# -*- coding:utf-8 -*-
import os
import cv2
#import subprocess
#import serial
import time
from boss_train import Model
#from image_show import show_image
if __name__ == '__main__':
cap = cv2.VideoCapture(0)
#cap = cv2.VideoCapture('rtsp://192.168.43.209:554')
cascade_path = "haarcascade_frontalface_alt.xml"
model = Model()
model.load()
while True:
_, frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cascade = cv2.CascadeClassifier(cascade_path)
# facerect = cascade.detectMultiScale(frame_gray,1.03,1)
facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.4, minNeighbors=10, minSize=(10,10))
# facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.01, minNeighbors=3, minSize=(3, 3))
for (x,y,w,h) in facerect:
cv2.rectangle(frame,(x,y),(x+w,y+h),(127,255,0),1)
font = cv2.FONT_HERSHEY_TRIPLEX
cv2.putText(frame,"Searching .....",(10,100),font,1,(255,255,0),1,False)
# cv2.imshow('Opencv',frame)
# -*- coding:utf-8 -*-
import cv2
from boss_train import Model
from image_show import show_image
if __name__ == '__main__':
cap = cv2.VideoCapture(0)
#cascade_path = "/usr/local/opt/opencv/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml"
cascade_path = "/opt/anaconda3/envs/venv/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml"
model = Model()
model.load()
print("Face Detect Start!")
while True:
_, frame = cap.read()
# グレースケール変換 // 그레이스케일 변환
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# カスケード分類器の特徴量を取得する // 캐스케이드 분류기의 특징량을 취득한다.
cascade = cv2.CascadeClassifier(cascade_path)
# 物体認識（顔認識）の実行 // 물체인식 (얼굴인식) 의 실행
facerect = cascade.detectMultiScale(frame_gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
#facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.01, minNeighbors=3, minSize=(3, 3))
from __future__ import print_function
import cv2
import dlib
from boss_train import Model
from whois import who
if __name__ == '__main__':
# dlib 特徵選取器
detector = dlib.get_frontal_face_detector()
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture("rtsp://*****:*****@192.168.1.127/h264.out")
# cap = cv2.VideoCapture("d:/video/ooo.avi")
# cap = cv2.VideoCapture("d:/video/www.mp4")
# 訓練集載入
model = Model()
model.load()
while True:
ret,frame = cap.read()
if ret == True:
color = (0,255,255)
# 灰階
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 使用detector進行人臉辨識
dets = detector(frame_gray, 1)
for _, d in enumerate(dets):
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
# if os.path.exists(save_dir):
# shutil.rmtree(save_dir)
# os.mkdir(save_dir)
count = 0
for (x1, y1, x2, y2) in faces:
file_name = os.path.join(
save_dir,
str(count) + image_name.split(".")[0].split("/")[-1] + ".jpg")
Image.open(image_name).crop((x1, y1, x2, y2)).save(file_name)
count += 1
if __name__ == '__main__':
cap = cv2.VideoCapture(0)
cascade_path = "/usr/local/opt/opencv/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml"
model = Model()
model.load()
while True:
_, frame = cap.read()
pic_name = "pic.jpg"
plt.imsave(pic_name, frame)
faces = detectFaces(pic_name)
if faces:
rs = 1
for (x1, y1, x2, y2) in faces:
face_name = "face.jpg"
Image.open(pic_name).crop((x1, y1, x2, y2)).save(face_name)
image = cv2.imread(face_name)
rs = model.predict(image)
if rs == 0:
# -*- coding:utf-8 -*-
import cv2
from boss_train import Model
from image_show import show_image
if __name__ == '__main__':
model = Model()
model.load()
frame = cv2.imread("./dog.jpg", 3)
#cv2.imshow(frame)
result = model.predict(frame)
if result == 0: # boss
print('Boss is approaching')
#show_image()
else:
print('Not boss')