I've been developing a face detection app integrated with ROS to be used with DRONES (bebop parrot to be exact). The code itself can be found on google and what it does is, basically, shows every face that appear on screen. My problem is: I want the drone to follow my face (and only mine) but as i said before, the code can detect multiples faces at time.
Here's the code:
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# some people can just use the haarcascade_frontalface_default.xml without specifying the path
test = face_cascade.load('../../../../opencv/data/haarcascades/haarcascade_frontalface_default.xml')
# start the video capture
video_capture = cv2.VideoCapture(0)
# while-loop to detect face on webcam until you press 'q'.
while not rospy.is_shutdown():
# Capture frame-by-frame
ret, frame = video_capture.read()
frame = imutils.resize(frame, width=600)
#convert the frame (of the webcam) to gray)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mask = cv2.erode(gray, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
# detecting the faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_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)
x_imagem = x + (w/2)
y_imagem = y + (h/2)
cv2.circle(frame, (x+(w/2), y+(h/2)), 5, (0,0,255), -1)
#600 x 450;
if(x_imagem > 200 and x_imagem < 400):
rospy.loginfo("CENTRO")
elif(x_imagem < 200): #ROSTO PRA ESQUERDA, ENTAO VAI PARA DIREITA
rospy.loginfo("ROSTO NA ESQUERDA")
pub_face.publish("esq")
elif(x_imagem > 400): #ROSTO PRA DIREITA, ENTAO VAI PARA ESQUERDA
rospy.loginfo("ROSTO NA DIREITA")
pub_face.publish("dir")
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
rospy.loginfo("FIM DO PROGRAMA DE DETECCAO DE ROSTOS")
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
As you can see, we have an array of FACES at:
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
The code will draw a rectangle where the face is, is there a way that I can make it show only the biggest rectangle (in that case, my face)?
Hope I could make it clear!
Is there a way that I can make it show only the biggest rectangle (in that case, my face)?
Iterate thought the list of 'faces' and calculate the area of each Region of Interest (ROI), and grab the largest area.
Related
Problems
doesn't work well
When I use my code with my Image, it doesn't work well.
I only edited 'wc' and 'hc' from OpenCV DOC
import glob
import cv2 as cv
import numpy as np
wc = 7
hc = 4
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((wc * hc, 3), np.float32)
objp[:, :2] = np.mgrid[0:hc, 0:wc].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob. Glob('1.jpg')
for fname in images:
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (hc, wc), None)
# If found, add object points, image points (after refining them)
print(ret, wc, hc)
if True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (20, 20), (-1, -1),
criteria) # image, corners, winSize, zeroZone, criteria
imgpoints.append(corners2)
# Draw and display the corners
cv.drawChessboardCorners(img, (hc, wc), corners2, ret)
cv.imwrite('ChessboardCorners.png', img)
cv.waitKey(0)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
img = cv.imread('1.jpg')
print(img.shape[:2])
h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
# undistort
dst = cv.undistort(img, mtx, dist, None, newcameramtx)
# crop the image
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
cv.imwrite('calibresult.png', dst)
cv.waitKey(0)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2) / len(imgpoints2)
mean_error += error
print("total error: {}".format(mean_error / len(objpoints)))
print("\n\n", fname, "claer")
cv.destroyAllWindows()
exit(0)
original image - not well
ChessboardCorners - (I'm not sure that this is not well)
calibresult image - not well
works well with other images
But, when I use my code with the Image which was in the example in OpenCV DOC, it works well.
import glob
import cv2 as cv
import numpy as np
wc = 6
hc = 7
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((wc * hc, 3), np.float32)
objp[:, :2] = np.mgrid[0:hc, 0:wc].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob. Glob('img.png')
for fname in images:
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (hc, wc), None)
# If found, add object points, image points (after refining them)
print(ret, wc, hc)
if True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (20, 20), (-1, -1),
criteria) # image, corners, winSize, zeroZone, criteria
imgpoints.append(corners2)
# Draw and display the corners
cv.drawChessboardCorners(img, (hc, wc), corners2, ret)
cv.imwrite('ChessboardCorners.png', img)
cv.waitKey(0)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
img = cv.imread('img.png')
print(img.shape[:2])
h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
# undistort
dst = cv.undistort(img, mtx, dist, None, newcameramtx)
# crop the image
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
cv.imwrite('calibresult.png', dst)
cv.waitKey(0)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2) / len(imgpoints2)
mean_error += error
print("total error: {}".format(mean_error / len(objpoints)))
print("\n\n", fname, "claer")
cv.destroyAllWindows()
exit(0)
I removed the images because "Your question appears to be spam."
Please see the images on OpenCV DOC
Please, give me the solution to this problem.
Do I need to modify the parameters, or what should I do?
Is my chessboard wrong?
Below is what I have tried.
First, I tried to find correct numbers of 'wc' and 'hc'
I used this code to find.
import glob
import cv2 as cv
import numpy as np
for i in range(3, 50):
for j in range(i + 1, 50): # I used this code becuase I found that the order of the variables does not matter last time.
wc = i
hc = j
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((wc * hc, 3), np.float32)
objp[:, :2] = np.mgrid[0:hc, 0:wc].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('1.jpg')
for fname in images:
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (hc, wc), None)
# If found, add object points, image points (after refining them)
print(ret, wc, hc)
And the result here:
False 3 4
False 3 5
False 3 6
···
False 4 5
False 4 6
**True 4 7**
False 4 8
False 4 9
···
False 47 48
False 47 49
False 48 49
Process finished with exit code 0
I also found that the Image which was in the example in OpenCV DOC has another 'wc' and 'hc', (4, 4).
And result here:
ChessboardCorners by (4, 4
calibresult by (4, 4)
So, I'm expecting that the 'wc'and 'hc' of my Image (4, 7) might be small.
Should I increase the max and do a brute-force search again?
I can't tell for sure, but it looks like you are only using a single input image for calibration. If that's true, try increasing to at least 5 images (more would be better) and see if that helps. The images should be at different angles and distances.
I notice that your lens has significant distortion. A few suggestions for getting that to work well:
Use the rational model for distortion - the basic kappa 1, kappa 2 model won't do well.
Your data set will need to include image points from all parts of the image, including near the edges and corners of the image. This can be difficult/impossible to achieve using the normal chessboard pattern (because the entire pattern must be visible in the image) - I suggest using the ChAruco calibration pattern/functions. This uses a modified chessboard pattern that includes Aruco markers embedded in the white squares, which allows for partial patterns to be used.
Note that the wc and hc parameters you are searching for are used to describe the chessboard pattern width and height. This should be known to you ahead of time and you shouldn't need to search for it.
I am interested in the human following using a real robot.
I'd like to use the color of clothes as a key feature to identify the target person in front of the robot to follow him/ her but I am suffering due to it is a weak feature with a very simple illumination changing. So, I need to alter this algorithm to another or update values (RGB) online in real-time but I don't have enough experience with image processing.
this is my full code for color detection:
import cv2
import numpy as np
from imutils.video import FPS
# capturing video through webcam
import time
cap = cv2.VideoCapture(0)
width = cap.get(3) # float
height = cap.get(4) # float
print width, height
time.sleep(2.0)
fps = FPS().start()
while (1):
_, img = cap.read()
if _ is True:
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
else:
continue
# blue color
blue_lower = np.array([99, 115, 150], np.uint8)
blue_upper = np.array([110, 255, 255], np.uint8)
blue = cv2.inRange(hsv, blue_lower, blue_upper)
kernal = np.ones((5, 5), "uint8")
blue = cv2.dilate(blue, kernal)
res_blue = cv2.bitwise_and(img, img, mask=blue)
# Tracking blue
(_, contours, hierarchy) = cv2.findContours(blue, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for pic, contour in enumerate(contours):
area = cv2.contourArea(contour)
if (area > 300):
x, y, w, h = cv2.boundingRect(contour)
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.putText(img, "Blue Colour", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0))
cv2.imshow("Color Tracking", img)
if cv2.waitKey(10) & 0xFF == ord('q'):
cap.release()
cv2.destroyAllWindows()
break
fps.update()
# stop the timer and display FPS information
fps.stop()
# print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
# print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
these are the outputs:
1- recognize a person by his clothes color
2- it is lost, the illumination changing is a very simple not severe
Any ideas or suggestions will be appreciated
It does look like you need to use a bit more advanced color similarity function to handle complex cases. Delta E will be the right starting point.
Proper threshold or several colors with associated thresholds will help to achieve pretty accurate results:
See the list of colours on the right side
Complete example.
Though I realize that there is no "one size fits all" setting for OpenCV's HoughCircles, I'm having quite a bit of trouble finding even one reasonable set of parameters.
My input image is the following photo, which contains some pretty obvious big black circles, as well as some noise around it:
I tried playing with the p1 and p2 arguments, to try and get precisely the four black circles detected (and optionally the tape roll at the top -- that's not required but I wouldn't mind if it matched either).
import numpy as np
import cv2
gray = frame = cv2.imread('testframe2.png')
gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(5,5),0)
# gray = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 5, 2)
p1 = 200
p2 = 55
while True:
out = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 10, param1=p1, param2=p2, minRadius=10, maxRadius=0)
if circles is not None:
for (x, y, r) in circles[0]:
cv2.rectangle(out, (int(x - r), int(y - r)), (int(x + r), int(y + r)), (255, 0, 0))
cv2.putText(out, "r = %d" % int(r), (int(x + r), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 0, 0))
cv2.putText(out, "p: (%d, %d)" % (p1, p2), (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 4)
cv2.imshow('debug', out)
if cv2.waitKey(0) & 0xFF == ord('x'):
break
elif cv2.waitKey(0) & 0xFF == ord('q'):
p1 += 5
elif cv2.waitKey(0) & 0xFF == ord('a'):
p1 -= 5
elif cv2.waitKey(0) & 0xFF == ord('w'):
p2 += 5
elif cv2.waitKey(0) & 0xFF == ord('s'):
p2 -= 5
cv2.destroyAllWindows()
It seems the best I can do is detect the big circle several times but not the small one at all, or get a lot of false positives:
I've Read The F** Manual but it does not help me further: how do I somewhat reliably detect the circles and nothing but the circles in this image?
There was a bit of manual tweaking with the HoughCircles params, but this gives the result you're looking for. I've used the OpenCV Wrapper library which just simplifies some things.
import cv2
import opencv_wrapper as cvw
import numpy as np
frame = cv2.imread("tape.png")
gray = cvw.bgr2gray(frame)
thresh = cvw.threshold_otsu(gray, inverse=True)
opened = cvw.morph_open(thresh, 9)
circles = cv2.HoughCircles(
opened, cv2.HOUGH_GRADIENT, 1, 10, param1=100, param2=17, minRadius=5, maxRadius=-1
)
if circles is not None:
circles = np.around(circles).astype(int)
for circle in circles[0]:
cv2.floodFill(thresh, None, (circle[0], circle[1]), 155)
only_circles = thresh.copy()
only_circles[only_circles != 155] = 0
contours = cvw.find_external_contours(only_circles)
cvw.draw_contours(frame, contours, (255, 0, 255), thickness=2)
cv2.imwrite("tape_result.png", frame)
I used HoughCircles to find just the centers, as suggested in the documentation note.
I then used floodFill to fill the circles. Note that the left-most circle is very close to the edge. If the image was blurred, the flood filling would go into the background.
Disclosure: I'm the author of OpenCV Wrapper. Haven't added Hough Circles and flood filling yet.
I’m researching the subject of extracting the information from ID cards and have found a suitable algorithm to locate the face on the front. As it is, OpenCV has Haar cascades for that, but I’m unsure what can be used to extract the full rectangle that person is in instead of just the face (as is done in https://github.com/deepc94/photo-id-ocr). The few ideas that I’m yet to test are:
Find second largest rectangle that’s inside the card containing the face rect
Do “explode” of the face rectangle until it hits the boundary
Play around with filters to see what can be seen
What can be recommended to try here as well? Any thoughts, ideas or even existing examples are fine.
Normal approach:
import cv2
import numpy as np
import matplotlib.pyplot as plt
image = cv2.imread("a.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_,thresh = cv2.threshold(gray,128,255,cv2.THRESH_BINARY)
cv2.imshow("thresh",thresh)
thresh = cv2.bitwise_not(thresh)
element = cv2.getStructuringElement(shape=cv2.MORPH_RECT, ksize=(7, 7))
dilate = cv2.dilate(thresh,element,6)
cv2.imshow("dilate",dilate)
erode = cv2.erode(dilate,element,6)
cv2.imshow("erode",erode)
morph_img = thresh.copy()
cv2.morphologyEx(src=erode, op=cv2.MORPH_CLOSE, kernel=element, dst=morph_img)
cv2.imshow("morph_img",morph_img)
_,contours,_ = cv2.findContours(morph_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
areas = [cv2.contourArea(c) for c in contours]
sorted_areas = np.sort(areas)
cnt=contours[areas.index(sorted_areas[-3])] #the third biggest contour is the face
r = cv2.boundingRect(cnt)
cv2.rectangle(image,(r[0],r[1]),(r[0]+r[2],r[1]+r[3]),(0,0,255),2)
cv2.imshow("img",image)
cv2.waitKey(0)
cv2.destroyAllWindows()
I found the first two biggest contours are the boundary, the third biggest contour is the face. Result:
There is also another way to investigate the image, using sum of pixel values by axises:
x_hist = np.sum(morph_img,axis=0).tolist()
plt.plot(x_hist)
plt.ylabel('sum of pixel values by X-axis')
plt.show()
y_hist = np.sum(morph_img,axis=1).tolist()
plt.plot(y_hist)
plt.ylabel('sum of pixel values by Y-axis')
plt.show()
Base on those pixel sums over 2 asixes, you can crop the region you want by setting thresholds for it.
Haarcascades approach (The most simple)
# Using cascade Classifiers
import numpy as np
import cv2
# We point OpenCV's CascadeClassifier function to where our
# classifier (XML file format) is stored
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Load our image then convert it to grayscale
image = cv2.imread('./your/image/path.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Original image', image)
# Our classifier returns the ROI of the detected face as a tuple
# It stores the top left coordinate and the bottom right coordiantes
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
# When no faces detected, face_classifier returns and empty tuple
if faces is ():
print("No faces found")
# We iterate through our faces array and draw a rectangle
# over each face in faces
for (x, y, w, h) in faces:
x = x - 25 # Padding trick to take the whole face not just Haarcascades points
y = y - 40 # Same here...
cv2.rectangle(image, (x, y), (x + w + 50, y + h + 70), (27, 200, 10), 2)
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Link to the haarcascade_frontalface_default file
update to #Sanix darker code,
# Using cascade Classifiers
import numpy as np
import cv2
img = cv2.imread('link_to_your_image')
face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
scale_percent = 60 # percent of original size
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
image = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# face classifier
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
# When no faces detected, face_classifier returns and empty tuple
if faces is ():
print("No faces found")
# We iterate through our faces array and draw a rectangle
# over each face in faces
for (x, y, w, h) in faces:
x = x - 25 # Padding trick to take the whole face not just Haarcascades points
y = y - 40 # Same here...
cv2.rectangle(image, (x, y), (x + w + 50, y + h + 70), (27, 200, 10), 2)
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# if you want to crop the face use below code
for (x, y, width, height) in faces:
roi = image[y:y+height, x:x+width]
cv2.imwrite("face.png", roi)
I want to read text on the object. But OCR program can't recognize it. When I give the small part, it can recognize. I have to transform circle text to linear text. How can I do this? Thanks.
you can transform the image from Cartesian coordinate system to Polar coordinate system to prepare circle path text image for OCR program. This function logPolar() can help.
Here are some steps to prepare circle path text image:
Find the circles' centers using HoughCircles().
Get the mean and do some offset, so get the center.
(Optinal) Crop a square of the image from the center.
Do logPolar(), then rotate it if necessary.
After detect circles and get the mean of centers and do offset.
The croped image:
After logPolar() and rotate()
My Python3-OpenCV3.3 code is presented here, maybe it helps.
#!/usr/bin/python3
# 2017.10.10 12:44:37 CST
# 2017.10.10 14:08:57 CST
import cv2
import numpy as np
##(1) Read and resize the original image(too big)
img = cv2.imread("circle.png")
img = cv2.resize(img, (W//4, H//4))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
## (2) Detect circles
circles = cv2.HoughCircles(gray, method=cv2.HOUGH_GRADIENT, dp=1, minDist=3, circles=None, param1=200, param2=100, minRadius = 200, maxRadius=0 )
## make canvas
canvas = img.copy()
## (3) Get the mean of centers and do offset
circles = np.int0(np.array(circles))
x,y,r = 0,0,0
for ptx,pty, radius in circles[0]:
cv2.circle(canvas, (ptx,pty), radius, (0,255,0), 1, 16)
x += ptx
y += pty
r += radius
cnt = len(circles[0])
x = x//cnt
y = y//cnt
r = r//cnt
x+=5
y-=7
## (4) Draw the labels in red
for r in range(100, r, 20):
cv2.circle(canvas, (x,y), r, (0, 0, 255), 3, cv2.LINE_AA)
cv2.circle(canvas, (x,y), 3, (0,0,255), -1)
## (5) Crop the image
dr = r + 20
croped = img[y-dr:y+dr+1, x-dr:x+dr+1].copy()
## (6) logPolar and rotate
polar = cv2.logPolar(croped, (dr,dr),80, cv2.WARP_FILL_OUTLIERS )
rotated = cv2.rotate(polar, cv2.ROTATE_90_COUNTERCLOCKWISE)
## (7) Display the result
cv2.imshow("Canvas", canvas)
cv2.imshow("croped", croped)
cv2.imshow("polar", polar)
cv2.imshow("rotated", rotated)
cv2.waitKey();cv2.destroyAllWindows()