如何使用Python OpenCV混合圖像?
在這篇文章中,我們將提供一些使用OpenCV的示例。
在OpenCV中混合圖像我們將提供一個逐步的示例,說明如何使用Python OpenCV混合圖像。下面我們展示了目標(biāo)圖像和濾鏡圖像。
目標(biāo)圖像
濾鏡圖像
import cv2
# Two images
img1 = cv2.imread('target.jpg')
img2 = cv2.imread('filter.png')
# OpenCV expects to get BGR images, so we will convert from BGR to RGB
img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
# Resize the Images. In order to blend them, the two images
# must be of the same shape
img1 =cv2.resize(img1,(620,350))
img2 =cv2.resize(img2,(620,350))
# Now, we can blend them, we need to define the weight (alpha) of the target image
# as well as the weight of the filter image
# in our case we choose 80% target and 20% filter
blended = cv2.a(chǎn)ddWeighted(src1=img1,alpha=0.8,src2=img2,beta=0.2,gamma=0)
# finally we can save the image. Now we need to convert it from RGB to BGR
cv2.imwrite('Blending.png',cv2.cvtColor(blended, cv2.COLOR_RGB2BGR))
在OpenCV中處理圖像我們將展示如何使用Python中的OpenCV應(yīng)用圖像處理。
如何模糊影像import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
img = cv2.imread('panda.jpeg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
blurred_img = cv2.blur(img,ksize=(20,20))
cv2.imwrite("blurredpanda.jpg", blurred_img)
如何申請Sobel Operator你可以在Wikipedia上查看Sobel Operator:https://en.wikipedia.org/wiki/Sobel_operator也可以開始嘗試一些過濾器。讓我們應(yīng)用水平和垂直Sobel。img = cv2.imread('panda.jpeg',0)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
cv2.imwrite("sobelx_panda.jpg", sobelx)
cv2.imwrite("sobely_panda.jpg", sobely)
如何對圖像應(yīng)用閾值我們還可以對圖像進行二值化。img = cv2.imread('panda.jpeg',0)
ret,th1 = cv2.threshold(img,100,255,cv2.THRESH_BINARY)
fig = plt.figure(figsize=(12,10))
plt.imshow(th1,cmap='gray')
OpenCV中的人臉檢測我們將討論如何使用OpenCV應(yīng)用人臉檢測。我們通過一個實際的可重現(xiàn)的示例來直截了當(dāng)。邏輯如下:我們從URL(或從硬盤)獲取圖像。我們將其轉(zhuǎn)換為numpy array,然后轉(zhuǎn)換為灰度。然后通過應(yīng)用適當(dāng)?shù)模狢ascadeClassifier,**我們獲得了人臉的邊界框。最后,使用PIllow(甚至是OpenCV),我們可以在初始圖像上繪制框。import cv2 as cv
import numpy as np
import PIL
from PIL import Image
import requests
from io import BytesIO
from PIL import ImageDraw
# I have commented out the cat and eye cascade. Notice that the xml files are in the opencv folder that you have downloaded and installed
# so it is good a idea to write the whole path
face_cascade = cv.CascadeClassifier('C:\opencv\build\etc\haarcascades\haarcascade_frontalface_default.xml')
#cat_cascade = cv.CascadeClassifier('C:\opencv\build\etc\haarcascades\haarcascade_frontalcatface.xml')
#eye_cascade = cv.CascadeClassifier('C:\opencv\build\etc\haarcascades\haarcascade_eye.xml')
URL = "https://images.unsplash.com/photo-1525267219888-bb077b8792cc?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1050&q=80"
response = requests.get(URL)
img = Image.open(BytesIO(response.content))
img_initial = img.copy()
# convert it to np array
img = np.a(chǎn)sarray(img)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
# And lets just print those faces out to the screen
#print(faces)
drawing=ImageDraw.Draw(img_initial)
# For each item in faces, lets surround it with a red box
for x,y,w,h in faces:
# That might be new syntax for you! Recall that faces is a list of rectangles in (x,y,w,h)
# format, that is, a list of lists. Instead of having to do an iteration and then manually
# pull out each item, we can use tuple unpacking to pull out individual items in the sublist
# directly to variables. A really nice python feature
#
# Now we just need to draw our box
drawing.rectangle((x,y,x+w,y+h), outline="red")
display(img_initial)
最初的圖像是這樣的:
然后在繪制邊界框后,我們得到:
如我們所見,我們設(shè)法正確地獲得了四個面,但是我們還發(fā)現(xiàn)了窗后的“鬼”……裁剪臉部以分離圖像我們還可以裁剪臉部以分離圖像for x,y,w,h in faces:
img_initial.crop((x,y,x+w,y+h))
display(img_initial.crop((x,y,x+w,y+h)))
例如,我們得到的第一張臉是:
注意:如果你想從硬盤讀取圖像,則只需鍵入以下三行:# read image from the PC
initial_img=Image.open('my_image.jpg')
img = cv.imread('my_image.jpg')
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
OpenCV中的人臉檢測視頻這篇文章是一個實際示例,說明了我們?nèi)绾螌penCV]與Python結(jié)合使用來檢測視頻中的人臉。在上一篇文章中,我們解釋了如何在Tensorflow中應(yīng)用對象檢測和OpenCV中的人臉檢測。通常,計算機視覺和對象檢測是人工智能中的熱門話題。例如考慮自動駕駛汽車,該自動駕駛汽車必須連續(xù)檢測周圍的許多不同物體(行人,其他車輛,標(biāo)志等)。如何錄制人臉檢測視頻在以下示例中,我們將USB攝像頭應(yīng)用到人臉檢測,然后將視頻寫入.mp4文件。如你所見,OpenCV能夠檢測到面部,并且當(dāng)它隱藏在手后時,OpenCV會丟失它。import cv2
# change your path to the one where the haarcascades/haarcascade_frontalface_default.xml is
face_cascade = cv2.CascadeClassifier('../DATA/haarcascades/haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# MACOS AND LINUX: *'XVID' (MacOS users may want to try VIDX as well just in case)
# WINDOWS *'VIDX'
writer = cv2.VideoWriter('myface.mp4', cv2.VideoWriter_fourcc(*'XVID'),25, (width, height))
while True:
ret, frame = cap.read(0)
frame = detect_face(frame)
writer.write(frame)
cv2.imshow('Video Face Detection', frame)
# escape button to close it
c = cv2.waitKey(1)
if c == 27:
break
cap.release()
writer.release()
cv2.destroyAllWindows()
計算機視覺代碼的輸出只需幾行代碼即可通過動態(tài)人臉檢測錄制該視頻。如果你運行上面的代碼塊,你將獲得類似的視頻。
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