修改#检测目标 detections1 = model(image1) detections2 = model(image2) detections3 = model(image3)报错RuntimeError: expected scalar type Byte but found Float

这个错误通常是由于输入图像的像素值类型不正确导致的。YOLOv7模型要求输入图像像素值为8位无符号整数（即`uint8`类型），而您提供的输入图像像素值可能是浮点数（即`float`类型）。您可以使用以下代码将输入图像像素值类型转换为`uint8`类型：

import torch
import numpy as np

# 转换图像为PyTorch张量
image1 = torch.from_numpy(np.ascontiguousarray(image1.astype(np.uint8))).permute(2, 0, 1).unsqueeze(0).to(device)
image2 = torch.from_numpy(np.ascontiguousarray(image2.astype(np.uint8))).permute(2, 0, 1).unsqueeze(0).to(device)
image3 = torch.from_numpy(np.ascontiguousarray(image3.astype(np.uint8))).permute(2, 0, 1).unsqueeze(0).to(device)

# 检测目标
detections1 = model(image1)
detections2 = model(image2)
detections3 = model(image3)

这将确保您的输入图像像素值类型正确，并且可以被YOLOv7模型正确处理。

相关问题

修改# 对每个相机图像进行目标检测 detections1 = model(image1) # 检测结果1 detections2 = model(image2) # 检测结果2 detections3 = model(image3) # 检测结果3 # 对检测结果进行筛选 filtered_detections1 = non_max_suppression(detections1, confidence_threshold, iou_threshold) filtered_detections2 = non_max_suppression(detections2, confidence_threshold, iou_threshold) filtered_detections3 = non_max_suppression(detections3, confidence_threshold, iou_threshold)报错TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not numpy.ndarray

这个错误是因为YOLOv7输出的是一个numpy数组，而非PyTorch张量，因此无法在PyTorch中进一步处理。您需要将numpy数组转换为PyTorch张量，可以使用以下代码：

import torch
import numpy as np

# 转换图像为PyTorch张量
image1 = torch.from_numpy(np.ascontiguousarray(image1)).to(device)
image2 = torch.from_numpy(np.ascontiguousarray(image2)).to(device)
image3 = torch.from_numpy(np.ascontiguousarray(image3)).to(device)

# 检测目标
detections1 = model(image1)
detections2 = model(image2)
detections3 = model(image3)

# 转换检测结果为PyTorch张量
detections1 = [torch.from_numpy(x) for x in detections1]
detections2 = [torch.from_numpy(x) for x in detections2]
detections3 = [torch.from_numpy(x) for x in detections3]

# 对检测结果进行筛选
filtered_detections1 = non_max_suppression(detections1, confidence_threshold, iou_threshold)
filtered_detections2 = non_max_suppression(detections2, confidence_threshold, iou_threshold)
filtered_detections3 = non_max_suppression(detections3, confidence_threshold, iou_threshold)

这将确保您的检测结果可以在PyTorch中进一步处理。

把这段代码补充完整：import numpy as np import cv2 # 定义目标检测函数 def detect_objects(image, threshold): # 使用OpenCV加载预训练的目标检测模型 model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "model.caffemodel") # 图像预处理 blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0)) # 输入图像到模型中进行目标检测 model.setInput(blob) detections = model.forward() # 解析检测结果 num_detections = detections.shape[2] filtered_detections = [] for i in range(num_detections): confidence = detections[0, 0, i, 2] if confidence > threshold: x1 = int(detections[0, 0, i, 3] * image.shape[1]) y1 = int(detections[0, 0, i, 4] * image.shape[0]) x2 = int(detections[0, 0, i, 5] * image.shape[1])

import numpy as np
import cv2

# 定义目标检测函数
def detect_objects(image, threshold):
# 使用OpenCV加载预训练的目标检测模型
model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "model.caffemodel")

# 图像预处理
blob = cv2.dnn.blobFromImage(image, scalefactor=1.0, size=(300, 300), mean=(104.0, 177.0, 123.0))

# 输入图像到模型中进行目标检测
model.setInput(blob)
detections = model.forward()

# 解析检测结果
num_detections = detections.shape[2]
filtered_detections = []
for i in range(num_detections):
confidence = detections[0, 0, i, 2]
if confidence > threshold:
x1 = int(detections[0, 0, i, 3] * image.shape[1])
y1 = int(detections[0, 0, i, 4] * image.shape[0])
x2 = int(detections[0, 0, i, 5] * image.shape[1])
y2 = int(detections[0, 0, i, 6] * image.shape[0])
class_id = int(detections[0, 0, i, 1])

# 将检测到的目标信息保存到结果列表中
filtered_detections.append((x1, y1, x2, y2, class_id))

return filtered_detections

# 示例用法
image = cv2.imread("image.jpg") # 加载图像
threshold = 0.5 # 置信度阈值
result = detect_objects(image, threshold)
print(result)