I am learning the OpenCV. Here is my code:
import cv2
face_patterns = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
sample_image = cv2.imread('1.jpg')
gray = cv2.cvtColor(sample_image,cv2.COLOR_RGB2GRAY)
faces = face_patterns.detectMultiScale(gray,1.3,5)
print(len(faces))
for (x, y, w, h) in faces:
cv2.rectangle(sample_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imwrite('result.jpg', sample_image)
If I use the picture A, I could get a lot of faces, if I use the picture B, I get none.
I changed argument in detectMultiScale(gray,1.3,5) many times, it still doesn't work.
Picture A
Picture A Result
Picture B no face
I see this more as a problem of Cv2 module itself. There are better models than HAAR CASCADES for detecting faces. face_recognition library is also very useful to detect and recognize face. It uses hog as default model. You can also use cnn for better accuracy but the detection process will be slow.
Find more here.
import cv2
import face_recognition as fr
sample_image = fr.load_image_file("1.jpg")
unknown_face_loc = fr.face_locations(sample_image, model="hog")
print(len(unknown_face_loc)) #detected face count
for faceloc in unknown_face_loc:
y1, x2, y2, x1 = faceloc
cv2.rectangle(sample_image, (x1, y1), (x2, y2), (0, 0, 255), 2)
sample_image = sample_image[:, :, ::-1] #converting bgr image to rbg
cv2.imwrite("result.jpg", sample_image)
Instead of -
faces = face_patterns.detectMultiScale(gray,1.3,5)
Try Using -
faces = face_patterns.detectMultiScale(blackandwhite,1.3,5)
If the problem occurs even after this check out my code for face detection.
It uses hog as default model. You can also use cnn for better accuracy but the detection process will be slow.
cascade_classifier = cv2.CascadeClassifier('haarcascades/haarcascade_eye.xml')
cap = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, 0)
detections = cascade_classifier.detectMultiScale(gray,scaleFactor=1.3,minNeighbors=5)
if(len(detections) > 0):
(x,y,w,h) = detections[0]
frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
# for (x,y,w,h) in detections:
# frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
# Display the resulting frame
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()```
Related
hi guys I want to implement (zoom in and zoom out) like digital camera to the detected faces while real-time capturing using opencv, is there is any way I can do it without just cropping the frame then display it.
here is my code ... ,,,
import cv2
# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# To capture video from webcam.
cap = cv2.VideoCapture(0)
# To use a video file as input
# cap = cv2.VideoCapture('filename.mp4')
while True:
# Read the frame
_, img = cap.read()
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect the faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw the rectangle around each face
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
# Display
cv2.imshow('img', img)
# Stop if escape key is pressed
k = cv2.waitKey(30) & 0xff
if k==27:
break
# Release the VideoCapture object
cap.release()
I am trying to use face detection but I do not want the video feed window to open up when I use videocapture, this is the code I'm working on:
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imshow('img', img)
k = cv2.waitKey(30) & 0xff
if k==27:
break
cap.release()
I just want to somehow disable the video feed window that opens up automatically and instead get an output on the command line every time it detects a face.
I am using the code from here: https://github.com/adarsh1021/facedetection.
Thank you.
Reomve cv2.imshow('img', img) and replace it with print(faces)
cv2.imshow() is responsible for opening of image window.
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.
camera = webcam; % Connect to the camera
nnet = alexnet; % Load the neural net
while true
picture = camera.snapshot; % Take a picture
picture = imresize(picture,[227,227]); % Resize the picture
label = classify(nnet, picture); % Classify the picture
image(picture); % Show the picture
title(char(label)); % Show the label
drawnow;
end
I found this matlab code in the internet. It displays a window with the picture from a webcam and very quickly also names the things in the picture ("keyboard","bootle","pencil","clock"...). I want to do that in python.
So far I have this:
import cv2
import sys
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
video_capture = cv2.VideoCapture(0)
while True:
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
)
# 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('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
This is alreay very similar, but only detecting faces. The matlab code uses alexnet. I guess this is a pre-trained network based on imagenet data (http://www.image-net.org/). But it is no longer available.
How would I do this in python?
(There has been a similar question here, but it is 4 yrs. old and I think there are newer techniques now).
With the "tensorflow" package and the pre-trained network "vgg16", the solution is quite easy.
See https://github.com/machrisaa/tensorflow-vgg/blob/master/test_vgg16.py