论区, 以帮助更多的人 ^-^

结合GPT给出回答如下请题主参考
首先，你需要训练一个YOLOv1模型并保存该模型的权重。在训练模型后，您可以使用以下代码进行预测并可视化结果：

import cv2
import numpy as np
from PIL import Image
import tensorflow as tf
from yolo_v1 import YOLOv1

# Load model weights
model = YOLOv1()
model.load_weights('path/to/model/weights.h5')

# Define class labels
class_labels = ['class1', 'class2', 'class3', ...]

# Define the image to be predicted
img_path = 'path/to/image/dog.jpg'

# Load the image and preprocess
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (448, 448))
img = np.array(img, dtype='float32')
img /= 255.

# Predict the bounding boxes, objectness scores, and class probabilities
output = model.predict(np.array([img]))

# Process the output to extract the bounding boxes, objectness scores, and class predictions
boxes, obj_scores, class_probs = model.process_output(output[0], threshold=0.2)

# Draw the bounding boxes on the original image
img_draw = cv2.imread(img_path)
img_draw = cv2.cvtColor(img_draw, cv2.COLOR_BGR2RGB)
for i, box in enumerate(boxes):
    x1, y1, x2, y2 = box
    cv2.rectangle(img_draw, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(img_draw, '{}: {:.2f}'.format(class_labels[class_probs[i]], obj_scores[i]), (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display the image
img_draw = Image.fromarray(img_draw)
img_draw.show()

在此代码中，YOLOv1类是你的模型类，并且process_output方法将输出处理为边界框，对象分数和类概率。您可以根据需要更改类标签和阈值。

import cv2
import numpy as np
from PIL import Image
import tensorflow as tf
from yolo_v1 import YOLOv1
# Load model weights
model = YOLOv1()
model.load_weights('path/to/model/weights.h5')
# Define class labels
class_labels = ['class1', 'class2', 'class3', ...]
# Define the image to be predicted
img_path = 'path/to/image/dog.jpg'
# Load the image and preprocess
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (448, 448))
img = np.array(img, dtype='float32')
img /= 255.
# Predict the bounding boxes, objectness scores, and class probabilities
output = model.predict(np.array([img]))
# Process the output to extract the bounding boxes, objectness scores, and class predictions
boxes, obj_scores, class_probs = model.process_output(output[0], threshold=0.2)
# Draw the bounding boxes on the original image
img_draw = cv2.imread(img_path)
img_draw = cv2.cvtColor(img_draw, cv2.COLOR_BGR2RGB)
for i, box in enumerate(boxes):
    x1, y1, x2, y2 = box
    cv2.rectangle(img_draw, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.putText(img_draw, '{}: {:.2f}'.format(class_labels[class_probs[i]], obj_scores[i]), (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
# Display the image
img_draw = Image.fromarray(img_draw)
img_draw.show()

yovo1版本太低了吧，现在都v7了吧