我正在尝试转换Keras DarkNet代码的一部分,以使代码运行得更快。下面是我正在尝试优化的代码:
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这是我试图在不使用中间PIL转换来减少时间的情况下实现相同的输出:
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,在运行代码时,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''在规范化之前,我似乎需要将uint8类型更改为float32,但我不确定如何使用OpenCV来实现。
发布于 2019-08-02 21:49:21
您可以使用resized_image.astype(np.float32)将resized_image数据从unit8转换为float32,然后进行规范化和其他操作:
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.发布于 2019-08-02 20:23:06
您的问题是使用/=对同一变量进行除法和赋值。Numpy期望当您这样做时,数组的类型与以前相同,但您使用的是一个浮点数除法,这将改变值类型。
要解决此问题,您可以执行以下操作:
resized_image = resized_image / 255.它应该是有效的。但您必须注意,它会将矩阵转换为dtype=float64。要将其转换为float32,您可以执行以下操作:
resized_image.astype(np.float32)或
np.float32(resized_image)np应该来自:
import numpy as nphttps://stackoverflow.com/questions/57325720
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