def take_snapshot(self):
# get image from webcam
if not self.camera_is_on():
self.use_camera()
self.flushCameraBuffer() # this reduces the frame delay
frame = cv.QueryFrame(self.capture)
self.create_folder_if_not_exist()
image_name = "capture_%s.jpg" % time.strftime("%y_%m_%d_%H_%M_%S")
image_path = os.path.join(PICTURE_PATH, image_name)
cv.SaveImage(image_path, frame)
cv.SaveImage(os.path.join(PICTURE_PATH, "current.jpg"),
frame) # this is the preview image
return image_name
def grab_images(video_file, frame_inc=100, delay=100):
"""
Walks through the entire video and save image for each increment
"""
my_video = init_video(video_file)
if my_video != None:
# Display the video and save evry increment frames
cpt = 0
img = cv2.QueryFrame(my_video)
if img != None:
cv2.NamedWindow("Vid", cv2.CV_WINDOW_AUTOSIZE)
else:
return None
nFrames = int(
cv2.GetCaptureProperty(my_video, cv2.CV_CAP_PROP_FRAME_COUNT))
while cpt < nFrames:
for ii in range(frame_inc):
img = cv2.QueryFrame(my_video)
cpt += 1
cv2.ShowImage("Vid", img)
out_name = "" + str(cpt) + ".jpg"
cv2.SaveImage(out_name, img)
print out_name, str(nFrames)
cv2.WaitKey(delay)
else:
return None
def save_captures(num, captures):
dir = "data/capture_%02d" % num
if not os.path.exists(dir):
os.mkdir(dir)
for i, img in enumerate(captures):
path = os.path.join(dir, "card_%04d.png" % i)
if os.path.exists(path):
raise Exception("path %s already exists!" % path)
cv2.SaveImage(path, img)
def saveAsJPG(img):
global fname_temp
lt = time.localtime(time.time())
if ((lt[5] % 2) == 0):
fname = "%04d%02d%02d%02d%02d%02d" % (lt[0], lt[1], lt[2], lt[3],
lt[4], lt[5])
if (fname != fname_temp):
print("frame saved at " + fname)
cv2.SaveImage("img/" + fname + ".jpg", img)
fname_temp = fname
def display_rgb(dev, data, timestamp):
global keep_running
cv.Image = frame_convert.video_cv(data)
img = cv.CreateImage(cv.GetSize(cv.Image), cv.IPL_DEPTH_16S, 3)
cv.ShowImage('RGB', cv.Image)
for x in range(1, 5):
name = "img%d" % (x)
cv.SaveImage('name.png', cv.Image)
time.sleep(1)
if cv.WaitKey(10) == 27:
keep_running = False
def save_image(img, _pos, _time, _pitch, number):
_filename = "{}.jpeg".format("%02d" % number)
cv2.SaveImage(_filename, img)
im = Image.open(_filename)
exif_dict = piexif.load(_filename)
exif_dict["0th"][
piexif.ImageIFD.ImageDescription] = "pitch: {} postion: {}".format(
_pitch, _pos)
exif_bytes = piexif.dump(exif_dict)
im.save(_filename, "jpeg", exif=exif_bytes)
return
def take_preview_image(self):
# get image from webcam
if not self.camera_is_on():
self.use_camera()
self.flushCameraBuffer() # this reduces the frame delay
frame = cv.QueryFrame(self.capture)
if frame is None:
self.close_camera()
return
self.create_folder_if_not_exist()
cv.SaveImage(os.path.join(PICTURE_PATH, "current.jpg"),
frame) # this is the preview image
def detect_features(self, faces, image_filename):
""" Detects Features in an list of faces and returns the images """
logging.debug('start detect %s features for file %s (train.py)' %(parameter.description_method, image_filename))
keypoints = []
descriptors = []
# detect features and save
for i in range(len(faces)):
if not parameter.face_enlargement == None:
faces[i] = tools.enlarge_image(faces[i], parameter.face_enlargement)
logging.debug('Cropped face from file %s has been enlarged with factor %.3f (surf-detector.py)' % (image_filename, parameter.face_enlargement))
face_numpy = np.asarray(faces[i]) # convert image for further processing
# compute surf calculation
if parameter.description_method == 'surf':
surf = cv2.SURF(parameter.hessian_threshold, parameter.nOctaves, parameter.nOctaveLayers) # threshold, number of octaves, number of octave layers within each octave (http://opencv.itseez.com/modules/features2d/doc/feature_detection_and_description.html, http://www.mathworks.de/help/toolbox/vision/ref/detectsurffeatures.html)
tmpkeypoints, tmpdescriptors = surf.detect(face_numpy, None, False) # extracting the SURF keys
if len(tmpdescriptors) == 0:
logging.warn('No descriptors found for a face in file %s (surf-detector.py)' % (image_filename))
else:
tmpdescriptors.shape = (-1, surf.descriptorSize()) # change the shape of the descriptor from 1-dim to 2-dim (notwendig, damit die Funktionen - match_bruteforce - bei der Suche funktionieren)
logging.info('%d Features found in file %s: face number %d (surf-detector.py)' % (len(tmpdescriptors), image_filename, (i+1)))
# compute sift calculation
if parameter.description_method == 'sift':
cv.SaveImage('tmp-sift.jpg', faces[i])
sift.process_image('tmp-sift.jpg',"tmp.sift")
l1,tmpdescriptors = sift.read_features_from_file("tmp.sift")
tmpkeypoints = []
if tmpdescriptors == None:
logging.warn('No descriptors found for a face in file %s (surf-detector.py)' % (image_filename))
else:
for j in range(len(l1)):
keypoint = cv2.KeyPoint(l1[j][0], l1[j][1], l1[j][2], l1[j][3])
tmpkeypoints.append(keypoint)
logging.info('%d Features found in file %s: face number %d (surf-detector.py)' % (len(tmpdescriptors), image_filename, (i+1)))
keypoints.append(tmpkeypoints) # add keypoints do list even when none are found
descriptors.append(tmpdescriptors) # add descriptors do list even when none are found
return(keypoints, descriptors)
def process_image(self, slider_pos):
global cimg, source_image1, ellipse_size, maxf, maxs, eoc, lastcx,lastcy,lastr
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
# Create the destination images
cimg = cv.CloneImage(self.source_image)
cv.Zero(cimg)
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
image04 = cv.CreateImage(cv.GetSize(self.source_image), cv.IPL_DEPTH_8U, 3)
cv.Zero(image04)
# Threshold the source image. This needful for cv.FindContours().
cv.Threshold(self.source_image, image02, slider_pos, 255, cv.CV_THRESH_BINARY)
# Find all contours.
cont = cv.FindContours(image02,
stor,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
(0, 0))
maxf = 0
maxs = 0
size1 = 0
for c in contour_iterator(cont):
if len(c) > ellipse_size:
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray,0,1,8,(0,0))
if iter == 0:
strng = segF + '/' + 'contour1.png'
cv.SaveImage(strng,image04)
color = (255,255,255)
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
if iter == 1:
if size[0] > size[1]:
size2 = size[0]
else:
size2 = size[1]
if size2 > size1:
size1 = size2
size3 = size
# Fits ellipse to current contour.
if eoc == 0 and iter == 2:
rand_val = abs((lastr - ((size[0]+size[1])/2)))
if rand_val > 20 and float(max(size[0],size[1]))/float(min(size[0],size[1])) < 1.5:
lastcx = center[0]
lastcy = center[1]
lastr = (size[0]+size[1])/2
if rand_val > 20 and float(max(size[0],size[1]))/float(min(size[0],size[1])) < 1.4:
cv.Ellipse(cimg, center, size,
angle, 0, 360,
color,2, cv.CV_AA, 0)
cv.Ellipse(source_image1, center, size,
angle, 0, 360,
color,2, cv.CV_AA, 0)
elif eoc == 1 and iter == 2:
(int,cntr,rad) = cv.MinEnclosingCircle(PointArray2D32f)
cntr = (cv.Round(cntr[0]), cv.Round(cntr[1]))
rad = (cv.Round(rad))
if maxf == 0 and maxs == 0:
cv.Circle(cimg, cntr, rad, color, 1, cv.CV_AA, shift=0)
cv.Circle(source_image1, cntr, rad, color, 2, cv.CV_AA, shift=0)
maxf = rad
elif (maxf > 0 and maxs == 0) and abs(rad - maxf) > 30:
cv.Circle(cimg, cntr, rad, color, 2, cv.CV_AA, shift=0)
cv.Circle(source_image1, cntr, rad, color, 2, cv.CV_AA, shift=0)
maxs = len(c)
if iter == 1:
temp3 = 2*abs(size3[1] - size3[0])
if (temp3 > 40):
eoc = 1
im_data = Image.fromarray(im, 'RGB')
filt_min, filt_max = net.blobs['upsample'].data.min(
), net.blobs['upsample'].data.max()
imgo = net.blobs['upsample'].data.copy().transpose(0, 2, 3, 1)[0]
fig = plt.figure()
a = fig.add_subplot(1, 2, 1)
#lum_img = img[:,:,0]
#imgplot = plt.imshow(lum_img)
imgplot = plt.imshow(im_data.astype(np.uint8),
interpolation='none',
cmap='gray')
a.set_title('Before')
#plt.colorbar(ticks=[0.1,0.3,0.5,0.7], orientation ='horizontal')
a = fig.add_subplot(1, 2, 2)
imgplot = plt.imshow(imgo.astype(np.uint8),
interpolation='none',
cmap='gray')
a.set_title('After')
plt.show()
b = cv2.cvtColor(imgo.astype(np.uint8), cv2.COLOR_GRAY2BGR)
cv2.SaveImage('transformedCat.png', b)
#plt.title("original image")
#plt.imshow(im)
#plt.axis('off')
#plt.imshow(imgo, interpolation='none')
#plt.show()
def GetBoth():
# freenect.stop_depth()
# freenect.stop_video()
depth = freenect.sync_get_depth()[0]
video = freenect.sync_get_video()[0]
# freenect.start_depth()
# freenect.start_video()
return (DoUsefulConvert8(depth), DoNiceConvertRGB(video))
def StopKinect():
freenect.sync_stop()
if __name__ == "__main__":
while True:
image = GetDepth8()
if image:
cv.ShowImage("Kinect view", image)
cv.SaveImage("/home/borg/freenect.png", image)
else:
print "invalid image %s" % repr(image)
k = cv.WaitKey(5)
if k == 0x1b:
print 'ESC pressed. Exiting ...'
break
def showNaoImage(IP, PORT,camID):#参数分别为IP、PORT、摄像头ID（区分上下摄像头）
#链接nao的摄像头
camProxy = ALProxy("ALVideoDevice", IP, PORT)
resolution = 2 # VGA``
colorSpace = 11 # RGB
videoClient = camProxy.subscribe("python_client", resolution, colorSpace, 5)#设置分辨率、帧速、颜色空间
t0 = time.time()
camProxy.setParam(18,camID)#设置摄像头
naoImage = camProxy.getImageRemote(videoClient)#将获取的图像赋给naoImage
t1 = time.time()
camProxy.unsubscribe(videoClient)
imageWidth = naoImage[0]
imageHeight = naoImage[1]
array = naoImage[6] #naoImage[6]为imagedata
im_cv = numpy.zeros((imageHeight, imageWidth, 3), numpy.uint8)#初始化图像im_cv
im_cv.data = array #将从摄像头获取的图像copy到im_cv，转为mat
#转化颜色空间由BGR到RGB
b, g, r = cv2.split(im_cv)
img1 = cv2.merge([r, g, b])
#转mat到cvmat
img3 = cv2.cv.fromarray(img1)
cv2.SaveImage("test22.bmp",img3)
#转换颜色空间到HSV
imgHSV = cv2.CreateImage(cv2.GetSize(img3), 8, 3)
cv2.CvtColor(img3, imgHSV, cv2.CV_RGB2HSV)
cimg,cimg_c=hsvProceed(imgHSV,camID) #调用hsvProceed处理图像，返回二值图
#圈取最小矩形框
#初始化
storage = cv2.cv.CreateMemStorage(0)
cnts = cv2.FindContours(cimg,storage,cv2.cv.CV_RETR_LIST,cv2.cv.CV_CHAIN_APPROX_SIMPLE)
currtnt=cnts
Area = 0
left_right = 0
up_down = 0
#为不同摄像头设置不同筛选条件
if camID == 0:
areamax = 2500
areamin = 40
valuemin = 25
value_w = 641
valuemax = 481
else :
areamax = 5000
areamin = 400
valuemin = 0
value_w = 500
valuemax = 400
while cnts:
rect = cv2.cv.BoundingRect(cnts,0)#获得单连通矩形框
area = rect[2]*rect[3] #获得矩形框面积
#获得矩形框中心点坐标
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
#调用choose0文件下的radio函数，筛选圆形部分
radio_c = choose0.radio(cimg_c,rect)
radio = float(rect[2])/rect[3] #计算矩形框的长宽比
#以下if语句均为筛选条件
if rect[1]>=valuemin:
if rect[1]<=valuemax:
if rect[0]<=value_w:
if area > areamin:
if area < areamax:
if radio > 0.6:
if radio < 1.6:
if radio_c == 1:
cv2.cv.DrawContours(img3, cnts, (255, 255, 0), (255, 255, 0), 0, 1)#画出单连通轮廓
cv2.cv.Rectangle(img3,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),(0,0,255),1)#画出矩形框
rect_center_x = rect[0] + rect[2]/2
rect_center_y = rect[1] + rect[3]/2
#计算通过条件的矩形框的面积以及在图像中的位置
Area = rect[2]*rect[3]
left_right = rect_center_x - cimg.width / 2
up_down = rect_center_y - cimg.height / 2
cnts = cnts.h_next()
return Area,left_right,up_down #返回球的面积以及在图像中的位置
cv.createTrackbar('Depth Window', 'Video', depth_window, 1, toggle_depth_window)
cv.createTrackbar('Threshold Window', 'Video', threshold_window, 1, toggle_threshold_window)
cv.createTrackbar('Detector Window', 'Video', detector_window, 1, toggle_detector_window)
# main program loop
while 1:
if depth_window:
show_depth()
if threshold_window:
show_threshold();
if detector_window:
show_detector();
show_video()
key = cv.WaitKey(5) & 0xFF
if key == 27:
break;
elif key == 115:
print '"s" key pressed, saving RGB image to file RGB.jpg'
cv2.imwrite('RGB.jpg', freenect.sync_get_video()[0]);
elif key == 100 and depth_window:
print '"d" key pressed, saving depth image to file DEPTH.jpg'
cv.SaveImage('DEPTH.jpg', depth_image)
import cv2 as cv
img = cv.LoadImage("friend1.jpg")
image_size = cv.GetSize(img) #获取图片的大小
greyscale = cv.CreateImage(image_size, 8, 1) #建立一个相同大小的灰度图像
cv.CvtColor(img, greyscale, cv.CV_BGR2GRAY) #将获取的彩色图像，转换成灰度图像
storage = cv.CreateMemStorage(0) #创建一个内存空间，人脸检测是要利用，具体作用不清楚
cv.EqualizeHist(greyscale, greyscale) #将灰度图像直方图均衡化，貌似可以使灰度图像信息量减少，加快检测速度
# detect objects
cascade = cv.Load('haarcascade_frontalface_alt2.xml') #加载Intel公司的训练库
#检测图片中的人脸，并返回一个包含了人脸信息的对象faces
faces = cv.HaarDetectObjects(greyscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING, (50, 50))
#获得人脸所在位置的数据
j = 0 #记录个数
for (x, y, w, h), n in faces:
j += 1
cv.SetImageROI(img, (x, y, w, h)) #获取头像的区域
cv.SaveImage("face" + str(j) + ".jpg", img)
#保存下来
def saveImage(self, filename):
# Save the image using the given filename
cv.SaveImage(filename, self.image)
post_on_facebook(people[final_label], counter, picture_name)
final_5= []
'''
cv2.imshow("Video Window", frame)
counter+= 1
#if (cv2.waitKey(5) & 0xFF== 27):
#break
command = cv2.waitKey(5)
if command == ord('q'):
print("Ending program")
break
#elif brk == 1:
# print("Detected !!")
# break
elif command == ord('s'):
print("Saving image")
cv2.SaveImage("img00.jpg",frame)
#elif counter == 100:
# print("Ending program by program")
# break
cv2.destroyAllWindows()
time.sleep(2)
# GPIO.output(21,GPIO.HIGH)
def landmarkdetect(IP, PORT, camID):
camProxy = ALProxy("ALVideoDevice", IP, PORT)
resolution = 2 # VGA``
colorSpace = 11 # RGB
videoClient = camProxy.subscribe("python_client", resolution, colorSpace,
5)
camProxy.setParam(18, camID)
naoImage = camProxy.getImageRemote(videoClient)
camProxy.unsubscribe(videoClient)
imageWidth = naoImage[0]
imageHeight = naoImage[1]
array = naoImage[6]
im_cv = numpy.zeros((imageHeight, imageWidth, 3), numpy.uint8)
im_cv.data = array
#im_cv = cv2.imread("img3.jpg",1)
b, g, r = cv2.split(im_cv)
img1 = cv2.merge([r, g, b])
img3 = cv2.fromarray(img1)
#img3=cv.LoadImage("img3.jpg",1)
cv.SaveImage("save1.jpg", img3)
imgHSV = cv.CreateImage(cv.GetSize(img3), 8, 3)
cv.CvtColor(img3, imgHSV, cv.CV_RGB2HSV)
cimg, cimg_c = hsvProceed(imgHSV)
# cv.ShowImage("imgHSV", imgHSV)
# cv.ShowImage("cimg", cimg)
# cv.WaitKey(5)
#
# img3 = cv.LoadImage("save1.jpg",1)
# imgHSV = cv.CreateImage(cv.GetSize(img3), 8, 3)
#
# cv.CvtColor(img3, imgHSV, cv.CV_RGB2HSV)
# cimg, cimg_c=hsvProceed(imgHSV)
# cv.ShowImage("s",cimg_c)
storage = cv2.CreateMemStorage(0)
cnts = cv.FindContours(cimg, storage, cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE)
currtnt = cnts
x = 0.0
Area = 0
left_right = 0
up_down = 0
if camID == 0:
areamax = 6000
areamin = 350
value = img3.height / 7
else:
areamax = 5000
areamin = 400
value = 0
while cnts:
rect = cv2.BoundingRect(cnts, 0)
area = rect[2] * rect[3]
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
# if camID == 1:
# radio_c = choose0.radio(cimg_c,rect)
# if camID == 0:
# radio_c = choose0.choose02(cimg_c,rect)
radio = float(rect[2]) / rect[3]
if rect[1] > 10:
if area > areamin:
if area < areamax:
if radio > 0.1:
if radio < 1.0:
#if radio_c == 1:
# cv2.cv.DrawContours(img3, cnts, (255, 255, 0), (255, 255, 0), 0, 1)
# cv2.cv.Rectangle(img3,(rect[0],rect[1]),(rect[0]+rect[2],rect[1]+rect[3]),(0,0,255),1)
#choose0.radio(cimg_c,rect)
rect_center_x = rect[0] + rect[2] / 2
rect_center_y = rect[1] + rect[3] / 2
Area = rect[2] * rect[3]
left_right = rect_center_x - cimg.width / 2
up_down = rect_center_y - cimg.height / 2
x, y = getloacation(rect[2], left_right)
cnts = cnts.h_next()
# cv2.cv.ShowImage("fsfs", img3)
# cv.WaitKey(1)
return Area, left_right, x
if __name__ == "__main__":
orig = cv.LoadImage(
r"C:\git\Python-Snippets\Image Recognition\images\D2C-Logins - RunDate 2019-10-03 - Part (22).image"
)
#Convert in black and white
res = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, res, cv.CV_BGR2GRAY)
#Operations on the image
openCloseImage(res)
dilateImage(res, 2)
erodeImage(res, 2)
smoothImage(res, 5)
thresholdImage(res, 150, cv.CV_THRESH_BINARY_INV)
#Get contours approximated
contourLow = getContours(res, 3)
#Draw them on an empty image
final = cv.CreateImage(cv.GetSize(res), 8, 1)
cv.Zero(final)
cv.DrawContours(final, contourLow, cv.Scalar(255), cv.Scalar(255), 2,
cv.CV_FILLED)
cv.ShowImage("orig", orig)
cv.ShowImage("image", res)
cv.SaveImage("modified.png", res)
cv.ShowImage("contour", final)
cv.SaveImage("contour.png", final)
cv.WaitKey(0)
import time
if __name__ == '__main__':
cv.namedWindow("camera", 1)
#开启ip摄像头
video = "http://*****:*****@172.16.200.214:8081/"
capture = cv.VideoCapture(video)
num = 0
while True:
img = cv.QueryFrame(capture)
cv.ShowImage("camera", img)
#按键处理，注意，焦点应当在摄像头窗口，不是在终端命令行窗口
key = cv.WaitKey(10)
if key == 27:
#esc键退出
print('esc break...')
break
if key == ord(' '):
#保存一张图像
num = num + 1
filename = "frames_%s.jpg" % num
cv.SaveImage(filename, img)
del (capture)
cv.DestroyWindow("camera")
# @Description: createImage.py
# @Author: 孤烟逐云zjy
# @Date: 2020/5/3 9:49
# @SoftWare: PyCharm
# @CSDN: https://blog.csdn.net/zjy123078_zjy
# @博客园: https://www.cnblogs.com/guyan-2020/
import cv2 as cv
im = cv.LoadImage('./images/photo01.jpg') # get the img
thum = cv.CreateImage((im.width / 2, im.height / 2), 8, 3)
cv.Resize(im, thum)
cv.SaveImage('thum.jpg', thum)
import cv2
import sys
storage = cv2.CreateMemStorage()
image_path = "yusei.jpg"
img = cv2.imread(image_path)
hc = cv2.Load("../data/haarcascades/haarcascade_frontalface_default.xml")
faces = cv2.HaarDetectObjects(img, hc, storage, 1.1, 3, 0, (0, 0))
max = 0
maxh = 0
maxw = 0
resx = 0
resy = 0
for (x, y, w, h), n in faces:
if max < w * h:
maxw = w
maxh = h
resx = x
resy = y
max = w * h
sub = cv2.GetSubRect(img, (resx, resy, maxw, maxh))
cv2.SaveImage("face_" + sys.argv[1], sub)
#!/usr/bin/python
import cv2 #import the openCV lib to python
import serial #import the pyserial module
#Module -1: Image Processing
hc = cv2.imread(
'/home/george/PycharmProjects/Embeded image processing system/haarcascade_frontalface_alt2.xml'
)
img = cv2.imshow('/home/jayneil/beautiful-faces.jpg', 0)
faces = cv2.HaarDetectObjects(img, hc, cv2.CreateMemStorage())
a = 1
print(faces)
for (x, y, w, h), n in faces:
cv2.Rectangle(img, (x, y), (x + w, y + h), 255)
cv2.SaveImage("faces_detected.jpg", img)
dst = cv2.imread('faces_detected.jpg')
cv2.NamedWindow('Face Detected', cv2.CV_WINDOW_AUTOSIZE)
cv2.imshow('Face Detected', dst)
cv2.WaitKey(5000)
cv2.DestroyWindow('Face Detected')
#Module -2: Trigger Pyserial
if faces == []:
ser = serial.Serial('/dev/ttyUSB0', 9600)
print(ser)
ser.write('N')
else:
ser = serial.Serial('/dev/ttyUSB0', 9600)
import cv2 imageBuffer = cv2.LoadImage( 'images/digits_sudoku2.png' ) nW = 468 nH = 99 smallerImage = cv2.CreateImage( (nH, nW), imageBuffer.depth, imageBuffer.nChannels ) cv2.Resize( imageBuffer, smallerImage , interpolation=cv2.CV_INTER_CUBIC ) cv2.SaveImage( 'images/digits_sudoku3.png', smallerImage )
