cv2.addWeighted报错：输入数组类型不同需指定输出类型

问题重现 #

执行cv2.addWeighted(image, 1, lane_image, 1, 0)时触发以下错误：

cv2.error: OpenCV(4.6.0) /Users/runner/work/opencv-python/opencv-python/opencv/modules/core/src/arithm.cpp:674: error: (-5:Bad argument) When the input arrays in add/subtract/multiply/divide functions have different types, the output array type must be explicitly specified in function 'arithm_op'

相关代码片段：

# Get image ready for feeding into model
#small_img = imresize(image, (80, 160, 3))
small_img  = resize(image, (80,160), order=3)

small_img = np.array(small_img)
small_img = small_img[None,:,:,:]

# Make prediction with neural network (un-normalize value by multiplying by 255)
prediction = model.predict(small_img)[0] * 255

# Add lane prediction to list for averaging
lanes.recent_fit.append(prediction)
# Only using last five for average
if len(lanes.recent_fit) > 5:
    lanes.recent_fit = lanes.recent_fit[1:]

# Calculate average detection
lanes.avg_fit = np.mean(np.array([i for i in lanes.recent_fit]), axis = 0)

# Generate fake R & B color dimensions, stack with G
blanks = np.zeros_like(lanes.avg_fit).astype(np.uint8)
lane_drawn = np.dstack((blanks, lanes.avg_fit, blanks))

# Re-size to match the original image
#lane_image = imresize(lane_drawn, (720, 1280, 3))
lane_image = resize(lane_drawn, (720,1280), order=3)

# Merge the lane drawing onto the original image
result = cv2.addWeighted(image, 1, lane_image, 1, 0)

return result

错误原因 #

• 原始图像image通常为uint8类型（像素值范围0-255的整数），而经过模型预测、均值计算后的lanes.avg_fit是浮点型，后续通过dstack和resize生成的lane_image也保留了浮点类型。
• cv2.addWeighted要求输入数组的数据类型一致；当输入类型不同时，必须显式指定输出数组的类型，否则会触发该参数错误。

解决方案 #

方案一：统一输入数组类型 #

将lane_image转换为与image一致的uint8类型，在resize后添加类型转换代码：

# Re-size to match the original image
lane_image = resize(lane_drawn, (720,1280), order=3)
# 转换为uint8类型，确保像素值在0-255范围内
lane_image = lane_image.astype(np.uint8)

注：当前代码中prediction已通过*255完成反归一化，均值后的lanes.avg_fit值范围在0-255之间，直接转换不会出现数值溢出问题。

方案二：显式指定输出数组类型 #

调用cv2.addWeighted时，通过dtype参数强制指定输出类型为uint8：

# Merge the lane drawing onto the original image
result = cv2.addWeighted(image, 1, lane_image, 1, 0, dtype=cv2.CV_8U)

内容的提问来源于stack exchange，提问作者Pranav MS