在OpenCV中設定ROI外的背景顏色

我成功顯示了視頻源，并試圖將 ROI 外區域的背景顏色從黑色更改為藍色，但螢屏仍顯示黑色背景。請幫我解決問題。任何幫助將不勝感激。

原碼

import numpy as np
from cv2 import cv2
'''
ML object detection algo(haarcascade)used to identify objects. 
the XML file consists of trained Haar Cascade models.
'''
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#initialize video from the webcam
video = cv2.VideoCapture(1)

while True:
# ret tells if the camera works properly. Frame is an actual frame from the video feed
    ret, frame= video.read()
    # print(cv2.VideoCapture(0).isOpened())
    # make sure port is working and read the image
    if frame is not None and video.isOpened():
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # Detect the faces within the subregions of the image in scales
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6)
        # Draw the rectangle around each face
        for (x, y, w, h) in faces:
            #Use the coordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
            center_coordinates = x   w // 2, y   h // 2
            radius = w // 2 # or can be h / 2 or can be anything based on your requirements
            #background color(black) 
            mask=np.zeros(frame.shape[:2] , dtype="uint8") 
         
            # Draw the desired region to crop out in white 
            cv2.circle(mask, center_coordinates, radius, (255,255,255),-1)    
            masked=cv2.bitwise_and(frame,frame,mask=mask)
            cv2.imshow('mask applied',masked)
        if cv2.waitKey(30) & 0xff==27:
            break
video.release()
cv2.destroyAllWindows()

上面的代碼檢測并在黑色背景上顯示圓形蒙版中的人臉。但如上所述，圓形 ROI 外的背景顏色應為藍色。

我嘗試用下面的代碼替換 mask=np.zeros(frame.shape[:2], dtype="uint8") 但失敗了。Frame.shape[0:2] 甚至不包括通道，我一開始不知道如何改變顏色。

mask=np.ones(frame.shape[0:2], dtype="uint8")
mask[:,:,0]=255
mask[:,:,1]=0
mask[:,:,2]=0

我還嘗試創建一個圓形蒙版影像，然后將其放在另一個影像上，卻發現它導致了同樣的問題。

import numpy as np
from cv2 import cv2
'''
ML object detection algo(haarcascade)used to identify objects. 
the XML file consists of trained Haar Cascade models.
'''
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#initialize video from the webcam
video = cv2.VideoCapture(1)
print(cv2.VideoCapture(1).isOpened())

while True:
# ret tells if the camera works properly. Frame is an actual frame from the video feed
    ret, frame= video.read()
    # print(cv2.VideoCapture(0).isOpened())
    # make sure port is working and read the image
    if frame is not None and video.isOpened():
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # Detect the faces within the subregions of the image in scales
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6)
        # Draw the rectangle around each face
        for (x, y, w, h) in faces:
            #Use the coordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
            center_coordinates = x   w // 2, y   h // 2
            radius = w // 2 # or can be h / 2 or can be anything based on your requirements
            #background color(black) 
            mask=np.zeros(frame.shape[:2] , dtype="uint8") 
            # create blue colored background
            color = np.full_like(frame, (255,0,0))
           
            # Draw the desired region to crop out in white 
            roi=cv2.circle(mask, center_coordinates, radius, (255,255,255),-1)    
            masked=cv2.bitwise_and(frame,frame,mask=mask)
            mask_blue=cv2.bitwise_and(color,color,mask=mask-roi)
            # combine the two masked images
            result = cv2.add(masked,mask_blue)
            cv2.imshow('result',result)
        if cv2.waitKey(30) & 0xff==27:
            break
video.release()
cv2.destroyAllWindows()

uj5u.com熱心網友回復：

我已根據您的要求將您的代碼更改如下。在這里我添加了額外的一行

masked[np.where((masked==[0,0,0]).all(axis=2))]=[255,0,0]

您可以將黑色區域的像素值更改為任何特定顏色。

import numpy as np
import cv2
'''
ML object detection algo(haarcascade)used to identify objects. 
the XML file consists of trained Haar Cascade models.
'''
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades  'haarcascade_frontalface_default.xml')
#initialize video from the webcam
video = cv2.VideoCapture(0)
print(cv2.VideoCapture(0).isOpened())

while True:
# ret tells if the camera works properly. Frame is an actual frame from the video feed
    ret, frame= video.read()
    # print(cv2.VideoCapture(0).isOpened())
    # make sure port is working and read the image
    if frame is not None and video.isOpened():
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # Detect the faces within the subregions of the image in scales
        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=6)
        # Draw the rectangle around each face
        for (x, y, w, h) in faces:
            #Use the coordinates to find the center of the face and from that point draw a rectangle of radius w/2 or h/2.
            center_coordinates = x   w // 2, y   h // 2
            radius = w // 2 # or can be h / 2 or can be anything based on your requirements
            #background color(black) 
            mask=np.zeros(frame.shape[:2] , dtype="uint8") 
            
            # Draw the desired region to crop out in white 
            roi=cv2.circle(mask, center_coordinates, radius, (255,255,255),-1)    
            masked=cv2.bitwise_and(frame,frame,mask=mask)
            masked[np.where((masked==[0,0,0]).all(axis=2))]=[255,0,0]
            cv2.imshow('result',masked)
        if cv2.waitKey(30) & 0xff==27:
            break
video.release()
cv2.destroyAllWindows()

標籤：Python opencv 图像处理 视频截取 haar-分类器

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