def frame2base64(frame):
img = Image.fromarray(frame) #将每一帧转为Image
output_buffer = BytesIO() #创建一个BytesIO
img.save(output_buffer, format='JPEG') #写入output_buffer
byte_data = output_buffer.getvalue() #在内存中读取
# base64_data = base64.b64encode(byte_data) #转为BASE64
return byte_data #转码成功 返回base64编码
def tesseractCharacter(self):
self.plate_characters = sorted(self.plate_characters,
key=lambda x: x[0])
# sort contours left to right
for character in self.plate_characters[:8]: # only first 8 contours
char_image = Image.fromarray(character[1])
char = tes.image_to_string(char_image, config='-psm 10')
self.plate_number += char.upper()
return True
#! /usr/bin/env python3
from pillow import Image #图像处理模块
import numpy as np
a = np.asarray(Image.open("这里是原图片的路径").convert('L')).astype(
'float') #将图像以灰度图的方式打开并将数据转为float存入np中
depth = 10. # (0-100)
grad = np.gradient(a) #取图像灰度的梯度值
grad_x, grad_y = grad #分别取横纵图像梯度值
grad_x = grad_x * depth / 100.
grad_y = grad_y * depth / 100.
A = np.sqrt(grad_x**2 + grad_y**2 + 1.)
uni_x = grad_x / A
uni_y = grad_y / A
uni_z = 1. / A
#建立一个位于图像斜上方的虚拟光源
vec_el = np.pi / 2.2 # 光源的俯视角度，弧度值
vec_az = np.pi / 4. # 光源的方位角度，弧度值
dx = np.cos(vec_el) * np.cos(vec_az) #光源对x 轴的影响
dy = np.cos(vec_el) * np.sin(vec_az) #光源对y 轴的影响
dz = np.sin(vec_el) #光源对z 轴的影响
#计算各点新的像素值
b = 255 * (dx * uni_x + dy * uni_y + dz * uni_z) #光源归一化
b = b.clip(0, 255) #clip函数将区间外的数字剪除到区间边缘
im = Image.fromarray(b.astype('uint8')) #重构图像
im.save("这里是输出图片的路径")
