Python OpenCV实现鼠标画框效果

使用Python+OpenCV实现鼠标画框的代码，供大家参考，具体内容如下

# -*-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)

到此这篇关于Python OpenCV实现鼠标画框效果就介绍到这了。花季雨季的我们，应该珍爱青春，敞开心扉，感受多彩生命，编织人生梦想，实现精神成长。来吧，更多相关Python OpenCV实现鼠标画框效果内容请查看相关栏目，小编编辑不易，再次感谢大家的支持！