numpy - OpenCV-将uint8图像转换为float32标准化图像

我正在尝试转换Keras DarkNet代码的一部分，以尝试使代码运行更快。
这是我要优化的代码:

model_image_size = (416, 416)

import cv2
from PIL import Image

frame = cv2.imread("test.png", cv2.IMREAD_COLOR)

im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
im = Image.fromarray(im).crop((1625, 785, 1920, 1080))  # crop ROI

resized_image = im.resize(tuple(reversed(model_image_size)), Image.BICUBIC)
image_data = np.array(resized_image, dtype='float32')

image_data /= 255.
image_data = np.expand_dims(image_data, 0)  # Add batch dimension.

return image_data

这是我在不使用中间PIL覆盖范围以减少时间的情况下实现相同输出的尝试:

model_image_size = (416, 416)

import cv2

frame = cv2.imread("test.png", cv2.IMREAD_COLOR)

frame = frame[785:1080,1625:1920]  # crop ROI
im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

resized_image = cv2.resize(im, model_image_size, interpolation = cv2.INTER_CUBIC)

resized_image /= 255.
image_data = np.expand_dims(resized_image, 0)  # Add batch dimension.

return image_data

但是，运行代码后，它将返回:

resized_image /= 255.
TypeError: ufunc 'true_divide' output (typecode 'd') could not be coerced to provided output parameter (typecode 'B') according to the casting rule ''same_kind''

似乎我需要在标准化之前将uint8类型更改为float32，但我不确定如何使用OpenCV实现它。

最佳答案

您可以使用resized_image.astype(np.float32)将resized_image数据从unit8转换为float32，然后继续进行标准化和其他操作:

frame = cv2.imread("yourfile.png")

frame = frame[200:500,400:1000]  # crop ROI
im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

model_image_size = (416, 416)
resized_image = cv2.resize(im, model_image_size, interpolation = cv2.INTER_CUBIC)
resized_image = resized_image.astype(np.float32)
resized_image /= 255.

image_data = np.expand_dims(resized_image, 0)  # Add batch dimension.

关于numpy - OpenCV-将uint8图像转换为float32标准化图像，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57325720/