Python OpenCV实现鼠标画框

使用Python+OpenCV实现鼠标画框的代码：

# -*-coding: utf-8 -*-
"""
    @Project: IntelligentManufacture
    @File   : user_interaction.py
    @Author : panjq
    @E-mail : pan_jinquan@163.com
    @Date   : 2019-02-21 15:03:18
"""
# -*- coding: utf-8 -*-
import cv2
from utils import image_processing
import numpy as np
global img
global point1, point2
global g_rect
def on_mouse(event, x, y, flags, param):
    global img, point1, point2,g_rect
    img2 = img.copy()
    if event == cv2.EVENT_LBUTTONDOWN:  # 左键点击,则在原图打点
        print("1-EVENT_LBUTTONDOWN")
        point1 = (x, y)
        cv2.circle(img2, point1, 10, (0, 255, 0), 5)
        cv2.imshow('image', img2)
    elif event == cv2.EVENT_MOUSEMOVE and (flags & cv2.EVENT_FLAG_LBUTTON):  # 按住左键拖曳，画框
        print("2-EVENT_FLAG_LBUTTON")
        cv2.rectangle(img2, point1, (x, y), (255, 0, 0), thickness=2)
        cv2.imshow('image', img2)
    elif event == cv2.EVENT_LBUTTONUP:  # 左键释放，显示
        print("3-EVENT_LBUTTONUP")
        point2 = (x, y)
        cv2.rectangle(img2, point1, point2, (0, 0, 255), thickness=2)
        cv2.imshow('image', img2)
        if point1!=point2:
            min_x = min(point1[0], point2[0])
            min_y = min(point1[1], point2[1])
            width = abs(point1[0] - point2[0])
            height = abs(point1[1] - point2[1])
            g_rect=[min_x,min_y,width,height]
            cut_img = img[min_y:min_y + height, min_x:min_x + width]
            cv2.imshow('ROI', cut_img)
def get_image_roi(rgb_image):
    '''
    获得用户ROI区域的rect=[x,y,w,h]
    :param rgb_image:
    :return:
    '''
    bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    global img
    img=bgr_image
    cv2.namedWindow('image')
    while True:
        cv2.setMouseCallback('image', on_mouse)
        # cv2.startWindowThread()  # 加在这个位置
        cv2.imshow('image', img)
        key=cv2.waitKey(0)
        if key==13 or key==32:#按空格和回车键退出
            break
    cv2.destroyAllWindows()
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return g_rect
def select_user_roi(image_path):
    '''
    由于原图的分辨率较大，这里缩小后获取ROI，返回时需要重新scale对应原图
    :param image_path:
    :return:
    '''
    orig_image = image_processing.read_image(image_path)
    orig_shape = np.shape(orig_image)
    resize_image = image_processing.resize_image(orig_image, resize_height=800,resize_width=None)
    re_shape = np.shape(resize_image)
    g_rect=get_image_roi(resize_image)
    orgi_rect = image_processing.scale_rect(g_rect, re_shape,orig_shape)
    roi_image=image_processing.get_rect_image(orig_image,orgi_rect)
    image_processing.cv_show_image("RECT",roi_image)
    image_processing.show_image_rect("image",orig_image,orgi_rect)
    return orgi_rect
if __name__ == '__main__':
    # image_path="../dataset/images/IMG_0007.JPG"
    image_path="../dataset/test_images/lena.jpg"
    # rect=get_image_roi(image)
    rect=select_user_roi(image_path)
    print(rect)

其中image_processing.py文件如下：

# -*-coding: utf-8 -*-
"""
    @Project: IntelligentManufacture
    @File   : image_processing.py
    @Author : panjq
    @E-mail : pan_jinquan@163.com
    @Date   : 2019-02-14 15:34:50
"""
import os
import glob
import cv2
import numpy as np
import matplotlib.pyplot as plt
def show_image(title, image):
    '''
    调用matplotlib显示RGB图片
    :param title: 图像标题
    :param image: 图像的数据
    :return:
    '''
    # plt.figure("show_image")
    # print(image.dtype)
    plt.imshow(image)
    plt.axis('on')  # 关掉坐标轴为 off
    plt.title(title)  # 图像题目
    plt.show()
def cv_show_image(title, image):
    '''
    调用OpenCV显示RGB图片
    :param title: 图像标题
    :param image: 输入RGB图像
    :return:
    '''
    channels=image.shape[-1]
    if channels==3:
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # 将BGR转为RGB
    cv2.imshow(title,image)
    cv2.waitKey(0)
def read_image(filename, resize_height=None, resize_width=None, normalization=False):
    '''
    读取图片数据,默认返回的是uint8,[0,255]
    :param filename:
    :param resize_height:
    :param resize_width:
    :param normalization:是否归一化到[0.,1.0]
    :return: 返回的RGB图片数据
    '''
    bgr_image = cv2.imread(filename)
    # bgr_image = cv2.imread(filename,cv2.IMREAD_IGNORE_ORIENTATION|cv2.IMREAD_COLOR)
    if bgr_image is None:
        print("Warning:不存在:{}", filename)
        return None
    if len(bgr_image.shape) == 2:  # 若是灰度图则转为三通道
        print("Warning:gray image", filename)
        bgr_image = cv2.cvtColor(bgr_image, cv2.COLOR_GRAY2BGR)
    rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)  # 将BGR转为RGB
    # show_image(filename,rgb_image)
    # rgb_image=Image.open(filename)
    rgb_image = resize_image(rgb_image,resize_height,resize_width)
    rgb_image = np.asanyarray(rgb_image)
    if normalization:
        # 不能写成:rgb_image=rgb_image/255
        rgb_image = rgb_image / 255.0
    # show_image("src resize image",image)
    return rgb_image
def resize_image(image,resize_height, resize_width):
    '''
    :param image:
    :param resize_height:
    :param resize_width:
    :return:
    '''
    image_shape=np.shape(image)
    height=image_shape[0]
    width=image_shape[1]
    if (resize_height is None) and (resize_width is None):#错误写法：resize_height and resize_width is None
        return image
    if resize_height is None:
        resize_height=int(height*resize_width/width)
    elif resize_width is None:
        resize_width=int(width*resize_height/height)
    image = cv2.resize(image, dsize=(resize_width, resize_height))
    return image
def scale_image(image,scale):
    '''
    :param image:
    :param scale: (scale_w,scale_h)
    :return:
    '''
    image = cv2.resize(image,dsize=None, fx=scale[0],fy=scale[1])
    return image
def get_rect_image(image,rect):
    '''
    :param image:
    :param rect: [x,y,w,h]
    :return:
    '''
    x, y, w, h=rect
    cut_img = image[y:(y+ h),x:(x+w)]
    return cut_img
def scale_rect(orig_rect,orig_shape,dest_shape):
    '''
    对图像进行缩放时，对应的rectangle也要进行缩放
    :param orig_rect: 原始图像的rect=[x,y,w,h]
    :param orig_shape: 原始图像的维度shape=[h,w]
    :param dest_shape: 缩放后图像的维度shape=[h,w]
    :return: 经过缩放后的rectangle
    '''
    new_x=int(orig_rect[0]*dest_shape[1]/orig_shape[1])
    new_y=int(orig_rect[1]*dest_shape[0]/orig_shape[0])
    new_w=int(orig_rect[2]*dest_shape[1]/orig_shape[1])
    new_h=int(orig_rect[3]*dest_shape[0]/orig_shape[0])
    dest_rect=[new_x,new_y,new_w,new_h]
    return dest_rect
def show_image_rect(win_name,image,rect):
    '''
    :param win_name:
    :param image:
    :param rect:
    :return:
    '''
    x, y, w, h=rect
    point1=(x,y)
    point2=(x+w,y+h)
    cv2.rectangle(image, point1, point2, (0, 0, 255), thickness=2)
    cv_show_image(win_name, image)
def rgb_to_gray(image):
    image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    return image
def save_image(image_path, rgb_image,toUINT8=True):
    if toUINT8:
        rgb_image = np.asanyarray(rgb_image * 255, dtype=np.uint8)
    if len(rgb_image.shape) == 2:  # 若是灰度图则转为三通道
        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_GRAY2BGR)
    else:
        bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
    cv2.imwrite(image_path, bgr_image)
def combime_save_image(orig_image, dest_image, out_dir,name,prefix):
    '''
    命名标准：out_dir/name_prefix.jpg
    :param orig_image:
    :param dest_image:
    :param image_path:
    :param out_dir:
    :param prefix:
    :return:
    '''
    dest_path = os.path.join(out_dir, name + "_"+prefix+".jpg")
    save_image(dest_path, dest_image)
    dest_image = np.hstack((orig_image, dest_image))
    save_image(os.path.join(out_dir, "{}_src_{}.jpg".format(name,prefix)), dest_image)
if __name__=="__main__":
    image_path="../dataset/test_images/src.jpg"
    image = read_image(image_path, resize_height=None, resize_width=None)
    image = rgb_to_gray(image)
    orig_shape=np.shape(image)#shape=(h,w)
    orig_rect=[50,100,100,200]#x,y,w,h
    print("orig_shape:{}".format(orig_shape))
    show_image_rect("orig",image,orig_rect)
    dest_image=resize_image(image,resize_height=None,resize_width=200)
    dest_shape=np.shape(dest_image)
    print("dest_shape:{}".format(dest_shape))
    dest_rect=scale_rect(orig_rect, orig_shape, dest_shape)
    show_image_rect("dest",dest_image,dest_rect)