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@sauyon

sauyon/face.py

Last active February 22, 2018 18:45
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face.py
import cv2
import numpy as np
import time
#Open Camera object
cap = cv2.VideoCapture(0)
#Set properties
#cap.set(cv2.CAP_PROP_SATURATION, 0)
#cap.set(cv2.CAP_PROP_HUE, 2)
def nothing(x):
pass
# Function to find angle between two vectors
def Angle(v1,v2):
dot = np.dot(v1,v2)
x_modulus = np.sqrt((v1*v1).sum())
y_modulus = np.sqrt((v2*v2).sum())
cos_angle = dot / x_modulus / y_modulus
angle = np.degrees(np.arccos(cos_angle))
return angle
# Function to find distance between two points in a list of lists
def FindDistance(A,B):
return np.sqrt(np.power((A[0][0]-B[0][0]),2) + np.power((A[0][1]-B[0][1]),2))
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_eye_tree_eyeglasses.xml')
while(True):
#Measure execution time
start_time = time.time()
#Capture frames from the camera
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = frame[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
##### Show final image ########
cv2.imshow('image',frame)
###############################
#Print execution time
#print time.time()-start_time
#close the output video by pressing 'ESC'
k = cv2.waitKey(10) & 0xFF
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
Raw
hand.py
import cv2
import numpy as np
import time
#Open Camera object
cap = cv2.VideoCapture(0)
#Decrease frame size
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1000)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 600)
def nothing(x):
pass
# Function to find angle between two vectors
def Angle(v1,v2):
dot = np.dot(v1,v2)
x_modulus = np.sqrt((v1*v1).sum())
y_modulus = np.sqrt((v2*v2).sum())
cos_angle = dot / x_modulus / y_modulus
angle = np.degrees(np.arccos(cos_angle))
return angle
# Function to find distance between two points in a list of lists
def FindDistance(A,B):
return np.sqrt(np.power((A[0][0]-B[0][0]),2) + np.power((A[0][1]-B[0][1]),2))
# Creating a window for HSV track bars
cv2.namedWindow('HSV_TrackBar')
# Starting with 100's to prevent error while masking
h,s,v = 100,100,100
# Creating track bar
cv2.createTrackbar('h', 'HSV_TrackBar',0,179,nothing)
cv2.createTrackbar('s', 'HSV_TrackBar',0,255,nothing)
cv2.createTrackbar('v', 'HSV_TrackBar',0,255,nothing)
while(1):
#Measure execution time
start_time = time.time()
#Capture frames from the camera
ret, frame = cap.read()
#Blur the image
blur = cv2.blur(frame,(3,3))
#Convert to HSV color space
hsv = cv2.cvtColor(blur,cv2.COLOR_BGR2HSV)
#Create a binary image with where white will be skin colors and rest is black
mask2 = cv2.inRange(hsv,np.array([2,50,50]),np.array([15,255,255]))
#Kernel matrices for morphological transformation
kernel_square = np.ones((11,11),np.uint8)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
#Perform morphological transformations to filter out the background noise
#Dilation increase skin color area
#Erosion increase skin color area
dilation = cv2.dilate(mask2,kernel_ellipse,iterations = 1)
erosion = cv2.erode(dilation,kernel_square,iterations = 1)
dilation2 = cv2.dilate(erosion,kernel_ellipse,iterations = 1)
filtered = cv2.medianBlur(dilation2,5)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(8,8))
dilation2 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)
kernel_ellipse= cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
dilation3 = cv2.dilate(filtered,kernel_ellipse,iterations = 1)
median = cv2.medianBlur(dilation2,5)
ret,thresh = cv2.threshold(median,127,255,0)
#Find contours of the filtered frame
img, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#Draw Contours
#cv2.drawContours(frame, cnt, -1, (122,122,0), 3)
#cv2.imshow('Dilation',median)
#Find Max contour area (Assume that hand is in the frame)
max_area=100
ci=0
for i in range(len(contours)):
cnt=contours[i]
area = cv2.contourArea(cnt)
if(area>max_area):
max_area=area
ci=i
#Largest area contour
cnts = contours[ci]
#Find convex hull
hull = cv2.convexHull(cnts)
#Find convex defects
hull2 = cv2.convexHull(cnts,returnPoints = False)
defects = cv2.convexityDefects(cnts,hull2)
#Get defect points and draw them in the original image
FarDefect = []
for i in range(defects.shape[0]):
s,e,f,d = defects[i,0]
start = tuple(cnts[s][0])
end = tuple(cnts[e][0])
far = tuple(cnts[f][0])
FarDefect.append(far)
cv2.line(frame,start,end,[0,255,0],1)
cv2.circle(frame,far,10,[100,255,255],3)
#Find moments of the largest contour
moments = cv2.moments(cnts)
#Central mass of first order moments
if moments['m00']!=0:
cx = int(moments['m10']/moments['m00']) # cx = M10/M00
cy = int(moments['m01']/moments['m00']) # cy = M01/M00
centerMass=(cx,cy)
#Draw center mass
cv2.circle(frame,centerMass,7,[100,0,255],2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame,'Center',tuple(centerMass),font,2,(255,255,255),2)
#Distance from each finger defect(finger webbing) to the center mass
distanceBetweenDefectsToCenter = []
for i in range(0,len(FarDefect)):
x = np.array(FarDefect[i])
centerMass = np.array(centerMass)
distance = np.sqrt(np.power(x[0]-centerMass[0],2)+np.power(x[1]-centerMass[1],2))
distanceBetweenDefectsToCenter.append(distance)
#Get an average of three shortest distances from finger webbing to center mass
sortedDefectsDistances = sorted(distanceBetweenDefectsToCenter)
AverageDefectDistance = np.mean(sortedDefectsDistances[0:2])
#Get fingertip points from contour hull
#If points are in proximity of 80 pixels, consider as a single point in the group
finger = []
for i in range(0,len(hull)-1):
if (np.absolute(hull[i][0][0] - hull[i+1][0][0]) > 80) or ( np.absolute(hull[i][0][1] - hull[i+1][0][1]) > 80):
if hull[i][0][1] <500 :
finger.append(hull[i][0])
#The fingertip points are 5 hull points with largest y coordinates
finger = sorted(finger,key=lambda x: x[1])
fingers = finger[0:5]
#Calculate distance of each finger tip to the center mass
fingerDistance = []
for i in range(0,len(fingers)):
distance = np.sqrt(np.power(fingers[i][0]-centerMass[0],2)+np.power(fingers[i][1]-centerMass[0],2))
fingerDistance.append(distance)
#Finger is pointed/raised if the distance of between fingertip to the center mass is larger
#than the distance of average finger webbing to center mass by 130 pixels
result = 0
for i in range(0,len(fingers)):
if fingerDistance[i] > AverageDefectDistance+130:
result = result +1
#Print number of pointed fingers
cv2.putText(frame,str(result),(100,100),font,2,(255,255,255),2)
#show height raised fingers
#cv2.putText(frame,'finger1',tuple(finger[0]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger2',tuple(finger[1]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger3',tuple(finger[2]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger4',tuple(finger[3]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger5',tuple(finger[4]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger6',tuple(finger[5]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger7',tuple(finger[6]),font,2,(255,255,255),2)
#cv2.putText(frame,'finger8',tuple(finger[7]),font,2,(255,255,255),2)
#Print bounding rectangle
x,y,w,h = cv2.boundingRect(cnts)
img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.drawContours(frame,[hull],-1,(255,255,255),2)
##### Show final image ########
cv2.imshow('Dilation',frame)
###############################
#Print execution time
#print time.time()-start_time
#close the output video by pressing 'ESC'
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
Raw
hand1.py
import cv2
import numpy as np
import time
# Creating a window for HSV track bars
cv2.namedWindow('HSV_TrackBar')
# Starting with 100's to prevent error while masking
hl,sl,vl = 0,30,0
hu,su,vu = 26,116,255
def sethl(v):
global hl
hl = v
def setsl(v):
global sl
sl = v
def setvl(v):
global vl
vl = v
def sethu(v):
global hu
hu = v
def setsu(v):
global su
su = v
def setvu(v):
global vu
vu = v
# Creating track bar
cv2.createTrackbar('hl', 'HSV_TrackBar',hl,179,sethl)
cv2.createTrackbar('sl', 'HSV_TrackBar',sl,255,setsl)
cv2.createTrackbar('vl', 'HSV_TrackBar',vl,255,setvl)
cv2.createTrackbar('hu', 'HSV_TrackBar',hu,179,sethu)
cv2.createTrackbar('su', 'HSV_TrackBar',su,255,setsu)
cv2.createTrackbar('vu', 'HSV_TrackBar',vu,255,setvu)
#Open Camera object
cap = cv2.VideoCapture(0)
#Decrease frame size
#cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1000) # width of video
#cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 600) # height of video
#cap.set(cv2.CAP_PROP_SATURATION, 1)
def findSkin(frame):
lower = np.array([hl, sl, 0], dtype = "uint8")
upper = np.array([hu, su, 255], dtype = "uint8")
hsvFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(hsvFrame, lower, upper)
cv2.imshow('orig', frame)
# For tuning
#cv2.imshow('video', skinMask)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 1))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
cv2.imshow('video', skinMask)
return skinMask
while (cap.isOpened()):
ret, frame = cap.read()
skin = findSkin(frame)
k = cv2.waitKey(10) & 0xFF
if k == 27:
break
cap.release()
cv2.destroyAllWindows()
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