图像旋转，背景指定像素填充

20190802更新： 统计待旋转图像画面中像素值最多的为背景点

#coding=utf-8
import cv2
import os
from math import *
import numpy as np


def get_background(srcimg):
    gray = cv2.cvtColor(srcimg,cv2.COLOR_BGR2GRAY)
    hest = np.zeros([256],dtype=np.int32)

    hs = gray.shape[0]
    ws = gray.shape[1]
    for h in range(0,hs):
        for w in range(0,ws):
            pix = gray[h,w]
            hest[pix] += 1

    idx = np.where(hest == np.max(hest))
    idxx = idx[0][0]
    for h in range(0,hs):
        for w in range(0,ws):
            pix = gray[h,w]
            if idxx == pix:
                return (int(srcimg[h,w,0]),int(srcimg[h,w,1]),int(srcimg[h,w,2]))


def rotate_bound_white_bg(image, angle):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)
    pix_border = get_background(image)

    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (i.e., the rotation components of the matrix)
    # -angle位置参数为角度参数负值表示顺时针旋转; 1.0位置参数scale是调整尺寸比例（图像缩放参数），建议0.75
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])

    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))

    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY

    # perform the actual rotation and return the image
    # borderValue 缺失背景填充色彩，此处为白色，可自定义
    return cv2.warpAffine(image, M, (nW, nH),
                          borderValue=pix_border)  # return cv2.warpAffine(image, M, (nW, nH),borderValue=(0,255,255))
    # borderValue 缺省，默认是黑色（0, 0 , 0）
    # return cv2.warpAffine(image, M, (nW, nH))


root = "/media/data_1/2019_project_test/PSENet/data/CTW1500/train/mark/"
list_hz = os.listdir(root)
for img_name in list_hz:
    img_path = root + img_name

    img = cv2.imread(img_path)
    imgRotation = rotate_bound_white_bg(img, 45)
    cv2.imshow("img", img)
    cv2.imshow("imgRotation", imgRotation)
    cv2.waitKey(0)

# -*- coding: utf-8 -*-
 
import cv2
import os
from math import *
import numpy as np

def get_pix_background(img):
    T = 15
    height = img.shape[0]
    width = img.shape[1]
    channels = img.shape[2]
    for h in range(height):
        for w in range(width):
            b = img[h,w,0]
            g = img[h, w, 1]
            r = img[h, w, 2]
            if abs(b-g)<T and abs(b-r)<T and abs(g-r)<T:
                return (int(b),int(g),int(r))
    return (int(img[1,1,0]),int(img[1,1,1]),int(img[1,1,2]))



 
# 旋转angle角度，缺失背景白色（255, 255, 255）填充
def rotate_bound_white_bg(image, angle):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)
    pix_border = get_pix_background(image)
 
    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (i.e., the rotation components of the matrix)
    # -angle位置参数为角度参数负值表示顺时针旋转; 1.0位置参数scale是调整尺寸比例（图像缩放参数），建议0.75
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
 
    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
 
    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
 
    # perform the actual rotation and return the image
    # borderValue 缺失背景填充色彩，此处为白色，可自定义
    return cv2.warpAffine(image, M, (nW, nH), borderValue=pix_border) #return cv2.warpAffine(image, M, (nW, nH),borderValue=(0,255,255))
    # borderValue 缺省，默认是黑色（0, 0 , 0）
    # return cv2.warpAffine(image, M, (nW, nH))

root = "/media/data_2/everyday/0725/hz/"
list_hz = os.listdir(root)
for img_name in list_hz:
    img_path = root + img_name

    img = cv2.imread(img_path)
    imgRotation = rotate_bound_white_bg(img, 45)
    cv2.imshow("img", img)
    cv2.imshow("imgRotation", imgRotation)
    cv2.waitKey(0)

随机角度，批量操作

# -*- coding: utf-8 -*-
 
import cv2
import os
from math import *
import numpy as np
import random

def get_pix_background(img):
    T = 15
    height = img.shape[0]
    width = img.shape[1]
    channels = img.shape[2]
    for h in range(height):
        for w in range(width):
            b = img[h,w,0]
            g = img[h, w, 1]
            r = img[h, w, 2]
            if r<30 and b<30 and g<30:
                continue
            if abs(b-g)<T and abs(b-r)<T and abs(g-r)<T:
                return (int(b),int(g),int(r))
    return (int(img[1,1,0]),int(img[1,1,1]),int(img[1,1,2]))

# 旋转angle角度，缺失背景白色（255, 255, 255）填充
def rotate_bound_white_bg(image, angle):
    # grab the dimensions of the image and then determine the
    # center
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)
    pix_border = get_pix_background(image)
 
    # grab the rotation matrix (applying the negative of the
    # angle to rotate clockwise), then grab the sine and cosine
    # (i.e., the rotation components of the matrix)
    # -angle位置参数为角度参数负值表示顺时针旋转; 1.0位置参数scale是调整尺寸比例（图像缩放参数），建议0.75
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
 
    # compute the new bounding dimensions of the image
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
 
    # adjust the rotation matrix to take into account translation
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
 
    # perform the actual rotation and return the image
    # borderValue 缺失背景填充色彩，此处为白色，可自定义
    return cv2.warpAffine(image, M, (nW, nH), borderValue=pix_border) #return cv2.warpAffine(image, M, (nW, nH),borderValue=(0,255,255))
    # borderValue 缺省，默认是黑色（0, 0 , 0）
    # return cv2.warpAffine(image, M, (nW, nH))

root = "/media/data_2/everyday/0725/hegezhang/hz_hege/"
root_save = "/media/data_2/everyday/0725/hegezhang/save/"
list_hz = os.listdir(root)
for img_name in list_hz:
    img_path = root + img_name
    img = cv2.imread(img_path)
    for cnt in range(4):
        ang = random.randint(3,350)
        print("img=%s,  ang=%d"%(img_name,ang))
        imgRotation = rotate_bound_white_bg(img, ang)
        new_name = img_name.replace('.jpg',"_"+str(ang) + '.jpg')
        cv2.imwrite(root_save + new_name,imgRotation)

        # cv2.imshow("img", img)
        # cv2.imshow("imgRotation", imgRotation)
        # cv2.waitKey(0)