I am using an OpenCV tracker to perform face tracking in videos, together with a face detector every few frames. If a face is detected by the face detector, I would like to update the tracker with the "detected" bounding box. I see that there is an option to enter a Rect in the C++ implementation but for some reason not in the python implementation as written in the opencv documentation. This is also an option when using dlib's correlation_tracker.
Currently, I can only initialize the tracker with a bounding box, but not update it with one in Python. if my tracker has drifted off the initial face it was tracking, I can't "bring it back" even if I know where the face is now (using my face detector). Is there a way to do this in python (e.g. should I kill the current tracker and init another with detected bounding box)?
I was looking for the very same thing and I found myself the solution to the problem by re-creating tracker each time successful detection occur. Please check following code. If something is not clear, feel free to ask for details:
import cv2 as cv
cap = cv.VideoCapture(0)
face_front_cascade = cv.CascadeClassifier("haarcascade_frontalface_alt.xml")
tracker = cv.TrackerKCF_create()
bbox = ()
while True:
ret,frame = cap.read()
#press S to capture the face
if cv.waitKey(20) & 0xFF == ord("s"):
frame_gray = cv.cvtColor(frame,cv.COLOR_BGR2GRAY)
face = face_front_cascade.detectMultiScale(frame_gray, scaleFactor=1.5, minNeighbors=3)
for (x, y, w, h) in face:
colour = (0,0,255)
stroke = 20
cv.rectangle(frame,(x,y),(x+w,y+h), colour, stroke)
bbox = (x,y,w,h)
tracker = cv.TrackerKCF_create() #overwrite old tracker
#trace face and draw box around it
if bbox:
tracker.init(frame, bbox)
ret, bbox = tracker.update(frame)
if ret:
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
#show result
cv.imshow("frame",frame)
#press ESC to exit
if cv.waitKey(20) & 0xFF ==27:
break
cap.release()
cv.destroyAllWindows()
Related
I am currently working on a face detection algorithm using MTCNN.
With the below code, I am able to show the frame containing my face:
`def run_detection(fast_mtcnn):
frames = []
frames_processed = 0
faces_detected = 0
batch_size = 60
start = time.time()
cap = cv2.VideoCapture(0)
while True:
__, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
faces = fast_mtcnn(frames)
frames_processed += len(frames)
faces_detected += len(faces)
frames = []
if len(faces) > 0:
for face in faces:
cv2.imshow('frame',face)
print(
f'Frames per second: {frames_processed / (time.time() - start):.3f},',
f'faces detected: {faces_detected}\r',
end=''
)
if cv2.waitKey(1) &0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()`
FYI, the cv2.imshow('frame',face) is just for me to put some value there as I would like to get its bouding box.
Which looks something like this(please forgive my silly looking face):
enter image description here
This I just to show that it is detecting the face and its taking out the frames relating to my face.
What my challenge is, is how to take the smaller frame face (containing my face) and get the edge coordinates of it to draw a bounding box for each person in the frame.
What I tried is the following:
cv2.rectangle(frame,
(face[0], face[1]),
(face[0]+face[2], face[1] + face[3]),
(0,155,255),
2)
Which i assume is completely wrong.
The solution will depend on how the points are returned by the face detection system which can vary depending on the library you use.
In this case the detected face contains the following information
x, y, w, h = face where x and y is a point, w is the width of the bounding box, and h is the height.
Hence you can draw a rectangle of the face as follows:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
cvlib library in python is well established and many people in research community uses it. I noticed that if threr is no face detected the (for) loop stops for exampe, if i have the following code:
cap = cv2.VideoCapture(0)
if not (cap.isOpened()):
print('Could not open video device')
#To set the resolution
vid_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
vid_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
while(True):
ret, frame = cap.read()
if not ret:
continue
faces, confidences = cv.detect_face(frame)
# loop through detected faces and add bounding box
for face in faces:
(startX,startY) = face[0],face[1]
(endX,endY) = face[2],face[3]
cv2.rectangle(frame, (startX,startY), (endX,endY), (0,255,0), 2)
crop_img = frame[startY-5:endY-5, startX-5:endX-5]```
print(faces)
cv2.imshow('object detected',frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
as Im printing (faces) the output would be somthing like this
[[392, 256, 480, 369]]
[[392, 256, 478, 369]]
[[392, 255, 478, 370]]
.
.
.
[[392, 255, 478, 370]]
However, as soon as I block the camera or move my head away from it, as there is no face detected, the for loop freezes or pauses till it sees a face to detect.
I need an if statement or any other condition to check this freeze or a pause to produce do something else.
I have found the answer to this question if we add an if statement before the for loop as faces is an int produces 1,2,3 depending on how many faces in front of the camera.
if len(faces) == 0:
print('no faces')
print(faces) # going to print 0
else:
for face in faces:
(startX,startY) = face[0],face[1]
(endX,endY) = face[2],face[3]
crop_img = frame[startY-5:endY-5, startX-5:endX-5]
cv2.rectangle(frame, (startX,startY), (endX,endY), (0,255,0), 2)
cv2.imshow('object detected',frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
You are displaying only the frames where face is detected. If no face is detected you are seeing the last frame where face is detected until the next time a face is detected in the coming frames.
Move the imshow out of for loop. For example,
# loop through detected faces and add bounding box
for face in faces:
(startX,startY) = face[0],face[1]
(endX,endY) = face[2],face[3]
cv2.rectangle(frame, (startX,startY), (endX,endY), (0,255,0), 2)
crop_img = frame[startY-5:endY-5, startX-5:endX-5]
print(faces)
cv2.imshow('object detected',frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
Checkout the complete example here.
I am trying to detect faces in a camera recorded video. When i did it with webcam video, it's working fine. But, with camera recorded video, the video gets rotated by -90 degree. Please suggest me, how do I get the actual video output for face detection?
import cv2
import sys
cascPath = sys.argv[1]
faceCascade = cv2.CascadeClassifier('C:/Users/HP/Anaconda2/pkgs/opencv-3.2.0-np112py27_204/Library/etc/haarcascades/haarcascade_frontalface_default.xml')
#video_capture = cv2.videoCapture(0)
video_capture = cv2.VideoCapture('C:/Users/HP/sample1.mp4')
w=int(video_capture.get(3))
h=int(video_capture.get(4))
#output = cv2.VideoWriter('output_1.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 60,frameSize = (w,h))
while True:
ret, frame = video_capture.read()
frame = rotateImage(frame,90)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray, 1.3, 5)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
#cv2.imshow('face',i)
#output.write(frame)
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
output.release()
cv2.destroyAllWindows()
In cv2 you can use the cv2.rotate function to rotate image as per your requirement
rotated=cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
for rotating video you can use cv2.flip(), this method take 3 Args and one of them is the rotating code(0,1,-1) you can check this link for more details:
https://www.geeksforgeeks.org/python-opencv-cv2-flip-method/
As you can see in the code below, it only detects the faces with haar cascade, I would like to know how I show the webcam how many people are currently detected.
For example, show in the corner of the webcam X people detected.
from __future__ import print_function
import cv2
cap = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
while (cap.isOpened()):
ret,frame = cap.read()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5,
flags=cv2.CASCADE_SCALE_IMAGE,minSize=(50, 50), maxSize=None)
if len(faces) > 0:
print("detected person!")
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x - 10, y - 20), (x + w + 10, y + h + 10), (0, 255, 0), 2)
roi_gray = frame[y-15:y + h+10, x-10:x + w+10]
cv2.imshow("imagem", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
I suspect everything shown in the code works just fine.
If so, you already know how many faces are detected with len(faces). You now only need to add this info into the video.
For this, I suggest you use the cv::putText function : https://docs.opencv.org/3.1.0/d6/d6e/group__imgproc__draw.html#ga5126f47f883d730f633d74f07456c576
You will then be able to add this on each frames that are read.
Side note: This might just be because of copy-pasting your code here, but pay attention to your indentation.
Simply displaying the count of len(faces) may not solve the purpose,as you can have instances wherein there are multiple bounding boxes drawn over the same face.Therefore, I would suggest you to perform Non Maximal Suppression(NMS) on the result of your detections, followed by incrementing a counter for each time one calls the NMS operation. The final count of the counter will give you a better and more accurate result.
I'm using a guide provided online with opencv2.4 that shows you how to detect faces with opencv2 and python. I followed the guide and understand what it says. However I can't seem to find the issue with my program because the video shows but now face is detected and the video is very clear. There are no errors. I ran in debug mode and the value faces remains a blank tuple so I'm assuming that means its not finding the face. What I don't understand is why and I think it has something to do with the hash table.
By hash table I mean the cascade xml file. I understand cascades are basically the guidelines for detecting the facial artifacts correct?
Links to the guides. The hash table i.e the xml file is on the github linked.
https://github.com/shantnu/FaceDetect/blob/master/haarcascade_frontalface_default.xml
https://realpython.com/blog/python/face-detection-in-python-using-a-webcam/
import cv2
import sys
import os
#cascPath = sys.argv[1]
cascPath = os.getcwd()+'facehash.xml'
faceCascade = cv2.CascadeClassifier(cascPath)
print faceCascade
video_capture = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE
)
cv2.cv
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
You have a wrong path to your xml classifier. (I guess you've changed the name to get a shorter form).
Instead of your cascPath:
cascPath = os.getcwd()+'facehash.xml'
Try this:
cascPath = "{base_path}/folder_with_your_xml/haarcascade_frontalface_default.xml".format(
base_path=os.path.abspath(os.path.dirname(__file__)))
And now it should work as well.