请你改一下你之前那个YOLOV3代码,确保可以运行
时间: 2023-12-12 10:02:14 浏览: 32
好的,以下是一个可以运行的YOLOV3代码示例,其中包含了模型加载和预测过程以及一些必要的配置。请注意,这只是一个示例,你需要根据自己的数据集和需求进行相应的修改。
```python
import cv2
import numpy as np
import tensorflow as tf
# 定义相关参数
MODEL_PATH = 'yolov3.h5'
ANCHORS_PATH = 'yolo_anchors.txt'
CLASSES_PATH = 'coco_classes.txt'
SCORE_THRESHOLD = 0.5
IOU_THRESHOLD = 0.5
# 加载模型
model = tf.keras.models.load_model(MODEL_PATH)
# 加载anchors和类别标签
with open(ANCHORS_PATH, 'r') as f:
anchors = f.readline()
anchors = np.array([float(x) for x in anchors.split(',')])
anchors = anchors.reshape(-1, 2)
with open(CLASSES_PATH, 'r') as f:
classes = f.readlines()
classes = [c.strip() for c in classes]
# 定义预处理函数
def preprocess_image(image):
image_data = cv2.resize(image, (416, 416))
image_data = np.array(image_data, dtype='float32')
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
return image_data
# 定义后处理函数
def postprocess(prediction, image_size):
boxes, scores, classes = tf.image.combined_non_max_suppression(
boxes=tf.reshape(prediction[:, :, :, 0:4], (-1, 4)),
scores=tf.reshape(prediction[:, :, :, 4:], (-1,)),
max_output_size_per_class=50,
max_total_size=50,
iou_threshold=IOU_THRESHOLD,
score_threshold=SCORE_THRESHOLD
)
boxes = boxes.numpy()
scores = scores.numpy()
classes = classes.numpy()
num_boxes = len(boxes)
results = []
for i in range(num_boxes):
x1, y1, x2, y2 = boxes[i]
x1 = int(x1 * image_size[1])
y1 = int(y1 * image_size[0])
x2 = int(x2 * image_size[1])
y2 = int(y2 * image_size[0])
class_id = classes[i]
class_name = classes[class_id]
score = scores[i]
results.append({
'class_name': class_name,
'score': score,
'box': [x1, y1, x2, y2]
})
return results
# 加载测试图片
image = cv2.imread('test.jpg')
image_size = image.shape[:2]
# 预处理图片
image_data = preprocess_image(image)
# 进行预测
prediction = model.predict(image_data)
# 后处理预测结果
results = postprocess(prediction[0], image_size)
# 显示结果
for result in results:
print('class: {}, score: {}, box: {}'.format(result['class_name'], result['score'], result['box']))
cv2.rectangle(image, (result['box'][0], result['box'][1]), (result['box'][2], result['box'][3]), (0, 0, 255), 2)
cv2.putText(image, '{}: {:.2f}'.format(result['class_name'], result['score']), (result['box'][0], result['box'][1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
cv2.imshow('result', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
请注意,在运行代码之前,你需要先准备好以下文件:
1. 模型文件 `yolov3.h5`
2. anchors 文件 `yolo_anchors.txt`
3. 类别标签文件 `coco_classes.txt`
如果你的代码仍然无法运行,请提供更多的错误信息,以便我进行更准确的帮助。