def main():
'''
Arguments to be set:
showCam : determine if show the camera preview screen.
'''
print("Enter main() function")
if args.testImage is not None:
img = cv2.imread(args.testImage)
faceCoordinates = fd.face_detect(img)
face_img = fd.face_crop(img, faceCoordinates, face_shape=face_shape)
#cv2.imshow(windowsName, face_img)
cv2.imwrite('testing.png', face_img)
im = image.load_img('testing.png', target_size = (128, 128))
im = image.img_to_array(im)
im = np.expand_dims(im, axis = 0)
result = model.predict(im) #[0]
index = np.argmax(result)
print(emotions[index]) #, 'prob:', max(result))
sys.exit(0)
showCam = 1
capture = getCameraStream()
if showCam:
cv2.startWindowThread()
cv2.namedWindow(windowName, cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty(windowName, cv2.WND_PROP_FULLSCREEN, cv2.WND_PROP_FULLSCREEN)
display_and_classify(capture)
def main():
image = "testing-pictures/close_left.jpg"
image2 = "testing-pictures/close_right.jpg"
#far_left2 + far_right2: 0.368
#everything else: 0.127
camDist = 0.127
picLength = 600
picHeight = 1134
LRangle = 30.26
UDangle = 30.47
photoOneCoords = face_detection.face_detect(image)
photoTwoCoords = face_detection.face_detect(image2)
combinedCoords = []
print(photoOneCoords)
print(photoTwoCoords)
for i in range(len(photoOneCoords)):
combinedCoords.append(
calcDist.positionToCam(photoOneCoords[i], photoTwoCoords[i],
camDist, picLength, picHeight, LRangle,
UDangle))
print(
"Those are the 3D positions of the people in this picture wtih respect to the cameras:"
)
xyz = ["x", "y", "z"]
for i in range(0, len(combinedCoords)):
print("Person " + str(i + 1) + ": ")
for j in range(0, len(combinedCoords[i])):
print(xyz[j] + ": " + str(combinedCoords[i][j]))
distance = input("Enter a distance for social distancing(in meters): ")
distance = float(distance)
dict = {}
for i in range(len(photoOneCoords)):
dict[photoOneCoords[i]] = combinedCoords[i]
notDistanced = calcDist.checkAll(dict, distance)
if (len(notDistanced) != 0):
print("not Distanced:")
face_detection.draw_Rect(image)
else:
print("Distanced!")
def videoLoop(self):
try:
# keep looping over frames until we are instructed to stop
i = 0
while not self.stopEvent.is_set():
# grab the frame from the video stream and resize it to
# have a maximum width of 300 pixels
flag, self.frame = vs.read()
if i == 0:
i += 2
faceCoordinates = None
else:
try:
faceCoordinates = fd.face_detect(self.frame)
startX = faceCoordinates[0]
startY = faceCoordinates[1]
endX = faceCoordinates[2]
endY = faceCoordinates[3]
if startX is not None:
image = fd.draw_rect(self.frame, startX, startY,
endX, endY)
face_img = fd.face_crop(self.frame,
faceCoordinates,
face_shape=(128, 128))
im = img_to_array(face_img)
im = np.expand_dims(im, axis=0)
result = self.model.predict(im)
for i in range(0, 7):
self.resultLabel[i].config(
text=str(round(result[0][i], 4)))
except:
pass
self.frame = imutils.resize(self.frame, width=300)
self.frame = cv2.flip(self.frame, 1)
# OpenCV represents images in BGR order; however PIL
# represents images in RGB order, so we need to swap
# the channels, then convert to PIL and ImageTk format
image = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = ImageTk.PhotoImage(image)
# if the panel is not None, we need to initialize it
if self.panelA is None:
self.panelA = tki.Label(image=image)
self.panelA.image = image
self.panelA.pack(side="left", padx=10, pady=10)
self.panelA.place(x=20, y=20)
# otherwise, simply update the panel
else:
self.panelA.configure(image=image)
self.panelA.image = image
except RuntimeError:
print("[INFO] caught a RuntimeError")
def call_processing():
try :
result_c_img = face_detection.face_detect()
except:
print('Error')
else:
print("finish (no error)")
def __init__(self):
super(Webcam, self).__init__()
self._stop_event = threading.Event()
self.is_running = True
self.model = load_model('save_model1.h5')
self.FACE = face_detect("./file/haarcascade_frontalface_alt.xml") # haarcascade_frontalface_alt path
self.face_expression = ['angry', 'disgust', 'scary', 'happy', 'neutral', 'sad', 'surprise']
self.face_expression_cnt = defaultdict(int)
self.mood = 'None'
def display_and_classify(capture):
i=1
while (True):
flag, frame = capture.read()
"""
cv2.imshow(windowName+"s", frame)
if cv2.waitKey(1) == 27:
break
"""
#faceCoordinates = None
if i==1:
index = 4
i -= 1
faceCoordinates=None
try:
faceCoordinates = fd.face_detect(frame)
startX = faceCoordinates[0]
startY = faceCoordinates[1]
endX = faceCoordinates[2]
endY = faceCoordinates[3]
#faceCoordinates = dict['faceCoordinates']
#text = dict['text']
refreshFrame(frame, startX, startY, endX, endY, index)
except:
refreshFrame(frame, None, None, None, None, 7)
if faceCoordinates is not None:
face_img = fd.face_crop(frame, faceCoordinates, face_shape=face_shape)
#cv2.imshow(windowsName, face_img)
cv2.imwrite('testing.png', face_img)
im = image.load_img('testing.png', target_size = (128, 128))
im = image.img_to_array(im)
im = np.expand_dims(im, axis = 0)
result = model.predict(im) #[0]
index = np.argmax(result)
print(emotions[index]) #, 'prob:', max(result))
print("")
#text = "{:.2f}%".format(emotions[index] * 100)
# print(face_img.shape)
# emotion = class_label[result_index]
# print(emotion)
if cv2.waitKey(1) == 27:
break # esc to quit
break
import cv2
import csv
import numpy as np
from collections import defaultdict
from keras.models import load_model
#############################################
from face_detection import face_detect
from music_player import Music_Player
from weather import Weather_Api
#############################################
model = load_model('./file/save_model1.h5')
FACE = face_detect("./file/haarcascade_frontalface_alt.xml") # haarcascade_frontalface_alt 파일 경로
WTH = Weather_Api()
face_expression = ['화남', '역겨움', '무서움', '행복', '무표정', '우울', '놀람']
face_expression_cnt = defaultdict(int)
cap = cv2.VideoCapture("./file/video.mp4") # 비디오 경로
while True:
success, img = cap.read()
if not success:
break
faces, flag = FACE.detect_face(img)
for person in faces:
# cv2.imshow('person', person)
person = np.asarray(person)
import cv2
import numpy as np
from vid2photos import vid2photos
from face_detection import face_detect
#from pixel2degree import pixel2degree
if __name__ == "__main__":
frameCount = vid2photos()
#initialing list for the centroids
centroidPerFrame = [0] * (frameCount - 1)
for i in range(1, frameCount - 1):
centroidPerFrame[i] = face_detect("image" + str(i) + ".jpg")
newlist = centroidPerFrame.remove(0)
print(newlist)
cleanList = [x for x in centroidPerFrame if x != [[], []]]
print(cleanList)
#degree = pixel2degree (cleanList, "image1.jpg")
#print degree
# use the valueo of centroidPerFrame and parse it (need some parser fuction)
# then use the parsed value rearrange it prettily and convert it it to some
# angle given the distance (CONST) (need a function for this too)
# then
import sys
sys.path.append('site-packagesのパス')
from skimage import data, io, filters, color, img_as_ubyte
from sklearn import preprocessing
import numpy as np
import matplotlib.pyplot as plt
import scipy
import Cython
# C++で作成した共有ファイルの.soをインポート
import face_detection
input_path = '/media/docker_shared/mono_develop/img/getImg.jpg'
output_path = '/media/docker_shared/mono_develop/img/kansei.jpg'
# /Users/ruimac/opt/anaconda3/bin/python
try:
result_c_img = face_detection.face_detect()
except:
print('Error')
else:
print("finish (no error)")
def result():
if request.method == 'POST':
result = request.form
img = face_detect(result['url'])
img.save("templates/img.jpg")
return render_template("result.html", result=result)