我创建了原始的python脚本来处理已保存的图像。我现在想要它捕获图像并裁剪它。我有一个工作的网络摄像头部分和工作作物部分，但我无法将它们组合起来并制作它们。我已经包含了组合代码。目前，它仍将裁剪已保存的图像，网络摄像头的GUI会显示一秒钟但不显示任何内容（灰色屏幕）。任何人都可以帮助我吗？
import cv
import cv2
import numpy
import Image
import glob
import os
# Static
faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
padding = -1
inputimg = raw_input('Please enter the entire path to the image folder:')
outputimg = raw_input('Please enter the entire path to the output folder:')
if not os.path.exists(outputimg):
os.makedirs(outputimg)
while (padding < 0):
padding = int(raw_input('Enter crop padding:'))
capture = cv2.VideoCapture(0)
cv2.namedWindow("Face Crop")
if capture.isOpened():
frame = capture.read()
def DetectFace(image, faceCascade, returnImage=False):
#variables
min_size = (50,50)
haar_scale = 1.1
min_neighbors = 3
haar_flags = 0
DOWNSCALE = 4
# Equalize the histogram
cv.EqualizeHist(image, image)
# Detect the faces
faces = cv.HaarDetectObjects(image, faceCascade, cv.CreateMemStorage(0),haar_scale, min_neighbors, haar_flags, min_size)
# If faces are found
if faces and returnImage:
for ((x, y, w, h), n) in faces:
# Convert bounding box to two CvPoints
pt1 = (int(x), int(y))
pt2 = (int(x + w), int(y + h))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)
# Start video frame
minisize = (frame.shape[1]/DOWNSCALE,frame.shape[0]/DOWNSCALE)
miniframe = cv2.resize(frame, minisize)
faceCam = classifier.detectMultiScale(miniframe)
for f in faceCam:
x, y, w, h = [ v*DOWNSCALE for v in f ]
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255))
cv2.putText(frame, "Press ESC to close.", (5, 25),
cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,255,255))
cv2.imshow("preview", frame)
# get next frame
frame = capture.read()
raw_input('Pause for testing')
key = cv2.waitKey(20)
if key in [27, ord('Q'), ord('q')]: # exit on ESC
break
if returnImage:
return image
else:
return faces
def pil2cvGrey(pil_im):
pil_im = pil_im.convert('L')
cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0] )
return cv_im
def imgCrop(image, cropBox, boxScale=1):
# Crop a PIL image with the provided box [x(left), y(upper), w(width), h(height)]
# Calculate scale factors
xPadding=max(cropBox[2]*(boxScale-1),int(padding))
yPadding=max(cropBox[3]*(boxScale-1),int(padding))
# Convert cv box to PIL box [left, upper, right, lower]
PIL_box=[cropBox[0]-xPadding, cropBox[1]-yPadding, cropBox[0]+cropBox[2]+xPadding, cropBox[1]+cropBox[3]+yPadding]
return image.crop(PIL_box)
def Crop(imagePattern,boxScale=1):
imgList=glob.glob(imagePattern)
if len(imgList)<=0:
return
else:
for img in imgList:
pil_im=Image.open(img)
cv_im=pil2cvGrey(pil_im)
faces=DetectFace(cv_im,faceCascade)
if faces:
n=1
for face in faces:
croppedImage=imgCrop(pil_im, face[0],boxScale=boxScale)
fname,ext=os.path.splitext(img)
fname = os.path.basename(fname)
croppedImage.save(outputimg + '\\' + fname + ' -c' + ext)
n+=1
print 'Cropping:', fname
else:
print 'No faces found:', img
# Crop all images in a folder
Crop(inputimg + '\*.png', boxScale=1)
Crop(inputimg + '\*.jpg', boxScale=1)
此外，如果有人有任何代码改进，请告诉我，因为我不熟悉Python。
答案 0 :(得分：0)
我能够通过重新编写逻辑和代码流来解决这个问题。更新了以下代码和github，https://github.com/aDroidman/EyeonYou
import cv
import cv2
import numpy
import Image
import glob
import os
# Static
faceCascade = cv.Load('haarcascade_frontalface_alt.xml')
padding = -1
boxScale = 1
# Needed for webcam CV2 section
HaarXML = "haarcascade_frontalface_alt.xml"
classifier = cv2.CascadeClassifier(HaarXML)
downScale = 4
webcam = cv2.VideoCapture(0)
def DetectFace(image, faceCascade, returnImage=False):
#variables
min_size = (50,50)
haar_scale = 1.1
min_neighbors = 3
haar_flags = 0
DOWNSCALE = 4
# Equalize the histogram
cv.EqualizeHist(image, image)
# Detect the faces
faces = cv.HaarDetectObjects(image, faceCascade, cv.CreateMemStorage(0),haar_scale, min_neighbors, haar_flags, min_size)
# If faces are found
if faces and returnImage:
for ((x, y, w, h), n) in faces:
# Convert bounding box to two CvPoints
pt1 = (int(x), int(y))
pt2 = (int(x + w), int(y + h))
cv.Rectangle(image, pt1, pt2, cv.RGB(255, 0, 0), 5, 8, 0)
if returnImage:
return image
else:
return faces
def pil2cvGrey(pil_im):
pil_im = pil_im.convert('L')
cv_im = cv.CreateImageHeader(pil_im.size, cv.IPL_DEPTH_8U, 1)
cv.SetData(cv_im, pil_im.tostring(), pil_im.size[0] )
return cv_im
def imgCrop(image, cropBox, boxScale=1):
# Crop a PIL image with the provided box [x(left), y(upper), w(width), h(height)]
# Calculate scale factors
xPadding=max(cropBox[2]*(boxScale-1),int(padding))
yPadding=max(cropBox[3]*(boxScale-1),int(padding))
# Convert cv box to PIL box [left, upper, right, lower]
PIL_box=[cropBox[0]-xPadding, cropBox[1]-yPadding, cropBox[0]+cropBox[2]+xPadding, cropBox[1]+cropBox[3]+yPadding]
return image.crop(PIL_box)
def Crop(imagePattern,boxScale,outputimg):
imgList=glob.glob(imagePattern)
if len(imgList)<=0:
return
else:
for img in imgList:
pil_im=Image.open(img)
cv_im=pil2cvGrey(pil_im)
faces=DetectFace(cv_im,faceCascade)
if faces:
n=1
for face in faces:
croppedImage=imgCrop(pil_im, face[0],boxScale=boxScale)
fname,ext=os.path.splitext(img)
fname = os.path.basename(fname)
croppedImage.save(outputimg + '\\' + fname + ' -c' + ext)
n+=1
print 'Cropping:', fname
else:
print 'No faces found:', img
def CropSetup(padding, boxScale):
inputimg = raw_input('Please enter the entire path to the image folder:')
outputimg = raw_input('Please enter the entire path to the output folder:')
# Create output folder if missing
if not os.path.exists(outputimg):
os.makedirs(outputimg)
# Get padding for crop
while (padding < 0):
padding = int(raw_input('Enter crop padding:'))
# Crop images
Crop(inputimg + '\*.png', boxScale, outputimg)
Crop(inputimg + '\*.jpg', boxScale, outputimg)
print 'Option 1: Detect image from Webcam'
print 'Option 2: Crop saved images'
option = int(raw_input('Please enter 1 or 2: '))
def Webcam(webcam, classifier, downScale):
if webcam.isOpened():
rval, frame = webcam.read()
else:
rval = False
while rval:
# detect faces and draw bounding boxes
minisize = (frame.shape[1]/downScale,frame.shape[0]/downScale)
miniframe = cv2.resize(frame, minisize)
faces = classifier.detectMultiScale(miniframe)
for f in faces:
x, y, w, h = [ v*downScale for v in f ]
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,0,255))
cv2.putText(frame, "Press ESC to close.", (5, 25),
cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,255,255))
cv2.imshow("Face Crop", frame)
# get next frame
rval, frame = webcam.read()
key = cv2.waitKey(10)
if key in [27, ord('Q'), ord('q')]: # exit on ESC
break
if option == 1:
Webcam(webcam, classifier, downScale)
elif option == 2:
CropSetup(padding, boxScale)
else:
print 'Not a valid input'