def detect_and_draw(img, cascade):
# allocate temporary images
#gray = cv.CreateImage((img.width,img.height), 8, 1)
global haar_scale
global min_neighbors
for image_scale in range(1,5,1):
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),cv.Round (img.height / image_scale)), 8, 1)
global noface
global current_time
global dailyfolder
current_time=time.strftime("%H:%M:%S")
dailyfolder=time.strftime("%F")
FileName="/Detected-Faces/"
FileName=dailyfolder+FileName+current_time+"_Image_Scale_"+str(image_scale)+ "_Min_Neighbors_" + str(min_neighbors) +".jpeg"
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = clock()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),haar_scale, min_neighbors, haar_flags, min_size)
t = clock() -t
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
#### DEBUG ###
#IO.LEDON()
#wait(10)
#IO.LEDOFF()
#save image update log
cv.SaveImage(FileName,img)
f.updatelog(t,image_scale,min_neighbors)
del(gray)
del(small_img)
else:
del(gray)
del(small_img)
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
global noface
global current_time
global dailyfolder
global imagenum
global t1
current_time=time.strftime("%H:%M:%S")
dailyfolder=time.strftime("%F")
FileName="/Detected-Faces/"
FileName=dailyfolder+FileName+current_time+ ".jpeg"
cropFileName = "/FaceCapture/"
cropFileName=dailyfolder+cropFileName
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = clock()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = clock() -t
if faces:
imagenum = imagenum +1
print ("face")
i = 0
for ((x, y, w, h), n) in faces:
i = i +1
cropFileName = cropFileName + "face"+ str(imagenum)+ "_" + str(i) + ".jpeg"
#cropFilename =cropFilename + 'face.jpeg'
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cropimg = img[int(y*image_scale):int((y+h)*image_scale), int(x*image_scale):int((x+w)*image_scale)]
cv.SaveImage(cropFileName,cropimg)
#cv.SaveImage(cropFileName,img)
#### DEBUG ###
#IO.LEDON()
#time.sleep(1)
#IO.LEDOFF()
#imagenum = imagenum+1
# save image ; update log
cv.SaveImage(FileName,img)
f.updatelog(imagenum,i,t,t1)
del(gray)
del(small_img)
else:
del(gray)
del(small_img)