文章目錄

• 影象處理（Getting Started with Images）
• 讀取影象
• 顯示影象
• 儲存影象
• 使用Matplotlib
• 視訊處理（Getting Started with Videos）
• 讀取攝像頭播放視訊
• 播放視訊檔案
• 儲存視訊檔案
• OpenCV畫圖函式
• 畫線
• 畫矩形
• 畫圓
• 畫橢圓
• 畫多邊形
• 新增文字
• 以上步驟最終影象
• 滑鼠作為畫圖刷
• 簡單示例
• 高階示例
• 移動條調色盤

有感於人工智慧發展，現在開始學習Opencv關於計算機視覺的知識，又不想搗鼓C++程式碼，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部分主要包括影象處理、視訊處理、畫圖工具、GUI工具4個部分。

影象處理（Getting Started with Images）

學習使用cv2.imread(), cv2.imshow() , cv2.imwrite()

這三個函式，以及Matplotlib庫顯示影象。

讀取影象

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引數u用於定點陣圖片，可以是當前路徑或絕對路徑，注意windows平臺，字串前加 r
# 第二項引數的預設值是：cv2.IMREAD_COLOR，讀取色彩影象，還可以是
# cv2.IMREAD_GRAYSCALE : 讀取灰度影象
# cv2.IMREAD_UNCHANGED : 讀取影象包含 alpha channel
 

顯示影象

cv2.imshow() 函式將影象顯示到視窗，視窗大小自動適應影象大小。

cv2.imshow('image',img)
#引數1：視窗標題名稱
#引數2：已讀取的顯示影象
cv2.waitKey(0)
# cv2.waitKey()函式等待任意按鍵， 
# 引數是等待超時時間，時間單位為ms；引數為0時，等待直到任意鍵按下，
# 返回值是按下的鍵值
cv2.destroyAllWindows()
# 銷燬所有開啟的視窗

儲存影象

img = cv2.imread('/home/luke/圖片/2018-07-19 19-47-33螢幕截圖.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庫顯示影象、縮放影象，儲存影象，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影象儲存格式為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]

視訊處理（Getting Started with Videos）

主要學習函式：

• cv2.VideoCapture(),
• cv2.VideoWriter()

讀取攝像頭播放視訊

要播放攝像頭視訊，需要先捕捉攝像頭視訊流，創造一個VideoCapture物件。

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()用於建立VideoCapture物件，引數可以使video裝置序號，從0開始，也可以是裝置名稱/dev/video0cap 物件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()函式判斷，返回值為True，表示已開啟；否則用cap.open()函式開啟。
• cap.get(propId)函式可以獲取視訊相關引數
• cap.set(propId, vaule),設定視訊相關引數
• 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)

播放視訊檔案

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.

儲存視訊檔案

以下程式碼一遍播放一遍儲存視訊檔案

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()引數說明：

• 引數1：儲存的視訊檔名稱
• 引數2：視訊檔案格式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)
• 引數3：視訊檔案的幀率大小，每秒多少幀
• 引數4：元組格式(640, 320)，表示視訊檔案畫面的大小

OpenCV畫圖函式

基本函式

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

基本引數

• img : 被作圖的影象載體
• 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

首先建立全黑影象，然後給出起點與終點座標，線的顏色、厚度
以下函式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

宣告矩形的左上角座標、右下角座標

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

畫圓

宣告圓心點座標、半徑、顏色

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

畫橢圓

橢圓引數：

• 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

多邊形引數：

• 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.

新增文字

向影象新增文字需要宣告以下引數

• 文字內容
• 文字起點座標，起始位置為左下角
• 字型型別
• 字型大小
• 其它引數：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)

以上步驟最終影象

滑鼠作為畫圖刷

學習函式：cv2.setMouseCallback()

簡單示例

• 1、首先建立滑鼠事件回撥函式，每一個滑鼠事件給出一個座標(x,y)，根據此座標與對應的滑鼠EVENT，灰回撥函式可以實現任何事情
• 2、滑鼠事件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、以下程式碼，設定滑鼠雙擊左鍵事件，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()

高階示例

滑鼠回撥函式繫結多個事件，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)

主迴圈檢測“m”鍵，是否按下，用以切換畫圓還是畫矩形
滑鼠左鍵按下使能畫圖，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()

移動條調色盤

主要學習函式

• cv2.createTrackbar()
• cv2.getTrackbarPos()，引數1，trackbar名稱，引數2，附著視窗名稱，引數3，預設值，引數4，最大值，引數5，回撥函式，移動條value變化時出發執行，此示例中，只需trackbar的value值，因此回撥函式什麼也不做
• 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，可以作為switch開關，此示例中用來決定是否顯色調色的後的顏色，value為0 時，調色盤為黑色

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！