diff --git a/Gaze Detection/Operations.py b/Gaze Detection/Operations.py
new file mode 100644
index 0000000..0a8fa41
--- /dev/null
+++ b/Gaze Detection/Operations.py
@@ -0,0 +1,5 @@
+import face_forward
+import face_recognition
+import cv2
+import os
+
diff --git a/Gaze Detection/Suspects/suspect_2.jpg b/Gaze Detection/Suspects/suspect_2.jpg
new file mode 100644
index 0000000..d229c27
Binary files /dev/null and b/Gaze Detection/Suspects/suspect_2.jpg differ
diff --git a/Gaze Detection/Suspects/suspect_3.jpg b/Gaze Detection/Suspects/suspect_3.jpg
new file mode 100644
index 0000000..a97d748
Binary files /dev/null and b/Gaze Detection/Suspects/suspect_3.jpg differ
diff --git a/Gaze Detection/caught/suspect_1.jpg b/Gaze Detection/caught/suspect_1.jpg
index a170c28..ce441ac 100644
Binary files a/Gaze Detection/caught/suspect_1.jpg and b/Gaze Detection/caught/suspect_1.jpg differ
diff --git a/Gaze Detection/caught/suspect_10.jpg b/Gaze Detection/caught/suspect_10.jpg
new file mode 100644
index 0000000..aedd922
Binary files /dev/null and b/Gaze Detection/caught/suspect_10.jpg differ
diff --git a/Gaze Detection/caught/suspect_12.jpg b/Gaze Detection/caught/suspect_12.jpg
new file mode 100644
index 0000000..9746231
Binary files /dev/null and b/Gaze Detection/caught/suspect_12.jpg differ
diff --git a/Gaze Detection/caught/suspect_2.jpg b/Gaze Detection/caught/suspect_2.jpg
new file mode 100644
index 0000000..c14b6e1
Binary files /dev/null and b/Gaze Detection/caught/suspect_2.jpg differ
diff --git a/Gaze Detection/caught/suspect_6.jpg b/Gaze Detection/caught/suspect_6.jpg
new file mode 100644
index 0000000..a297adc
Binary files /dev/null and b/Gaze Detection/caught/suspect_6.jpg differ
diff --git a/Gaze Detection/clean_up.py b/Gaze Detection/clean_up.py
new file mode 100644
index 0000000..dcbcaa9
--- /dev/null
+++ b/Gaze Detection/clean_up.py
@@ -0,0 +1,51 @@
+import face_recognition
+import os
+
+
+# Removes duplicates of suspects
+def cleanUp():
+ dir = "caught/"
+ fileCount = len(os.listdir(dir))
+ print("num of images: " + str(fileCount))
+ if(os.path.exists(dir + "suspect_1.jpg")):
+ # Adds suspect_1 to the individual suspects array
+ suspect1 = face_recognition.load_image_file(dir + "suspect_1.jpg")
+ suspect1Encoding = face_recognition.face_encodings(suspect1)[0]
+ individual_suspects = [suspect1Encoding]
+ i=2
+ # Iterates from suspect_2 to the last suspect
+ for suspects in range(2,fileCount+1):
+ image,i = loadNextImage(i)
+ try:
+ imageEncoding = face_recognition.face_encodings(image)[0]
+ except IndexError:
+ print("there are no detected faces")
+ i+=1
+ continue
+ # results is a boolean array that compares the encoding with each individual suspect
+ results = face_recognition.compare_faces(individual_suspects, imageEncoding)
+ if(not True in results):
+ # suspect is a new individual and is added to the array
+ individual_suspects.append(imageEncoding)
+ else:
+ os.remove(dir + "suspect_" + str(i)+ ".jpg")
+ print("suspect_" + str(i) + " was removed")
+ i+=1
+ numIndividualSuspects = len(individual_suspects)
+ print("num of individual suspects: " + str(numIndividualSuspects))
+ else:
+ return
+
+# Gets the next valid image
+# i is the index of the next valid image
+def loadNextImage(j):
+ i=j
+ while os.path.exists(dir):
+ try:
+ image = face_recognition.load_image_file(dir + "suspect_" + str(i) +".jpg")
+ return image,i
+ except FileNotFoundError:
+ i+=1
+ continue
+
+cleanUp()
\ No newline at end of file
diff --git a/Gaze Detection/face_forward.py b/Gaze Detection/face_forward.py
index 2d6b57a..fa8946c 100644
--- a/Gaze Detection/face_forward.py
+++ b/Gaze Detection/face_forward.py
@@ -19,9 +19,19 @@ def main():
print(facial_features[0][0])
print(str(timer))
+def up_sample(landmark_list , sample_size=4):
+ for face_landmark in landmark_list:
+ if len(face_landmark) > 1:
+ for key in face_landmark.keys():
+ face_landmark[key] = [(w[0]*sample_size , w[1]*sample_size) for w in face_landmark[key]]
+ return landmark_list
+
def facial_coordinates(image):
- print ('starting isForward')
- face_landmarks_list = face_recognition.face_landmarks(image,model="small")
+ #print ('starting isForward')
+ small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
+ face_landmarks_list = face_recognition.face_landmarks(small_frame)
+ face_landmarks_list = up_sample(face_landmarks_list)
+
pil_image = Image.fromarray(image)
d = ImageDraw.Draw(pil_image)
@@ -30,9 +40,9 @@ def facial_coordinates(image):
# Let's trace out each facial feature in the image with a line!
for facial_feature in face_landmarks.keys():
- # if facial_feature == "chin" or facial_feature == "nose_tip":
- print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))
- return face_landmarks[facial_feature]
+ #if facial_feature == "nose_bridge" or facial_feature == "nose_tip":
+ #print("The {} in this face has the following points: {}".format(facial_feature, face_landmarks[facial_feature]))
+ return face_landmarks["nose_bridge"], face_landmarks["nose_tip"]
# return the picture
# return pil_image
@@ -49,9 +59,10 @@ def nose_inCircle(nose, center, radius):
def calculate_length(a, b):
#calculations to get length with points
- # (Xa, Ya) and (Xb, Yb)
- delta_x = math.abs(b[0] - a[0])
- delta_y = math.abs(b[1] - b[1])
+ # (Xa, Ya) and (Xb, Yb)
+ delta_x = abs(b[0] - a[0])
+ delta_y = abs(b[1] - a[1])
+
size = math.sqrt(delta_x**2 + delta_y**2)
return size
@@ -64,14 +75,10 @@ def display(image):
# image.show()
def transform(image, size):
- if size is "small":
- #Shrink image to a fourth of the size to process faster
- image = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
- else:
- #Shrink image to a fourth of the size to process faster
- image = cv2.resize(image, (0, 0), fx=2, fy=2)
+ #Shrink image to a fourth of the size to process faster
+ image = cv2.resize(image, (0, 0), fx=size, fy=size)
+
return image
if __name__ == '__main__':
main()
-
diff --git a/Gaze Detection/real_time.py b/Gaze Detection/real_time.py
index 811f60d..8c66f61 100644
--- a/Gaze Detection/real_time.py
+++ b/Gaze Detection/real_time.py
@@ -1,8 +1,10 @@
+from __future__ import division
import face_forward
import face_recognition
import cv2
+import sys
import os
-
+import math
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
@@ -30,8 +32,18 @@
suspicion_levels = []
process_this_frame = True
suspect_num = 0
-priority = "0"
+#priority = "0"#******
+priority = 0
solid = True
+initial = True
+
+# Checks if a face encoding exists in current known face encodings
+def checkDupe(face_encoding):
+ results = face_recognition.compare_faces(known_face_encodings, face_encoding)
+ if(not True in results):
+ return False
+ else:
+ return True
#We will only anylyze every 5 frames facial features
frame_count = 0
@@ -41,8 +53,18 @@
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
- small_frame = face_forward.transform(frame, 'small')
- frame = face_forward.transform(frame, 'big')
+ resize_small = .25
+ small_frame = face_forward.transform(frame,resize_small)
+
+ #Make frame big given arg
+ if initial:
+ print("STARTING AT DEFULT CAMERA SIZE 1\n -use W to increase and S to decrease-")
+ video_size = 1
+ frame = face_forward.transform(frame, video_size)
+
+ #Get transformation number
+ t = int(round(float(video_size)/resize_small))
+
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
@@ -52,9 +74,11 @@
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
+
face_names = []
for face_encoding in face_encodings:
+
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
@@ -68,7 +92,7 @@
#only inciment priority once every 10 frames
suspicion_levels[first_match_index] += 1
if suspicion_levels[first_match_index]%2 == 0:
- priority = int(suspicion_levels[first_match_index]/10)
+ #priority = int(suspicion_levels[first_match_index]/10)#************
if solid:
solid = False
else:
@@ -89,6 +113,23 @@
#add it to list of known faces
known_face_encodings.append(suspect_face_encoding)
known_face_names.append("SUSPECT_" + str(suspect_num))
+
+ if(len(face_recognition.face_encodings(suspect_image)) != 0):
+ suspect_face_encoding = face_recognition.face_encodings(suspect_image)[0]
+ # Removes frame from storage if encoding returns duplicate
+ if(checkDupe(suspect_face_encoding)):
+ print("captured frame is a duplicate. Removing...")
+ os.remove(storage + "/suspect_" + str(suspect_num) + ".jpg")
+ suspect_num -= 1
+ continue
+ #Initialize suspition level
+ suspicion_levels.append(0)
+ print("Suspect priorities: "+ str(suspicion_levels))
+ #add it to list of known faces
+ known_face_encodings.append(suspect_face_encoding)
+ known_face_names.append("SUSPECT_" + str(suspect_num))
+ print("suspect_" + str(suspect_num) + " detected")
+
face_names.append(name)
process_this_frame = not process_this_frame
@@ -100,12 +141,12 @@
# frame = face_forward.isLookingForward(frame)
# Blow the box to the size of video feed
- top *= 8
- right *= 8
- bottom *= 8
- left *= 8
+ top *= t
+ right *= t
+ bottom *= t
+ left *= t
- priority = int(priority)
+ #priority = int(priority)#*********
#Set color of bounding box
TextColor = (255, 255, 255)
if priority < 5:
@@ -130,12 +171,35 @@
TextColor = (0, 0, 0)
#Get pos of nose
- center = int((left+right)/2), int((top+(bottom - 70))/2)
+ center = int((left+right)/2), int((top+(bottom - 20))/2)
#Get radius
- radius = int(((bottom-70) - top)/5)
+ radius = int(((bottom - 20) - top)/5)
+ #Get nose point and bridge points
+ ff = face_forward.facial_coordinates(frame)
+ looking = True
+ #If we did find a face and facial features
+ if ff :
+ noseBridgePts = ff[0]
+ nosePointPts = ff[1]
+ #Check if all nose point tips are in the circle
+ for x in nosePointPts:
+ inCircle1 = face_forward.nose_inCircle(x, center, radius)
+ #cv2.line(frame,center,x, (255,255,255),1)
+ if inCircle1 == False:
+ looking = False
+ #Check if the low nose bridge point is in the circle
+ inCircle2 = face_forward.nose_inCircle(noseBridgePts[3], center, radius)
+ #cv2.line(frame,center,noseBridgePts[3], (255,255,255),1)
+ if inCircle2 == False:
+ looking = False
+ #print(looking)
+ if looking == True:
+ if priority<2:
+ add = 0.20
+ else:
+ add = add+.00001
+ priority = priority + add
- #nose[] = face_forward.getNose(frame)
-
# Draw a bounding box around the face
cv2.rectangle(frame, (left, top), (right, bottom), color, 2)
#cv2.circle(frame, left, 3, (0,0,255), thickness=1, lineType=8, shift=0)
@@ -145,7 +209,9 @@
cv2.circle(frame,center, radius, color)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 38), font, 1.2, TextColor, 1)
- cv2.putText(frame, "Priority: " + str(priority), (left + 6, bottom - 6), font, 0.8, TextColor, 1)
+ priorityStr = str(math.floor(priority))
+ #cv2.putText(frame, "Priority: " + str(priority), (left + 6, bottom - 6), font, 0.8, TextColor, 1)
+ cv2.putText(frame, "Priority: " + priorityStr, (left + 6, bottom - 6), font, 0.8, TextColor, 1)
# Display the resulting image
cv2.imshow('Live Feed', frame)
@@ -158,9 +224,27 @@
if cv2.waitKey(1) & 0xFF == ord('q'):
break
+ # Resize with numbers
+ initial = False
+ if cv2.waitKey(1) & 0xFF == ord('w'):
+ video_size += 1
+ print("Video_size is: "+ str(video_size))
+ if cv2.waitKey(1) & 0xFF == ord('s'):
+ if video_size > 1:
+ video_size -= 1
+ print("Video_size is: "+ str(video_size))
+ # elif cv2.waitKey(1) & 0xFF == ord('2'):
+ # video_size = 2
+ # elif cv2.waitKey(1) & 0xFF == ord('3'):
+ # video_size = 3
+ # elif cv2.waitKey(1) & 0xFF == ord('4'):
+ # video_size = 4
+
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
def drawCircle(image, center, radius):
cv2.circle(image, center, radius, color, thickness=1, lineType=8, shift=0)
+
+print("Program Closed")
diff --git a/Gaze Detection/real_time_makeup.py b/Gaze Detection/real_time_makeup.py
new file mode 100644
index 0000000..2a7dabc
--- /dev/null
+++ b/Gaze Detection/real_time_makeup.py
@@ -0,0 +1,92 @@
+import cv2
+import face_recognition
+from PIL import Image, ImageDraw
+import numpy as np
+import face_forward
+
+
+def PIL2array(img):
+ """ Convert a PIL/Pillow image to a numpy array """
+ return np.array(img.getdata(),
+ np.uint8).reshape(img.size[1], img.size[0], 3)
+
+def up_sample(landmark_list , sample_size=4):
+ for face_landmark in landmark_list:
+ if len(face_landmark) > 1:
+ for key in face_landmark.keys():
+ face_landmark[key] = [(w[0]*sample_size , w[1]*sample_size) for w in face_landmark[key]]
+ return landmark_list
+
+
+class FaceLandMarkDetection:
+
+ def predict(self , frame):
+ face_landmarks = face_recognition.face_landmarks(frame)
+ if down_sampling:
+ self.face_landmarks = up_sample(face_landmarks)
+ else:
+ self.face_landmarks = face_landmarks
+
+
+ def plot(self , frame):
+ pil_image = Image.fromarray(frame)
+ for face_landmarks in self.face_landmarks:
+ if len(face_landmarks) > 1:
+ d = ImageDraw.Draw(pil_image, 'RGBA')
+
+ # Make the eyebrows into a nightmare
+ d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))
+ d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))
+ d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=5)
+ d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=5)
+
+ # Gloss the lips
+ d.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128))
+ d.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128))
+ d.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=8)
+ d.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=8)
+
+ # Sparkle the eyes
+ d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
+ d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))
+
+ # Apply some eyeliner
+ d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=6)
+ d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=6)
+
+ return PIL2array(pil_image)
+
+video_capture = cv2.VideoCapture(0)
+video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
+video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
+
+
+face_landmark_detection = FaceLandMarkDetection()
+down_sampling = True
+while True:
+ # Grab a single frame of video
+ ret, frame = video_capture.read()
+
+ if down_sampling:
+ small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+
+ # Find all facial features in all the faces in the image
+ face_landmark_detection.predict(small_frame)
+ else:
+ # Find all facial features in all the faces in the image
+ face_landmark_detection.predict(frame)
+
+ ### plot
+ frame = face_landmark_detection.plot(frame)
+
+ frame = face_forward.transform(frame, "Big")
+ # Display the resulting image
+ cv2.imshow('Video', frame)
+
+ # Hit 'q' on the keyboard to quit!
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/Gaze Detection/server.html b/Gaze Detection/server.html
new file mode 100644
index 0000000..9a4d35a
--- /dev/null
+++ b/Gaze Detection/server.html
@@ -0,0 +1,11 @@
+
+
+
+
+ Gaze Detection Server
+
+
+ Most Recent Suspect
+ 
+
+
\ No newline at end of file
diff --git a/Gaze Detection/server.py b/Gaze Detection/server.py
new file mode 100644
index 0000000..47ea771
--- /dev/null
+++ b/Gaze Detection/server.py
@@ -0,0 +1,18 @@
+from http.server import HTTPServer, SimpleHTTPRequestHandler
+
+class Server(SimpleHTTPRequestHandler):
+
+ def do_get(self):
+ if self.path == '/':
+ self.path = '/index.html'
+ try:
+ file_to_open = open(self.path[1:]).read()
+ self.send_response(200)
+ except:
+ file_to_open = "File Not Found"
+ self.send_response(404)
+ self.end_headers()
+ self.wfile.write(bytes(file_to_open, 'utf-8'))
+
+httpd = HTTPServer(('localhost', 8080), Server)
+httpd.serve_forever()
\ No newline at end of file