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watcher.py
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watcher.py
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# import the necessary packages
from typing import TYPE_CHECKING
import numpy as np
import cv2
import math
import random
# initialize the HOG descriptor/person detector
def in_circle(center_x, center_y, radius, x, y):
dist = math.sqrt((center_x - x) ** 2 + (center_y - y) ** 2)
return dist <= radius
def gate(value, minimum, maximum):
return max(min(value,maximum), minimum)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
cv2.startWindowThread()
# open webcam video stream
cap = cv2.VideoCapture(0)
# the output will be written to output.avi
output_width = 1650
output_height = 1280
scale = int(output_width/640)
speed = int(.0125*output_width)/2
left_center = (int(output_width/5),int(output_height/2))
right_center = (int(output_width/5*4),int(output_height/2))
left_pupil_location = left_center
right_pupil_location = right_center
target = left_center
directed = False
attention = 20
iterations = 0
iris_size = 207*scale
oldframe = None
out = cv2.VideoWriter(
'output.avi',
cv2.VideoWriter_fourcc(*'MJPG'),
15.,
(output_width,output_height))
while(True):
iterations +=1
if iterations > attention:
print("Bored, looking elsewhere")
directed = False
iterations = 0
# Capture frame-by-frame
ret, frame = cap.read()
# resizing for faster detection
frame = cv2.resize(frame, (640,480))
frame = cv2.flip(frame,1)
# using a greyscale picture, also for faster detection
gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
# detect people in the image
# returns the bounding boxes for the detected objects
#boxes = face_cascade.detectMultiScale(gray, 1.1, 4)
boxes, weights = hog.detectMultiScale(gray, winStride=(8,8) )
faceBoxes = []
if oldframe is not None:
oldframe = cv2.GaussianBlur(oldframe, (21,21),0)
newframe = cv2.GaussianBlur(gray, (21,21),0)
subtraction = cv2.absdiff(oldframe, newframe)
threshold = cv2.threshold(subtraction, 25,255, cv2.THRESH_BINARY)[1]
threshold = cv2.dilate(threshold, None, iterations =2)
contouring = threshold.copy()
outlines, hierarchy = cv2.findContours(contouring, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
a =0
for c in outlines:
if cv2.contourArea(c) > a:
a = cv2.contourArea(c)
x,y,w,h = cv2.boundingRect(c)
faceBoxes = [(int(x*scale+w*scale/2),int(y*scale))]
oldframe = gray
frame = cv2.resize(frame, (output_width,output_height))
#boxes = np.array([[x+int(w/2), y+ h, x + w, y + h] for (x, y, w, h) in boxes])
#faceBoxes = np.array([[x*scale, y*scale] for (x, y, w, h) in boxes])
currX, currY = left_pupil_location
if (len(faceBoxes)>=1):
speed = int(.0125*output_width)
target = faceBoxes[0]
directed = True
print(target)
print("Spotted you!")
iterations = 0
iris_size +=int(6*scale)
else:
iris_size -=int(3*scale)
iris_size = gate(iris_size, 21*scale ,int(.0546*output_width))
if not(directed):
#print("Looking at new target")
speed = int(4*scale)
target = (currX + random.randint(-speed*200, speed*200), currY + random.randint(-speed*300, speed*200))
#print(target)
directed = True
iterations = 0
x,y =target
# display the detected boxes in the colour picture
#cv2.rectangle(frame, (xA, yA), (xB, yB),
# (0, 255, 0), 2)
if (x > currX): newX = currX+speed
else: newX = currX-speed
if (y > currY): newY = currY+speed
else: newY = currY-speed
newY = max(min(newY,int(output_height/2+50)),int(.28125*output_height))
if (in_circle(int(output_width/5),int(output_height/2),int(.14*output_width),newX, newY)):
left_pupil_location = (newX,newY)
right_pupil_location = tuple(np.add(right_pupil_location , (newX-currX, newY-currY)))
# print("...")
#else:
#directed = False
arc = np.array([[1,1],[newX, newY],[output_width,output_height]], np.int32)
arc = arc.reshape((-1, 1, 2))
cv2.rectangle(frame,(0,0), (output_width,output_height), (0,0,0,10), thickness = -1)
cv2.circle(frame,left_center,207*scale,(150,150,255, 255),thickness=-1) #pink
cv2.circle(frame,left_center,int(.2109*output_width),(225,225,255, 255),thickness=-1) #white
cv2.circle(frame,right_center,207*scale,(150,150,255, 255),thickness=-1) #pink
cv2.circle(frame,right_center,int(.2109*output_width),(225,225,255, 255),thickness=-1) #white
if (newY < int(output_height/2)):
cv2.ellipse(frame, (int(output_width/2),(int(.833*output_height))), (int(.36*output_width), int(.833*output_height)-newY),
0, 200, 340, (150,150,255, 255), thickness = 1)
else:
cv2.ellipse(frame, (int(output_width/2),int(.166*output_height)), (int(.36*output_width), newY-int(.166*output_height)),
0, 35, 155, (150,150,255, 255), thickness = 1)
cv2.circle(frame,left_pupil_location,int(.078125*output_width),(3,39,148,150),thickness=-1) #Iris
cv2.circle(frame,left_pupil_location,iris_size,1,thickness=-1) #pupil
#eyelid
cv2.circle(frame,right_pupil_location,int(.078125*output_width),(3,39,148,150),thickness=-1) #Iris
cv2.circle(frame,right_pupil_location,iris_size,1,thickness=-1)
cv2.ellipse(frame, left_center, (int(.38*output_width), int(.242*output_width)),
19, 0, 360, 1, thickness = int(.15*output_width)) #pupil
cv2.ellipse(frame, right_center, (int(.38*output_width), int(.242*output_width)),
-19, 0, 360, 1, thickness = int(.15*output_width)) #eyelid
# Write the output video
out.write(frame.astype('uint8'))
# Display the resulting frame
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
# and release the output
out.release()
# finally, close the window
cv2.destroyAllWindows()
cv2.waitKey(1)