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)