我们使用img.tobytes()来读取图像的所有二进制数据。 # 以二进制模式打开图片文件withopen(image_path,'rb')asimg_file:img_data=img_file.read()# 读取文件为二进制数据 1. 2. 3. 步骤4: 转换为bytearray 一旦我们得到了图片的二进制数据,可以很容易地将其转换为bytearray。 # 将二进制数据转换为bytearr...
1. 2. 3. 3. 获取图片地址并转换为字节数组 下面的函数image_to_byte_array接收一个图片的URL,获取该图片并将其转换为字节数组。 AI检测代码解析 defimage_to_byte_array(url):response=requests.get(url)ifresponse.status_code!=200:raiseException("无法获取图片:HTTP状态码 {}".format(response.status_cod...
from PIL import Image import io def image_to_byte_array(image: Image) -> bytes: # BytesIO is a fake file stored in memory imgByteArr = io.BytesIO() # image.save expects a file as a argument, passing a bytes io ins image.save(imgByteArr, format=image.format) # Turn the BytesIO...
For example, say you are writing an image recognition library that processes images. As we know everything in computers is basically stored in 0’s and 1’s. You can receive these images as a ByteArray object and do the necessary processing. ByteArrays are a good way to pack and process...
flatNumpyArray = numpy.array(randomByteArray) # Convert the array to make a 400x300 grayscale image. grayImage = flatNumpyArray.reshape(300, 400) cv2.imwrite('RandomGray.png', grayImage) # Convert the array to make a 400x100 color image. ...
im=Image.fromarray(result*255.0) im.convert('L').save("1.jpg",format='jpeg') 这是我得到的128*256大小的灰度图 二、利用CV库 看这篇博客,这个方法和利用PIL库有异曲同工之处 主要步骤 1.生成普通python数组(bytearray(),os.urandom())
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fromPILimportImageimage_width,image_height=30,30# 填充占位数据image_bytes=bytearray([0x70,0x70,0x70])*image_width*image_heighti=0# 设置颜色渐变foryinrange(image_height):forxinrange(image_width):image_bytes[i]=int(255.0*(x/image_width))# Rimage_bytes[i+1]=int(255.0*(y/image_height)...
open(image_data) # 如果需要,可以将图片转换为特定模式(如灰度图) # image = image.convert('L') 使用OpenCV库 OpenCV是一个开源的计算机视觉库,也提供了将二进制数据转换为图片的功能。 python import numpy as np import cv2 # 将二进制数据转换为numpy数组 image_array = np.asarray(bytearray(binary_...
(path) import numpy as np from PIL import Image import matplotlib.pyplot as plt image_path = "/content/01_L.bmp" image = np.array(Image.open(image_path).convert("L")) # Convert to grayscale # Visualize the magnitude of coefficients magnitude_image = np.abs(scattering_coeffs[0]) # ...