图像旋转角度计算并旋转

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import time

def Rotate(img, angle=0.0,fill=0):
    """
    旋转
    :param img:待旋转图像
    :param angle: 旋转角度
    :param fill：填充方式，默认0黑色填充
    :return: img: 旋转后的图像
    """
    w, h = img.shape[:2]
    center = (int(w / 2), int(h / 2))
    rot = cv2.getRotationMatrix2D(center, angle, 1.0)
    img = cv2.warpAffine(img, rot, (h, w), borderValue=fill)
    return img

def CalcAngle(img):
    h, w = img.shape[:2]
    x1, y1, x2, y2 = 0, 0, 0, 0
    angle = 0
    for i in range(h - 1):
        if img[i][int(w / 3)] == 0 and img[i - 1][int(w / 3)] != 0:
            # print("1",int(w/3),i)
            x1, y1 = int(w / 3), i
        if img[i][int(w * 2 / 3)] == 0 and img[i - 1][int(w * 2 / 3)] != 0:
            # print("2",int(w*2/3),i)
            x2, y2 = int(w * 2 / 3), i
        if x1 != 0 and y1 != 0 and x2 != 0 and y2 != 0:
            if x2 - x1 == 0 or y2 - y1 == 0:
                print(u"不需要旋转")
                return 0
            else:
                length = (y2 - y1) / (x2 - x1)
                angle = np.arctan(length) / 0.017453
                if angle < -45:
                    angle = angle + 90
                elif angle > 45:
                    angle = angle - 90
                else:
                    pass
                print(u"旋转角度：", angle)
                return angle
starts = time.clock()

img1=cv2.imread("box.jpg",0)
# img=Rotate(img1,2,255)
ret,img=cv2.threshold(img1,200,255,cv2.THRESH_BINARY)
# cv2.imshow("0",img)
img = cv2.Canny(img, 10, 255, apertureSize=3)
angle=CalcAngle(img)
img=Rotate(img1,angle)
ends = time.clock()
print("time", ends - starts, "秒")
cv2.imwrite("00.jpg",img)
# cv2.imshow("00",img)
cv2.waitKey(0)
cv2.destroyAllWindows()

使用两张测试图片如下:

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对于lena的图像测试结果如下:

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另一张测试图片结果如下:

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?也可以使用下面代码进行测试:

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import cv2
import time
import numpy as np

def Location(img, tmp, threshold_value=120, dilate=3, resize_multiple=16):
    """
    图像定位
    :param img: 输入原图
    :param tmp: 定位匹配模板
    :param threshold_value: 图像阈值
    :param dilate: 膨胀值
    :param resize_multiple:缩小倍率
    :return: rect:矩形坐标点，从右上xy到右下xy，四个值
    """
    h, w = img.shape[:2]
    hy, wx = tmp.shape[:2]
    img = cv2.resize(img, (int(w * 1 / resize_multiple), int(h * 1 / resize_multiple)), interpolation=cv2.INTER_AREA)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    img = cv2.erode(img, kernel, iterations=dilate)
    w, h = img.shape[:2]
    for i in range(w):
        for j in range(h):
            if img[i][j] >= threshold_value:
                img[i][j] = 255
            else:
                img[i][j] = 0
    res = cv2.matchTemplate(img, tmp, cv2.TM_SQDIFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    top_left = min_loc
    # bottom_right = ((top_left[0] + wx) * resize_multiple, (top_left[1] + hy) * resize_multiple)
    # top_left = (top_left[0] * resize_multiple, top_left[1] * resize_multiple)
    rect = [top_left[0] * resize_multiple, top_left[1] * resize_multiple, (top_left[0] + wx) * resize_multiple,
            (top_left[1] + hy) * resize_multiple]
    return rect


def RotateAngle(img, threshold_value=120, dilate=3,linenum=6):
    """
    计算图像旋转角度
    :param img: 输入图像
    :param threshold_value: 阈值分割
    :param dilate: 膨胀值
    :return: angle: 旋转角度
    """
    ret,img=cv2.threshold(img,threshold_value,255,cv2.THRESH_BINARY)
    img_w, img_h = img.shape[:2]
    # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 2))
    # img = cv2.erode(img, kernel, iterations=dilate)
    line_widthsize = int(img_w)
    line_lensize = int(img_h / linenum)
    edges = cv2.Canny(img, 10, 255, apertureSize=3)
    try:
        lines = cv2.HoughLinesP(edges, 1, np.pi / 180, line_lensize, minLineLength=int(line_widthsize / 2),
                                maxLineGap=line_widthsize)
        for line in lines[0]:
            # print("角度测量的直线坐标", line)
            x1, y1, x2, y2 = line
            if x2 - x1 == 0 or y2 - y1 == 0:
                print(u"不需要旋转")
                return 0
            else:
                length = (y2 - y1) / (x2 - x1)
                angle = np.arctan(length) / 0.017453
                if angle < -45:
                    angle = angle + 90
                elif angle > 45:
                    angle = angle - 90
                else:
                    pass
                print(u"旋转角度：", angle)
                return angle
    except:
        return 0

def Rotate(img, angle=0.0):
    """
    旋转
    :param img:待旋转图像
    :param angle: 旋转角度
    :return: img: 旋转后的图像
    """
    w, h = img.shape[:2]
    center = (int(w / 2), int(h / 2))
    rot = cv2.getRotationMatrix2D(center, angle, 1.0)
    img = cv2.warpAffine(img, rot, (h, w), borderValue=255)
    return img


def GetObject_Location(img, tmp, threshold_value=120, dilate=3, resize_multiple=16):
    """
    旋转
    :param img:图像
    :param tmp: 模板
    :param threshold_value:阈值
    :param dilate: 膨胀值
    :param resize_multiple:缩放倍数
    :return:
    """
    rect = Location(img, tmp, threshold_value, dilate, resize_multiple)
    imgout = img[rect[1]:rect[3], rect[0]:rect[2]]
    angle = RotateAngle(imgout, threshold_value, dilate, resize_multiple, linenum=6)
    img = Rotate(imgout, angle)
    return img


def SaveTemple(img, file_name=".\\data\\Temple1.jpg", threshold_value=200, dilate=3, resize_multiple=16):
    """
    模板生成存储
    :param img: 输入图像
    :param file_name: 模板保存地址
    :param threshold_value: 阈值分割
    :param dilate: 膨胀值
    :return: img: 保存模板图片到本地
    """
    h, w = img.shape[:2]
    img = cv2.resize(img, (int(w * 1 / resize_multiple), int(h * 1 / resize_multiple)), interpolation=cv2.INTER_AREA)
    img_w, img_h = img.shape[:2]
    print(img_w, img_h)
    # 创建标准模板
    imgout = np.zeros((img_w + 4, img_h + 4, 1), np.uint8)
    # 图像初始化白色
    for i in range(img_w + 4):
        for j in range(img_h + 4):
            imgout[i][j] = 255
    # 图像二值化
    for i in range(img_w):
        for j in range(img_h):
            if img[i][j] >= threshold_value:
                img[i][j] = 255
            else:
                img[i][j] = 0
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    img = cv2.erode(img, kernel, iterations=dilate)
    for i in range(img_w):
        for j in range(img_h):
            if img[i][j] >= threshold_value:
                pass
            else:
                imgout[i + 2][j + 2] = 0
    cv2.imwrite(file_name, imgout)


"""一次切割，根据投影切割"""


def FirstCutting(img, Cvalue, Cerode, LineNum, LineNum1):
    (_, thresh) = cv2.threshold(img, Cvalue, 255, cv2.THRESH_BINARY)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
    outimg = cv2.erode(thresh, kernel, iterations=Cerode)
    height, width = outimg.shape[:2]
    z = [0] * height
    v = [0] * width
    hfg = [[0 for col in range(2)] for row in range(height)]
    lfg = [[0 for col1 in range(2)] for row1 in range(width)]
    Box = []
    linea = 0
    BlackNumber = 0
    for y in range(height):
        for x in range(width):
            cp = outimg[y][x]
            if cp == 0:
                linea = linea + 1
                BlackNumber += 1
            else:
                continue
        z[y] = linea
        linea = 0

    inline, start, lineNumber = 1, 0, 0
    for i in range(0, height):
        if inline == 1 and z[i] >= LineNum:
            start = i
            inline = 0
        elif (i - start > 3) and z[i] < LineNum and inline == 0:
            inline = 1
            hfg[lineNumber][0] = start - 2  # 保存行的分割位置起始位置
            hfg[lineNumber][1] = i + 2  # 保存行的分割终点位置
            lineNumber = lineNumber + 1

    lineb = 0
    for p in range(0, lineNumber):
        for x in range(0, width):
            for y in range(hfg[p][0], hfg[p][1]):
                cp1 = outimg[y][x]
                if cp1 == 0:
                    lineb = lineb + 1
                else:
                    continue
            v[x] = lineb
            lineb = 0
        incol, start1, lineNumber1 = 1, 0, 0
        z1 = hfg[p][0]
        z2 = hfg[p][1]
        for i1 in range(0, width):
            if incol == 1 and v[i1] >= LineNum1:
                start1 = i1
                incol = 0
            elif (i1 - start1 > 3) and v[i1] < LineNum1 and incol == 0:
                incol = 1
                lfg[lineNumber1][0] = start1 - 3
                lfg[lineNumber1][1] = i1 + 3
                l1 = start1 - 3
                l2 = i1 + 3
                tmp = [l1, z1, l2, z2]
                Box.append(tmp)
                lineNumber1 = lineNumber1 + 1
                # outimg=cv2.rectangle(outimg,(l1,z1),(l2,z2),(0,255,0),1)
    return Box, BlackNumber, outimg


def Threshold(img, threshold, KernelValue=3, KernelValue1=(1, 1)):
    """
    根据阈值框选
    :param img:输入待处理的图像
    :param threshold:阈值
    :param KernelValue:卷积核
    :return:outimg:输出处理后的图像
    """
    w, h = img.shape[:2]
    for i in range(w):
        for j in range(h):
            """通过设置阈值，来控制喷码花的程度"""
            if img[i][j] >= threshold:
                img[i][j] = 255
            else:
                img[i][j] = 0
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, KernelValue1)
    outimg = cv2.erode(img, kernel, iterations=KernelValue)
    outimg = cv2.dilate(outimg, kernel, iterations=KernelValue)
    return outimg


"""根据投影计算出来的坐标进行数组切割"""

starts = time.clock()
img = cv2.imread("lena.jpg", 0)
# img=Rotate(img,2)
angle=RotateAngle(img,200)
print(angle)
img=Rotate(img,angle)
cv2.imwrite("00.jpg",img)
ends = time.clock()
print("time", ends - starts, "秒")

# img=cv2.imread("formal.bmp",0)
# SaveTemple(img)

lena结果如下：

美女图片测试结果:

说明:以上代码仅仅是讲解介绍了图像旋转的计算及矫正原理，实际上准确度受不同图像的影响较大，不过里面使用的相关图像变换的函数值得借鉴参考学习。

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