有感於人工智能發展，現在開始學習Opencv關於計算機視覺的知識，又不想搗鼓C++代碼，因此決定用Python來搞，此篇開始按照官網的教程開始學習，記錄自己的學習歷程，寫一點筆記，方便以後查閱。
官方的教程：https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
GUI部分主要包括圖像處理、視頻處理、畫圖工具、GUI工具4個部分。

圖像處理（Getting Started with Images）

學習使用cv2.imread(), cv2.imshow() , cv2.imwrite()這三個函數，以及Matplotlib庫顯示圖像。

讀取圖像

cv2.imread(filename, flags=None)

import cv2
img = cv2.imread(r'/home/luke/圖片/2018-08-09 11-58-52.jpg',cv2.IMREAD_COLOR)
# filename參數u用於定位圖片，可以是當前路徑或絕對路徑，注意windows平臺，字符串前加 r
# 第二項參數的默認值是：cv2.IMREAD_COLOR，讀取色彩圖像，還可以是
# cv2.IMREAD_GRAYSCALE : 讀取灰度圖像
# cv2.IMREAD_UNCHANGED : 讀取圖像包含 alpha channel

顯示圖像

cv2.imshow() 函數將圖像顯示到窗口，窗口大小自動適應圖像大小。

cv2.imshow('image',img)
#參數1：窗口標題名稱
#參數2：已讀取的顯示圖像
cv2.waitKey(0)
# cv2.waitKey()函數等待任意按鍵， 
# 參數是等待超時時間，時間單位爲ms；參數爲0時，等待直到任意鍵按下，
# 返回值是按下的鍵值
cv2.destroyAllWindows()
# 銷燬所有打開的窗口

保存圖像

img = cv2.imread('/home/luke/圖片/2018-07-19 19-47-33屏幕截圖.png',0)
cv2.imshow('image',img)
k = cv2.waitKey(0)
#k = cv2.waitKey(0) & 0xFF
if k == 27:         # wait for ESC key to exit
    cv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exit
    cv2.imwrite('/home/luke/圖片/test.png',img)
    cv2.destroyAllWindows()

使用Matplotlib

使用Matplotlib庫顯示圖像、縮放圖像，保存圖像，關於這個庫的說明，需要另寫文章。以下是簡單示例

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('messi5.jpg',0)
plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
plt.show()
# 注意：OpenCV圖像存儲格式爲BGR模式，但是 Matplotlib 使用RGB模式；因此要想正確使用 Matplotlib庫必須先進行轉換。
# convert BGR image to RGB image
# cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img2 = img[:,:,::-1]

視頻處理（Getting Started with Videos）

主要學習函數：

cv2.VideoCapture(),
cv2.VideoWriter()

讀取攝像頭播放視頻

要播放攝像頭視頻，需要先捕捉攝像頭視頻流，創造一個VideoCapture對象。

import numpy as np
import cv2
cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture(/dev/video0)
#cap.read() returns a bool (True/False). If frame is read correctly, 
#it will be True. So you can check end of the video by checking this return value
while(True):
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Our operations on the frame come here
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Display the resulting frame
    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

cv2.VideoCapture()用於創建VideoCapture對象，參數可以使video設備序號，從0開始，也可以是設備名稱/dev/video0cap 對象代表的攝像頭節點可能還未被初始化，直接讀取可能會報錯可以使用cap.isOpened()函數判斷，返回值爲True，表示已打開；否則用cap.open()函數打開。
cap.get(propId)函數可以獲取視頻相關參數
cap.set(propId, vaule),設置視頻相關參數
propId取值範圍是0-21
CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.
CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.
CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.
CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.
CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.
CV_CAP_PROP_FPS Frame rate.
CV_CAP_PROP_FOURCC 4-character code of codec.
CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.
CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .
CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.
CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).
CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).
CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).
CV_CAP_PROP_HUE Hue of the image (only for cameras).
CV_CAP_PROP_GAIN Gain of the image (only for cameras).
CV_CAP_PROP_EXPOSURE Exposure (only for cameras).
CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.
CV_CAP_PROP_WHITE_BALANCE_U The U value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)
CV_CAP_PROP_WHITE_BALANCE_V The V value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)
CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)
CV_CAP_PROP_ISO_SPEED The ISO speed of the camera (note: only supported by DC1394 v 2.x backend currently)
CV_CAP_PROP_BUFFERSIZE Amount of frames stored in internal buffer memory (note: only supported by DC1394 v 2.x backend currently)

播放視頻文件

import numpy as np
import cv2

cap = cv2.VideoCapture('vtest.avi')
while(cap.isOpened()):
    ret, frame = cap.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    cv2.imshow('frame',gray)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

注意：Make sure proper versions of ffmpeg or gstreamer is installed. Sometimes, it is a headache to work with Video Capture mostly due to wrong installation of ffmpeg/gstreamer.

保存視頻文件

以下代碼一遍播放一遍保存視頻文件

import numpy as np
import cv2

cap = cv2.VideoCapture(0)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))
while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
        frame = cv2.flip(frame,0)
        # write the flipped frame，垂直方向翻轉
        out.write(frame)
        cv2.imshow('frame',frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()

cv2.VideoWriter()參數說明：

參數1：存儲的視頻文件名稱
參數2：視頻文件格式FourCC， 4-byte 的編碼格式，In Fedora: DIVX, XVID, MJPG, X264, WMV1, WMV2. (XVID is more preferable. MJPG results in high size video. X264 gives very small size video)
In Windows: DIVX (More to be tested and added)
參數3：視頻文件的幀率大小，每秒多少幀
參數4：元組格式(640, 320)，表示視頻文件畫面的大小

OpenCV畫圖函數

基本函數

cv2.line(),
cv2.circle() ,
cv2.rectangle(),
cv2.ellipse(),
cv2.putText()

基本參數

img : 被作圖的圖像載體
color : Color of the shape. for BGR, pass it as a tuple, eg: (255,0,0) for blue. For grayscale, just pass the scalar value.
thickness : Thickness of the line or circle etc. If -1 is passed for closed figures like circles, it will fill the shape. default thickness = 1
lineType : Type of line, whether 8-connected, anti-aliased line etc. By default, it is 8-connected. cv2.LINE_AA gives anti-aliased line which looks great for curves.

畫線

首先創建全黑圖像，然後給出起點與終點座標，線的顏色、厚度
以下函數畫一條從左上角到右下角的藍色線

import numpy as np
import cv2

# Create a black image
img = np.zeros((512,512,3), np.uint8)

# Draw a diagonal blue line with thickness of 5 px
img = cv2.line(img,(0,0),(511,511),(255,0,0),5)

畫矩形

聲明矩形的左上角座標、右下角座標

img = cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)

畫圓

聲明圓心點座標、半徑、顏色

img = cv2.circle(img,(447,63), 63, (0,0,255), -1)

畫橢圓

橢圓參數：

圓心座標 (x,y)
a、b長短軸長度 (major axis length, minor axis length)
作圖方向，逆時針，起點角度與終點角度 0 到 360 g畫出完整橢圓

img = cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1)

畫多邊形

多邊形參數：

多邊形定點座標

pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)
pts = pts.reshape((-1,1,2))
img = cv2.polylines(img,[pts],True,(0,255,255))

注意：If third argument is False, you will get a polylines joining all the points, not a closed shape.
注意：cv2.polylines() can be used to draw multiple lines. Just create a list of all the lines you want to draw and pass it to the function. All lines will be drawn individually. It is more better and faster way to draw a group of lines than calling cv2.line() for each line.

添加文字

向圖像添加文字需要聲明以下參數

文字內容
文字起點座標，起始位置爲左下角
字體類型
字體大小
其它參數：color, thickness, lineType， lineType = cv2.LINE_AA 推薦使用.

font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)

以上步驟最終圖像

鼠標作爲畫圖刷

學習函數：cv2.setMouseCallback()

簡單示例

1、首先創建鼠標事件回調函數，每一個鼠標事件給出一個座標(x,y)，根據此座標與對應的鼠標EVENT，灰回調函數可以實現任何事情
2、鼠標事件EVENT：['EVENT_FLAG_ALTKEY', 'EVENT_FLAG_CTRLKEY', 'EVENT_FLAG_LBUTTON', 'EVENT_FLAG_MBUTTON', 'EVENT_FLAG_RBUTTON', 'EVENT_FLAG_SHIFTKEY', 'EVENT_LBUTTONDBLCLK', 'EVENT_LBUTTONDOWN', 'EVENT_LBUTTONUP', 'EVENT_MBUTTONDBLCLK', 'EVENT_MBUTTONDOWN', 'EVENT_MBUTTONUP', 'EVENT_MOUSEHWHEEL', 'EVENT_MOUSEMOVE', 'EVENT_MOUSEWHEEL', 'EVENT_RBUTTONDBLCLK', 'EVENT_RBUTTONDOWN', 'EVENT_RBUTTONUP']
3、以下代碼，設置鼠標雙擊左鍵事件，以此事件的座標點換一個圓

import cv2
import numpy as np

# mouse callback function
def draw_circle(event,x,y,flags,param):
    if event == cv2.EVENT_LBUTTONDBLCLK:
        cv2.circle(img,(x,y),100,(255,0,0),-1)

# Create a black image, a window and bind the function to window
img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_circle)

while(1):
    cv2.imshow('image',img)
    if cv2.waitKey(20) & 0xFF == 27:
        break
cv2.destroyAllWindows()

高級示例

鼠標回調函數綁定多個事件，既可以畫圓也可以畫矩形

import cv2
import numpy as np

drawing = False # true if mouse is pressed
mode = True # if True, draw rectangle. Press 'm' to toggle to curve
ix,iy = -1,-1

# mouse callback function
def draw_circle(event,x,y,flags,param):
    global ix,iy,drawing,mode

    if event == cv2.EVENT_LBUTTONDOWN:
        drawing = True
        ix,iy = x,y

    elif event == cv2.EVENT_MOUSEMOVE:
        if drawing == True:
            if mode == True:
                cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)
            else:
                cv2.circle(img,(x,y),5,(0,0,255),-1)

    elif event == cv2.EVENT_LBUTTONUP:
        drawing = False
        if mode == True:
            cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1)
        else:
            cv2.circle(img,(x,y),5,(0,0,255),-1)

主循環檢測“m”鍵，是否按下，用以切換畫圓還是畫矩形
鼠標左鍵按下使能畫圖，並拖動開始畫圖

img = np.zeros((512,512,3), np.uint8)
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_circle)

while(1):
    cv2.imshow('image',img)
    k = cv2.waitKey(1) & 0xFF
    if k == ord('m'):
        mode = not mode
    elif k == 27:
        break

cv2.destroyAllWindows()

移動條調色板

主要學習函數

cv2.createTrackbar()
cv2.getTrackbarPos()，參數1，trackbar名稱，參數2，附着窗口名稱，參數3，默認值，參數4，最大值，參數5，回調函數，移動條value變化時出發執行，此示例中，只需trackbar的value值，因此回調函數什麼也不做
此示例有4個移動條，分別用來選擇R、G、B；以及On/Off選擇
trackbar的value取值只有0與1時，可以作爲switch開關，此示例中用來決定是否顯色調色的後的顏色，value爲0 時，調色板爲黑色

import cv2
import numpy as np

def nothing(x):
    pass

# Create a black image, a window
img = np.zeros((300,512,3), np.uint8)
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('R','image',0,255,nothing)
cv2.createTrackbar('G','image',0,255,nothing)
cv2.createTrackbar('B','image',0,255,nothing)

# create switch for ON/OFF functionality
switch = '0 : OFF \n1 : ON'
cv2.createTrackbar(switch, 'image',0,1,nothing)

while(1):
    cv2.imshow('image',img)
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break

    # get current positions of four trackbars
    r = cv2.getTrackbarPos('R','image')
    g = cv2.getTrackbarPos('G','image')
    b = cv2.getTrackbarPos('B','image')
    s = cv2.getTrackbarPos(switch,'image')

    if s == 0:
        img[:] = 0
    else:
        img[:] = [b,g,r]

cv2.destroyAllWindows()

好了，本片就這麼多，繼續學習！

Python進階之——OpenCV之GUI

文章目錄

圖像處理（Getting Started with Images）

讀取圖像

顯示圖像

保存圖像

使用Matplotlib

視頻處理（Getting Started with Videos）

讀取攝像頭播放視頻

播放視頻文件

保存視頻文件

OpenCV畫圖函數

畫線

畫矩形

畫圓

畫橢圓

畫多邊形

添加文字

以上步驟最終圖像

鼠標作爲畫圖刷

簡單示例

高級示例

移動條調色板

美團一面：項目中有 10000 個 if else 如何優化？想了半天，被問懵了！

京東面試：如何進行JVM調優？

Python 將PowerPoint (PPT/PPTX) 轉爲HTML

SQL優化-20231016

FFmpeg音視頻播放器系列（第二篇：音視頻播放同步）

Python進階——OpenCV之GUI

python進階—OpenCV之圖像特徵檢測與描述

QT之QTableWidget使用

QT之佈局

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

linux以太網驅動總結