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Commit 2b799a4c authored by klorydryk's avatar klorydryk
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replace average rectangle by gradient, and use french logo as cursor

parent c0aa4f99
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logo.png 0 → 100644
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logo.png

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trackmultiplefaces.py
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......@@ -6,7 +6,7 @@
#Import the OpenCV and dlib libraries
import cv2
import dlib
from colour import Color
import threading
import time
......@@ -29,9 +29,16 @@ WHITE =(255, 255, 255)
RED = (0, 0, 255)
BLUE = (255, 0, 0)
PURPLE = (127, 0, 255)
ORANGE = (0,165,255)
GREEN = (0, 255, 0)
suspiciousColors = {0:(0,0,0), 1:GREEN, 2:PURPLE, 3:RED}
suspiciousColors = {0:(0,0,0), 1:GREEN, 2:ORANGE, 3:RED}
avSusRectWidth = 50
cursorSize = (50, 20)
cursor = cv2.imread('logo.png') # Read the file
cursor = cv2.resize(cursor, cursorSize)
averageSuspicious = 0
......@@ -43,13 +50,34 @@ rectangleColor = (0,165,255)
rectangleColorSuspicious = RED
# Suspicious threshold
SUSPICIOUS_THRESHOLD = 2
SUSPICIOUS_THRESHOLD = 2
#We are not doing really face recognition
def doRecognizePerson(faceNames, fid):
time.sleep(2)
faceNames[ fid ] = randomnames.rand_name(charset) #"Person " + str(fid)
def displayGradientOn(resultImage, value):
red = Color("red")
orange = Color("orange")
colors = list(red.range_to(orange,10))
colors += list(orange.range_to(Color("green"),10))
width = resultImage.shape[1]
pixelColorSize = 5
i = 0
for color in colors:
h = color.hex.lstrip('#')
if(len(h) == 6):
cv2.rectangle(resultImage, (int(width-avSusRectWidth), i*pixelColorSize), (int(width), (i+1)*pixelColorSize), tuple(int(h[j:j+2], 16) for j in (4, 2, 0)), -1)
else:
cv2.rectangle(resultImage, (int(width-avSusRectWidth), i*pixelColorSize), (int(width), (i+1)*pixelColorSize), tuple(int(h[j:j+2], 16) for j in (2, 1, 0)), -1)
i += 1
x_offset = width-avSusRectWidth
y_offset = int((i)*pixelColorSize * (2-(value-1))/2)
resultImage[y_offset:y_offset+cursor.shape[0], x_offset:x_offset+cursor.shape[1]] = cursor
def calculateAverageSuspicious(faceSuspicion):
global averageSuspicious
......@@ -58,8 +86,8 @@ def calculateAverageSuspicious(faceSuspicion):
averageSuspicious = sum(faceSuspicion)/currentFaces
print(f"Average suspicion: {averageSuspicious}")
def detectAndTrackMultipleFaces(start_thread,
charset,
def detectAndTrackMultipleFaces(start_thread,
charset,
draw_person_dangerosity,
add_warning_message):
......@@ -111,12 +139,12 @@ def detectAndTrackMultipleFaces(start_thread,
#overlayed rectangle for the largest face
if FULLSCREEN == True:
resultImage = fullSizeBaseImage.copy()
cv2.setWindowProperty('result-image',
cv2.setWindowProperty('result-image',
cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_FULLSCREEN)
else:
resultImage = baseImage.copy()
cv2.setWindowProperty('result-image',
cv2.setWindowProperty('result-image',
cv2.WND_PROP_FULLSCREEN,
cv2.WINDOW_NORMAL)
......@@ -229,20 +257,20 @@ def detectAndTrackMultipleFaces(start_thread,
faceTrackers[ currentFaceID ] = tracker
# Pick up random name: Do we want a thread for that?
# Pick up random name: Do we want a thread for that?
# Advantage is that we can pop an "Identifying..." Message
# Disadvantage is that is more "lourd"
if start_thread :
#Start a new thread that is used to simulate face recognition.
#Start a new thread that is used to simulate face recognition.
t = threading.Thread( target = doRecognizePerson ,
args=(faceNames, currentFaceID))
t.start()
else:
# Directly add name + pass a charset to which name must belong
# Directly add name + pass a charset to which name must belong
# "Person " + str(fid)
faceNames[ currentFaceID ] = randomnames.rand_name(charset)
faceNames[ currentFaceID ] = randomnames.rand_name(charset)
calculateAverageSuspicious(faceSuspicion)
......@@ -257,11 +285,11 @@ def detectAndTrackMultipleFaces(start_thread,
for fid in faceTrackers.keys():
# Draw a rectangle around people's face
rectangle_around_face(fid,
faceTrackers,
rectangle_around_face(fid,
faceTrackers,
faceSuspicion,
# If False, colors will not change with suspicion level
draw_person_dangerosity,
draw_person_dangerosity,
resultImage)
# Deal with the two others rectangles
......@@ -272,52 +300,58 @@ def detectAndTrackMultipleFaces(start_thread,
t_w = int(tracked_position.width())
t_h = int(tracked_position.height())
cv2.rectangle(resultImage,
(t_x+t_w, t_y),
(t_x+t_w+30, t_y+80),
suspiciousColors[faceSuspicion[fid]],
-1)
# cv2.rectangle(resultImage,
# (t_x+t_w, t_y),
# (t_x+t_w+30, t_y+80),
# suspiciousColors[faceSuspicion[fid]],
# -1)
if averageSuspicious>2.5:
color = RED
elif averageSuspicious>1.5:
color = PURPLE
else:
color = GREEN
# if averageSuspicious>2.5:
# color = RED
# elif averageSuspicious>1.5:
# color = ORANGE
# else:
# color = GREEN
width = resultImage.shape[1]
cv2.rectangle(resultImage, (int(width-50), 0), (int(width), 120), color, -1)
# If Known -> print Name
# width = resultImage.shape[1]
# cv2.rectangle(resultImage, (int(width-50), 0), (int(width), 120), color, -1)
displayGradientOn(resultImage, averageSuspicious) # TODO optimization ?
# x_offset = 50#width-avSusRectWidth
# y_offset = 100
# resultImage[y_offset:y_offset+cursor.shape[0], x_offset:x_offset+cursor.shape[1]] = cursor
# If Known -> print Name
if fid in faceNames.keys():
cv2.putText(resultImage,
cv2.putText(resultImage,
faceNames[fid] ,
# Name's alignment
(t_x+ int((t_w - len(faceNames[fid])*10)/2 + 10), int(t_y) - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
NAME_COLOR,
1,
0.5,
NAME_COLOR,
1,
cv2.LINE_AA)
# Add a warning message depending on suspicious level
if add_warning_message:
put_warning_message(fid,
faceTrackers,
faceSuspicion,
put_warning_message(fid,
faceTrackers,
faceSuspicion,
resultImage)
# Else print Identifying
else:
text = "Identifying"
cv2.putText(resultImage,
cv2.putText(resultImage,
text ,
(t_x + int((t_w - len(text)*10)/2 + 10), int(t_y)),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
IDENTIFYING_COLOR,
1,
0.5,
IDENTIFYING_COLOR,
1,
cv2.LINE_AA)
#Since we want to show something larger on the screen than the
......@@ -353,19 +387,19 @@ def put_warning_message(fid, faceTrackers, faceSuspicion, resultImage):
t_y = int(tracked_position.top())
t_w = int(tracked_position.width())
t_h = int(tracked_position.height())
suspicion_level = faceSuspicion[fid]
if suspicion_level > SUSPICIOUS_THRESHOLD:
text = "ATTENTION! PERSONNE DANGEREUSE!"
cv2.putText(resultImage,
cv2.putText(resultImage,
text ,
# Name's alignment
(t_x+ int((t_w - len(text)*10)/2 + 10), int(t_y) + 20 + t_h),
cv2.FONT_HERSHEY_SIMPLEX,
0.5,
RED,
1,
0.5,
RED,
1,
cv2.LINE_AA)
......@@ -381,17 +415,17 @@ def rectangle_around_face(fid, faceTrackers, faceSuspicion, draw_person_dangeros
t_w = int(tracked_position.width())
t_h = int(tracked_position.height())
# If we want to have a dangerosity color per personn
# If we want to have a dangerosity color per personn
if draw_person_dangerosity :
suspicionLevel = faceSuspicion[fid]
person_color = pick_person_dangerosity_color(suspicionLevel)
person_color = suspiciousColors[suspicionLevel] #pick_person_dangerosity_color(suspicionLevel)
# Else default to rectangleColor
else:
person_color = rectangleColor
# Draw Rectangle
cv2.rectangle(resultImage,
cv2.rectangle(resultImage,
(t_x, t_y),
(t_x + t_w , t_y + t_h),
person_color,
......@@ -412,7 +446,7 @@ def pick_person_dangerosity_color(suspicionLevel):
if __name__ == '__main__':
detectAndTrackMultipleFaces(
# False: Names are directly printed on screen, no new thread
start_thread = False,
start_thread = False,
# Select only names with letters and whitespace. Any other value will select printable.
charset = "letters",
# If True, then rectangle color around people' face will turn red if dangerous
......
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