从色相饱和度直方图的皮肤检测 - OpenCV Python [英] Skin detection from hue-saturation histogram - OpenCV Python
问题描述
我正在使用python中的一个小程序来估计指向手势与来自单目相机的2D图片的方向，我使用OpenCV 2.3。
我知道这有点棘手，但我有动力！ :)
我的方法是fisrt使用面部检测来检测我确信有很多皮肤的区域：
img = cv2.imread（/ home / max / recordings / cameras / imageTEST.jpg，1）
img_hsv = cv2.cvtColor（img，cv2.COLOR_BGR2HSV）
hc1 = cv2.CascadeClassifier（/ home / max / haarcascade_frontalface_alt.xml）
faces1 = hc1.detectMultiScale（img）
在faces1中的（x，y，w，h）：
cv2 .rectangle（img，（x，y），（x + w，y + h），255）
crop_img = img [y + 2：y + w，x + 2：x + h]
我真的想使用这种方法，因为我希望我的检测对光的变化是鲁棒的。然后，我计算检测到的脸部的图片的色调饱和度直方图，以进行反投影：
roihist = cv2.calcHist （[crop_img]，[0,1]，None，[180,256]，[0,180,0,256]）
dst = cv2.calcBackProject（[img]，[0,1]，roihist ，[0,180,0,256]，1）
最后我可以阈值并跟踪头部和手部的斑点以估计指向的方向。
我的代码没有问题，但皮肤检测不到...
我做错了什么？
Thx为您提供帮助！
最大
我一直在通过可用的opencv的例子在网上最近（只是基本的东西为了乐趣）。我已经从面部识别（有趣，但太黑的盒子为我的喜好）移动手动选择在HSV空间roi，然后使用'camshift'跟踪。我仍然得到可变的结果，我不明白所以我也绘制所有的中间处理窗口，如hsv图像和backproject图像，还图形直方图窗口。突然，一切都清楚了 - 你可以看到计算机正在努力工作。
这里是我的工作代码python3.4，opencv3。您可以手动选择皮肤。
'cv2.calcBAckProject'函数可以很好地限制皮肤的功能。
import numpy as npimport cv2roiPts = [] track_mode = Falsetermination =（cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT，10，1）roiBox = Nonekernel = np.ones（（5，5），np.uint8）frame_width_in_px = 640number_of_histogram_elements = 16def selectROI（event，x，y，flags，param）：global track_mode，roiPts if（event == cv2.EVENT_LBUTTONDOWN）and（len（roiPts）== 4）：#reselecting ROI points so take out of tracking mode and empty current roipoints roiPts = [] track_mode = False if（event == cv2.EVENT_LBUTTONDOWN）and（len（roiPts）< 4）：#ROI point selection roiPts.append（[x，y]）cap = cv2.VideoCapture（0）cv2.namedWindow（frame）cv2.setMouseCallback（frame，selectROI）while True：ret，frame = cap.read（）if len（roiPts）<= 4和len（roiPts）> 0：for x，y in roiPts：cv2.circle（frame，（x，y），4，（0，255，0 ），1）＃为每个roi单击绘制小圆点如果len（roiPts）== 4和track_mode == False：#initialize的camshift＃将选定的点转换为一个盒子形状roiBox = np.array（roiPts，dtype = np .int32）s = roiBox.sum（axis = 1）tl = roiBox [np.argmin（s）] br = roiBox [np.argmax（s）]从图像中提取roi并计算组织roi = tl [1]：br [1]，tl [0]：br [0]] roi = cv2.cvtColor（roi，cv2.COLOR_BGR2HSV）＃roiHist = cv2.calcHist（[roi]，[0]，None， number_of_histogram_elements]，[0,180]）roiHist = cv2.normalize（roiHist，roiHist，0,255，cv2.NORM_MINMAX）roiBox =（tl [0]，tl [1]，br [0]，br [1] track_mode = True #ready for camshift if track_mode == True：#tracking mode hsv = cv2.cvtColor（frame，cv2.COLOR_BGR2HSV）backProj = cv2.calcBackProject（[hsv]，[0]，roiHist，[0，180]， 1）#perfrom一些降噪和平滑侵蚀= cv2.erode（backProj，kernel，iterations = 2）dilate = cv2.dilate（erosion，kernel，iterations = 2）（r，roiBox）= cv2.CamShift（dilate，roiBox ，终止）#this接受prev roiBox并计算新的roiBox pts = np.int0（cv2.boxPoints（r））cv2.polylines（frame，[pts]，True，（0，255，0），2）#tracking box cv2.polylines（backProj，[pts]，True，（0，255，0），2）#tracking box cv2.polylines（dilate，[pts]，True，（0，255，0），2） box cv2.polylines（hsv，[pts]，True，（0，255，0），2）#tracking box＃plot histogram窗口中的直方图折线x = np.linspace（0,640，number_of_histogram_elements，dtype = np.int32）y = roiHist.flatten（）。astype（np.int32，copy = False）-255 #note frame height需要大于255，这是最大histo值y = np.absolute（y）pts2 = np.stack x，y），axis = 1）cv2.polylines（frame，[pts2]，False，（0,255,0），2）cv2.polylines（hsv，[pts2]，False，（0,255,0） ，2）cv2.imshow（backproject，backProj）cv2.imshow（dilate，dilate）cv2.imshow（hsv，hsv）cv2.imshow（frame，frame）if cv2.waitKey & 0xFF == ord（'q'）：break＃当一切完成后，释放capturecap.release（）cv2.destroyAllWindows（）I'm working on a little program in python to estimate the direction of pointing gestures with 2D picture from a monocular camera and I'm using OpenCV 2.3. I know it's a bit tricky but I'm motivated! :) My approach is fisrt to use the face detection to detect an area into which I'm sure there is a lot of skin:
img = cv2.imread("/home/max/recordings/cameras/imageTEST.jpg",1) img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) hc1 = cv2.CascadeClassifier("/home/max/haarcascade_frontalface_alt.xml") faces1 = hc1.detectMultiScale(img) for (x,y,w,h) in faces1: cv2.rectangle(img, (x,y), (x+w,y+h), 255) crop_img = img[y+2:y+w, x+2:x+h]I really want to use this method because I want my detection to be robust to light variation. Then I compute the hue-saturation histogram of the picture of the detected face to make a back projection:
roihist = cv2.calcHist([crop_img],[0,1], None, [180, 256], [0, 180, 0, 256] ) dst = cv2.calcBackProject([img],[0,1],roihist,[0,180,0,256],1)And finally I would be able to binarize the picture with a threshold and track the head and hands blobs to estimate the direction of pointing. I've no problem with my code but the skin is not detected... What am I doing wrong? Thx for your help!
Max
解决方案I've been working through the available opencv examples on the web lately (just the basic stuff for fun). I've moved of from the face recognition (interesting, but too black box for my liking) to manually selecting the roi in the HSV space, then using 'camshift' to track. I was still getting variable results I didn't understand so I also plot all the intermediate processing windows such as the hsv image and the backproject image, also graph the histogram across the windows. Suddenly all is clear - you can see exactly what the computer is trying to work with.
Here is my working code for python3.4, opencv3. You can manually select the skin. Credit mostly to other examples I've found on the web.
The 'cv2.calcBAckProject' function thresholds out the skin features nicely.
import numpy as np import cv2 roiPts = [] track_mode = False termination = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) roiBox = None kernel = np.ones((5, 5), np.uint8) frame_width_in_px = 640 number_of_histogram_elements=16 def selectROI(event, x,y,flags,param): global track_mode, roiPts if (event == cv2.EVENT_LBUTTONDOWN) and (len(roiPts)==4): #reselecting ROI points so take out of tracking mode and empty current roipoints roiPts=[] track_mode = False if (event==cv2.EVENT_LBUTTONDOWN) and (len(roiPts) < 4): #ROI point selection roiPts.append([x, y]) cap = cv2.VideoCapture(0) cv2.namedWindow("frame") cv2.setMouseCallback("frame", selectROI) while True: ret, frame = cap.read() if len(roiPts)<=4 and len(roiPts)>0: for x,y in roiPts: cv2.circle(frame, (x,y), 4, (0, 255, 0), 1) # draw small circle for each roi click if len(roiPts)==4 and track_mode==False: #initialize the camshift # convert the selected points to a box shape roiBox = np.array(roiPts, dtype=np.int32) s = roiBox.sum(axis=1) tl = roiBox[np.argmin(s)] br = roiBox[np.argmax(s)] #extract the roi from the image and calculate the histograme roi = frame[tl[1]:br[1], tl[0]:br[0]] roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV) # roiHist = cv2.calcHist([roi], [0], None, [number_of_histogram_elements], [0, 180]) roiHist = cv2.normalize(roiHist, roiHist, 0, 255, cv2.NORM_MINMAX) roiBox = (tl[0], tl[1], br[0], br[1]) track_mode = True #ready for camshift if track_mode == True: #tracking mode hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) backProj = cv2.calcBackProject([hsv], [0], roiHist, [0, 180], 1) #perfrom some noise reduction and smoothing erosion = cv2.erode(backProj, kernel, iterations=2) dilate = cv2.dilate(erosion, kernel, iterations=2) (r, roiBox) = cv2.CamShift(dilate, roiBox, termination) #this takes prev roiBox and calculates the new roiBox pts = np.int0(cv2.boxPoints(r)) cv2.polylines(frame, [pts], True, (0, 255, 0), 2) #tracking box cv2.polylines(backProj, [pts], True, (0, 255, 0), 2) #tracking box cv2.polylines(dilate, [pts], True, (0, 255, 0), 2) #tracking box cv2.polylines(hsv, [pts], True, (0, 255, 0), 2) #tracking box # plot histogram polyline across the windows x = np.linspace(0,640,number_of_histogram_elements,dtype=np.int32) y = roiHist.flatten().astype(np.int32, copy=False)-255 #note frame height needs to be greater than 255 which is the max histo value y=np.absolute(y) pts2 = np.stack((x, y), axis=1) cv2.polylines(frame, [pts2], False, (0, 255, 0), 2) cv2.polylines(hsv, [pts2], False, (0, 255, 0), 2) cv2.imshow("backproject", backProj) cv2.imshow("dilate", dilate) cv2.imshow("hsv", hsv) cv2.imshow("frame", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # When everything done, release the capture cap.release() cv2.destroyAllWindows()
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