职业照--蓝底照片转换为白底

1、职业照–蓝底照片转换为白底

import cv2
import numpy as np
img=cv2.imread('C:/Users/Desktop/1.jpg') #原始图片
#缩放
rows,cols,channels = img.shape
img=cv2.resize(img,None,fx=0.5,fy=0.5)
rows,cols,channels = img.shape
cv2.imshow('img',img)
#转换hsv
hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
lower_blue=np.array([78,43,46])
upper_blue=np.array([110,255,255])
mask = cv2.inRange(hsv, lower_blue, upper_blue)
#cv2.imshow('Mask', mask)
#腐蚀膨胀
erode=cv2.erode(mask,None,iterations=1)
#cv2.imshow('erode',erode)
dilate=cv2.dilate(erode,None,iterations=1)
#cv2.imshow('dilate',dilate)
#遍历替换
for i in range(rows):for j in range(cols):if dilate[i,j]==255:# img[i,j]=(255,255,255)#此处替换颜色，为BGR通道 白底img[i, j] = (0, 0, 255)  # 此处替换颜色，为BGR通道 红底
cv2.imwrite('C:/Users/Desktop/red.jpg',img) #生成的新图片
cv2.waitKey(0)
cv2.destroyAllWindows()

2、将bmp格式的图片批量转成jpg

# 功能描述： 将bmp格式的图片批量转成jpg
# 注意：路径不能包含中文格式import os
import cv2
# 图片的路径
bmp_dir = 'C:/Users/Desktop/Data'
jpg_dir = 'C:/Users/Desktop/Data1'filelists = os.listdir(bmp_dir)for i,file in enumerate(filelists):# 读图，-1为不改变图片格式，0为灰度图img = cv2.imread(os.path.join(bmp_dir,file),-1)newName = file.replace('.bmp','.jpg')cv2.imwrite(os.path.join(jpg_dir,newName),img)print('第%d张图：%s'%(i+1,newName))

3、将批量图片合成一段视频

import os
import cv2
file_dir='E:/project_python/opencv_python/pic1/'
list=[]
for root,dirs,files in os.walk(file_dir):for file in files:list.append(file)  #获取目录下文件名列表video=cv2.VideoWriter('E:/project_python/opencv_python/pic1/test.avi',cv2.VideoWriter_fourcc(*'MJPG'),10,(1280,720))  #定义保存视频目录名称及压缩格式，fps=10,像素为1280*720
for i in range(1,len(list)):img=cv2.imread('E:/project_python/opencv_python/pic1/'+list[i-1])  #读取图片img=cv2.resize(img,(1280,720)) #将图片转换为1280*720video.write(img)   #写入视频video.release()

4、将一段视频处理成批量图片

import cv2
mp4 = cv2.VideoCapture("video.mp4")  # 读取视频
is_opened = mp4.isOpened()  # 判断是否打开
print(is_opened)
fps = mp4.get(cv2.CAP_PROP_FPS)  # 获取视频的帧率
print(fps)
widght = mp4.get(cv2.CAP_PROP_FRAME_WIDTH)  # 获取视频的宽度
height = mp4.get(cv2.CAP_PROP_FRAME_HEIGHT)  # 获取视频的高度
print(str(widght) + "x" + str(height))
i = 0
while is_opened:if i == 13:  # 截取前15张图片breakelse:i += 1(flag, frame) = mp4.read()  # 读取图片file_name = "iamge" + str(i) + ".jpg"print(file_name)if flag == True:cv2.imwrite(file_name, frame, [cv2.IMWRITE_JPEG_QUALITY])  # 保存图片
print("转换完成")

5、两张图片叠加放在一起，调整透明度

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img1= cv.imread('C:/Users/Desktop/1.jpg')
img2= cv.imread('C:/Users/Desktop/2.jpg')dst = cv.addWeighted(img1,0.62,img2,0.38,0)cv.imshow('new_pic',dst)
cv.waitKey(0)
cv.destroyAllWindows()

6、数据增强

# -*- coding:utf-8 -*-
"""数据增强1. 翻转变换 flip2. 随机修剪 random crop3. 色彩抖动 color jittering4. 平移变换 shift5. 尺度变换 scale6. 对比度变换 contrast7. 噪声扰动 noise8. 旋转变换/反射变换 Rotation/reflection
"""from PIL import Image, ImageEnhance, ImageOps, ImageFile
import numpy as np
import random
import threading, os, time
import logginglogger = logging.getLogger(__name__)
ImageFile.LOAD_TRUNCATED_IMAGES = Trueclass DataAugmentation:"""包含数据增强的八种方式"""def __init__(self):pass@staticmethoddef openImage(image):return Image.open(image, mode="r")@staticmethoddef randomRotation(image, mode=Image.BICUBIC):"""对图像进行随机任意角度(0~360度)旋转:param mode 邻近插值,双线性插值,双三次B样条插值(default):param image PIL的图像image:return: 旋转转之后的图像"""random_angle = np.random.randint(1, 360)return image.rotate(random_angle, mode)@staticmethoddef randomCrop(image):"""对图像随意剪切,考虑到图像大小范围(68,68),使用一个一个大于(36*36)的窗口进行截图:param image: PIL的图像image:return: 剪切之后的图像"""image_width = image.size[0]image_height = image.size[1]crop_win_size = np.random.randint(40, 68)random_region = ((image_width - crop_win_size) >> 1, (image_height - crop_win_size) >> 1, (image_width + crop_win_size) >> 1,(image_height + crop_win_size) >> 1)return image.crop(random_region)@staticmethoddef randomColor(image):"""对图像进行颜色抖动:param image: PIL的图像image:return: 有颜色色差的图像image"""random_factor = np.random.randint(0, 31) / 10.  # 随机因子color_image = ImageEnhance.Color(image).enhance(random_factor)  # 调整图像的饱和度random_factor = np.random.randint(10, 21) / 10.  # 随机因子brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor)  # 调整图像的亮度random_factor = np.random.randint(10, 21) / 10.  # 随机因1子contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor)  # 调整图像对比度random_factor = np.random.randint(0, 31) / 10.  # 随机因子return ImageEnhance.Sharpness(contrast_image).enhance(random_factor)  # 调整图像锐度@staticmethoddef randomGaussian(image, mean=0.2, sigma=0.3):"""对图像进行高斯噪声处理:param image::return:"""def gaussianNoisy(im, mean=0.2, sigma=0.3):"""对图像做高斯噪音处理:param im: 单通道图像:param mean: 偏移量:param sigma: 标准差:return:"""for _i in range(len(im)):im[_i] += random.gauss(mean, sigma)return im# 将图像转化成数组img = np.asarray(image)img.flags.writeable = True  # 将数组改为读写模式width, height = img.shape[:2]img_r = gaussianNoisy(img[:, :, 0].flatten(), mean, sigma)img_g = gaussianNoisy(img[:, :, 1].flatten(), mean, sigma)img_b = gaussianNoisy(img[:, :, 2].flatten(), mean, sigma)img[:, :, 0] = img_r.reshape([width, height])img[:, :, 1] = img_g.reshape([width, height])img[:, :, 2] = img_b.reshape([width, height])return Image.fromarray(np.uint8(img))@staticmethoddef saveImage(image, path):image.save(path)def makeDir(path):try:if not os.path.exists(path):if not os.path.isfile(path):# os.mkdir(path)os.makedirs(path)return 0else:return 1# except Exception, e:except Exception as e:print(str(e))return -2def imageOps(func_name, image, des_path, file_name, times=5):funcMap = {"randomRotation": DataAugmentation.randomRotation,"randomCrop": DataAugmentation.randomCrop,"randomColor": DataAugmentation.randomColor,"randomGaussian": DataAugmentation.randomGaussian}if funcMap.get(func_name) is None:logger.error("%s is not exist", func_name)return -1for _i in range(0, times, 1):new_image = funcMap[func_name](image)DataAugmentation.saveImage(new_image, os.path.join(des_path, func_name + str(_i) + file_name))opsList = {"randomRotation", "randomCrop", "randomColor", "randomGaussian"}def threadOPS(path, new_path):"""多线程处理事务:param src_path: 资源文件:param des_path: 目的地文件:return:"""if os.path.isdir(path):img_names = os.listdir(path)print("------------------")else:img_names = [path]img_names = os.listdir(path)print("==================")for img_name in img_names:print(img_name)tmp_img_name = os.path.join(path, img_name)print("+++++++++++++++++++++++++++++++++++++++++++++++++++++")print(tmp_img_name)print(tmp_img_name.split('.'))if os.path.isdir(tmp_img_name):if makeDir(os.path.join(new_path, img_name)) != -1:threadOPS(tmp_img_name, os.path.join(new_path, img_name))else:print('create new dir failure')return -1# os.removedirs(tmp_img_name)elif tmp_img_name.split('.')[1] != "DS_Store":# 读取文件并进行操作image = DataAugmentation.openImage(tmp_img_name)threadImage = [0] * 5_index = 0for ops_name in opsList:threadImage[_index] = threading.Thread(target=imageOps,args=(ops_name, image, new_path, img_name,))threadImage[_index].start()_index += 1time.sleep(0.2)if __name__ == '__main__':threadOPS("C:/Users/Desktop/Data1", "C:/Users/out_img1")

7、画图

画图一
import numpy as np
import cv2 as cv
# 创建一个黑色的图像
img = np.zeros((512,512,3), np.uint8)
# 画一条 5px 宽的蓝色对角线
cv.line(img,(0,0),(511,511),(255,0,0),5)
#画矩形
cv.rectangle(img,(384,0),(510,128),(0,255,0),3)
#画圆
cv.circle(img,(447,63), 63, (0,0,255), -1)
#画多边形
pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)
pts = pts.reshape((-1,1,2))
cv.polylines(img,[pts],True,(0,255,255))
cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()画图二
import numpy as np
import cv2 as cv
# 鼠标回调函数
def draw_circle(event,x,y,flags,param):if event == cv.EVENT_LBUTTONDBLCLK:cv.circle(img,(x,y),100,(255,0,0),-1)
# 创建一个黑色图像，一个窗口，然后和回调绑定
img = np.zeros((512,512,3), np.uint8)
cv.namedWindow('image')
cv.setMouseCallback('image',draw_circle)
while(1):cv.imshow('image',img)if cv.waitKey(20) & 0xFF == 27:break
cv.destroyAllWindows()画图三
import numpy as np
import cv2 as cv
drawing = False # 如果 True 是鼠标按下
mode = True # 如果 True，画矩形，按下‘m’切换到曲线
ix,iy = -1,-1
# 鼠标回调函数
def draw_circle(event,x,y,flags,param):global ix,iy,drawing,modeif event == cv.EVENT_LBUTTONDOWN:drawing = Trueix,iy = x,yelif event == cv.EVENT_MOUSEMOVE:if drawing == True:if mode == True:cv.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)else:cv.circle(img,(x,y),5,(0,0,255),-1)elif event == cv.EVENT_LBUTTONUP:drawing = Falseif mode == True:cv.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)else:cv.circle(img,(x,y),5,(0,0,255),-1)img = np.zeros((512,512,3), np.uint8)
cv.namedWindow('image')
cv.setMouseCallback('image',draw_circle)
while(1):cv.imshow('image',img)k = cv.waitKey(1) & 0xFFif k == ord('m'):mode = not modeelif k == 27:break
cv.destroyAllWindows()
画图四
import numpy as np
import cv2 as cv
def nothing(x):pass
# 创建一个黑色图像，一个窗口
img = np.zeros((300,512,3), np.uint8)
cv.namedWindow('image')
# 创建一个改变颜色的轨迹栏
cv.createTrackbar('R','image',0,255,nothing)
cv.createTrackbar('G','image',0,255,nothing)
cv.createTrackbar('B','image',0,255,nothing)
# 创建一个开关用来启用和关闭功能的
switch = '0 : OFF \n1 : ON'
cv.createTrackbar(switch, 'image',0,1,nothing)
while(1):cv.imshow('image',img)k = cv.waitKey(1) & 0xFFif k == 27:break# get current positions of four trackbarsr = cv.getTrackbarPos('R','image')g = cv.getTrackbarPos('G','image')b = cv.getTrackbarPos('B','image')s = cv.getTrackbarPos(switch,'image')if s == 0:img[:] = 0else:img[:] = [b,g,r]
cv.destroyAllWindows()
画图五
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
BLUE = [255,0,0]
img1 = cv.imread('C:/Users/Desktop/1.jpg')
replicate = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_REPLICATE)
reflect = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_REFLECT)
reflect101 = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_REFLECT_101)
wrap = cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_WRAP)
constant= cv.copyMakeBorder(img1,10,10,10,10,cv.BORDER_CONSTANT,value=BLUE)
plt.subplot(231),plt.imshow(img1,'gray'),plt.title('ORIGINAL')
plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')
plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT')
plt.subplot(234),plt.imshow(reflect101,'gray'),plt.title('REFLECT_101')
plt.subplot(235),plt.imshow(wrap,'gray'),plt.title('WRAP')
plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT')
plt.show()

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职业照--蓝底照片转换为白底

1、职业照–蓝底照片转换为白底

2、将bmp格式的图片批量转成jpg

3、将批量图片合成一段视频

4、将一段视频处理成批量图片

5、两张图片叠加放在一起，调整透明度

6、数据增强

7、画图

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