def kamera_ip(self):
width = int(self.cam.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)
height = int(self.cam.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('../recording/' + str(self.waktu)+'.avi', fourcc, 20.0, (width, height))
while True:
#time.sleep(0.1)
imgResp = urllib.urlopen(self.url)
imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)
img = cv2.imdecode(imgNp,-1)
while(self.cam.isOpened()):
if self.grabbed == True:
out.write(img)
#cv2.imshow('IPWebcam',img)
if (cv2.waitKey(1) & 0xFF) == ord('q'): # Hit `q` to exit
break
else:
break
print(img)
out.release()
self.cam.release()
cv2.destroyAllwindows()
def draw_geometric_shapes_example():
""" draw_geometric_shapes_example function """
img = np.zeros([1914, 1080, 3],
np.uint8) # create img with black bg (1914 x 1080)
# draw line to img from (0, 0) to (255, 255) with red color (0, 0, 255) (B G R) and 5 thickness
img = cv2.line(img, (0, 0), (255, 255), (0, 0, 255), 5)
# draw arrow line to img from (0, 200) to (255, 500) with blue color (255, 0, 0) (B G R) and 5 thickness
img = cv2.arrowedLine(img, (0, 200), (255, 500), (255, 0, 0), 5)
# draw rectagle to img with green color (255, 0, 0) (B G R) and 5 thickness
# x1,y1 -------
# | |
# | |
# | |
# ------- x2,y2
img = cv2.rectangle(img, (600, 100), (900, 400), (0, 255, 0), 5)
# draw circle to img at (1000, 40) 50 radius with red color (0, 0, 255) (B G R) and 5 thickness
img = cv2.circle(img, (1000, 400), 50, (0, 0, 255), 5)
# put text msg "Hello world!!" to img at (70, 750) hershey font 5 font size with text color white (255, 255, 255)
# and thickness 5 with line type AA
img = cv2.putText(img, "Hello world!!", (70, 750),
cv2.FONT_HERSHEY_SIMPLEX, 5, (255, 255, 255), 5,
cv2.LINE_AA)
cv2.imshow("Image", img) # display img
cv2.waitKey(0)
cv2.destroyAllwindows()
def prepareproposal(path):
img = cv.imread(path)
imgmultiproposals = img.copy()
proposals = []
init_react1 = [10,10,200,300]
# 4 个
proposals = genreateproposal(init_react1,proposals,4)
init_react2 = [100,120,300,500]
proposals = genreateproposal(init_react2,proposals,5)
proposals = np.array(proposals)
for rec in proposals:
cv.rectangle(imgmultiproposals,(rec[0],rec[1]), (rec[2],rec[3]),(0,0,255))
cv.imshow("before",imgmultiproposals)
keepindex = py_nms(proposals,0.5)
imgnms = img.copy()
for i in keepindex:
rec = proposals[i]
cv.rectangle(imgnms,(rec[0],rec[1]), (rec[2],rec[3]),(0,0,255))
cv.imshow("after",imgnms)
cv.waitKey(0)
cv.destroyAllwindows()
def gen(video):
while True:
tmpfile_list = os.listdir(input_dir)
infile_list = [file for file in tmpfile_list if file.endswith(".jpg")]
# print(infile_list)
if (len(infile_list) == 0):
sleep(2)
for i in range(0, len(infile_list)):
infilename = input_dir + '/' + infile_list[i]
# print(infilename)
image = cv.imread(infilename, 1)
ret, jpeg = cv.imencode('.jpg', image)
frame = jpeg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
sleep(1)
shutil.move(infilename, saved_dir + '/' + infile_list[i])
# shutil.copy(infilename,saved_dir+'/'+infile_list[i])
sleep(1)
tmpfile_list = ""
# cv.waitKey(1000)
cv.destroyAllwindows()
def readVideo(skipFrame): # pathName为视频文件路径，skipFrame为视频的第skipFrame帧
selectXY = [(0, 0), (0, 0)]
camera = cv2.VideoCapture(0) # 读取摄像头
if not camera.isOpened(): # 如果为发现摄像头，则按照路径pathName读取视频文件
camera = cv2.VideoCapture(
selectDir()) # 读取视频文件，如pathName='D:/test/test.mp4'
c = 1
while (camera.isOpened()):
ret, frame = camera.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.rectangle(frame, selectXY[0], selectXY[1], (0, 255, 0), 2)
if (c >= skipFrame):
mask = np.zeros(
gray.shape,
dtype=np.uint8) # 掩码操作，该矩阵与图片大小类型一致，为初始化全0像素值，之后对其操作区域赋值为1即可
if (c == skipFrame):
selectXY = get_rect(frame, title='get_rect') # 鼠标画矩形框
img01, img02 = frame, frame
gray01, gray02 = gray, gray
else:
img1, img2 = prev_frame, frame
gray1, gray2 = prev_frame, frame
cv2.imshow('frame', frame)
c = c + 1
prev_gray = gray
prev_frame = frame
if cv2.waitKey(1) & 0xFF == ord('q'): # 点击视频窗口，按q键退出
break
camera.release()
cv2.destroyAllwindows()
def record():
cap = cv2.VideoCapture(0)
date = time.gmtime(time.time())
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter(
str(date.tm_year) + str(date.tm_mon) + str(date.tm_mday) + '.avi',
fourcc, 25.0, (640, 480))
while (cap.isOpened()):
ret, frame = cap.read()
if ret:
out.write(frame)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllwindows()
def main(args):
ic = Image_checker()
rospy.init_node('node_estimate_checker.py', anonymous=True)
print("node_estimate_checker")
try:
rospy.spin()
except KeyboardInterrupt:
print("Shut down")
cv2.destroyAllwindows()
def tracking():
cap = cv2.VideoCapture(0)
cap.set(3, 320)
cap.set(4, 240)
while True:
ret, frame = cap.read()
cv2.imshow('video', frame)
if cv2.waitKey(1) & 0xff == ord('q'):
break
cv2.destroyAllwindows()
def img_move():
img = cv2.imread('../images/joy.jpeg')
h, w, c = img.shape[0:3]
print(h, w, c)
M = np.float32([[1, 0, 100], [0, 1, 50]])
print(M.shape)
img2 = cv2.warpAffine(img, M, (w, h))
cv2.imshow('joy ^^', img)
cv2.imshow('moved_joy ^^', img2)
cv2.waitKey(0)
cv2.destroyAllwindows()
def access_camera(cam_id):
"""
cam_id : camera ID obtained from user.
"""
# Invoke VideoCapture method by passing camera ID arguement from user
cap = cv2.VideoCapture(cam_id)
# Check to see if the camera opens up and if it doesn't raise appropriate error
if not cap.isOpened():
raise IOError(
"Cannot access camera, please check if you entered the ID correctly"
)
# While camera is opened
while True:
# Read the camera input
"""
ret : return True if the frame is available from the camera reading operation below.
frame : array of frames as it loops.
"""
start_time = time.time()
ret, frame = cap.read()
# Resizing window to save resources and have defined window
cv2.resize(frame, (640, 480))
# FPS = 1 / time to process loop
fps = 1.0 / (time.time() - start_time)
print("FPS: ", fps)
# fps = cv2.VideoCapture.get(CV_CAP_PROP_FPS)
# print(fps)
cv2.putText(frame, "FPS: {}".format(fps), (100, 100),
cv2.FONT_HERSHEY_SIMPLEX, 2, 255)
# Display the result
cv2.imshow("output", frame)
# Check to see if escape key was pressed every defined time interval and break if pressed
check = cv2.waitKey(1)
if check == 27:
break
# Release the camera resource
cap.release()
# Destroy all windows
cv2.destroyAllwindows()
def camShift():
global frame, frame2, inputmode, trackWindow, roi_hist, out
try:
cap = cv2.VideoCapture(1)
cap.set(3, 480)
cap.set(4, 320)
except:
print('카메라 구동 실패')
return
ret, frame = cap.read()
cv2.nameWindow('frame')
cv2.setMouseCallback('frame', onMouse, param=(frame, frame2))
termination = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
while True:
ret, frame = cap.read()
if not ret:
break
if trackWindow is not None:
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
ret, trackWindow = cv2.Camshift(dst, trackWindow, termination)
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
cv2.polylines(frame, [pts], True, (0, 255, 0), 2)
cv2.imshow('frame', frame)
k = cv2.waitKey(60) & 0xFF
if k == 27:
break
if k == ord('i'):
print('추적할 영역을 지정하고 아무키나 누르세요')
inputmode = True
frame2 = frame.copy()
while inputmode:
cv2.imshow('frame', frame)
cv2.waitKey(0)
cap.release()
cv2.destroyAllwindows()
def transform():
img = cv2.imread('../images/joy.jpeg')
h, w = img.shape[0:2]
print(h, w)
# 리사이징할 이미지 원본, dsize를 나타내는 튜플, 배율인자 x와y, 리사이징할 방법
img2 = cv2.resize(img, None, fx=0.5, fy=1, interpolation=cv2.INTER_AREA)
img3 = cv2.resize(img, None, fx=1, fy=0.5, interpolation=cv2.INTER_AREA)
img4 = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)
cv2.imshow('joy', img)
cv2.imshow('joy2', img2)
cv2.imshow('joy3', img3)
cv2.imshow('joy4', img4)
cv2.waitKey(0)
cv2.destroyAllwindows()
def cam():
global imgencode
while True:
ret1, img1 = cam1.read()
if img1 != []:
img1 = cv2.resize(img1,(320,240))
result, imgencode = cv2.imencode('.jpg',img1)
print imgencode
if cv2.waitKey(10) == 27:
break
cv2.destroyAllwindows()
def main():
global img1, img2
img1 = cv2.imread('C:/Users/HP NOTE/Desktop/test/1.jpg')
img2 = img1.copy()
cv2.namedWindow('original'), cv2.namedWindow('backproj')
cv2.setMouseCallback('original', onMouse, param=None)
cv2.imshow('backproj', img2)
while True:
cv2.imshow('original', img1)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
cv2.destroyAllwindows()
def tracking():
try:
print('카메라를 구동합니다')
cap = cv2.VideoCapture('../videos/sekyung.mp4')
except:
print('카메라 구동 실패')
return
while True:
ret, frame = cap.read()
#frame = cv2.imread('../images/traffic_light3.png')
# BGR -> HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array([90, 100, 100])
upper_blue = np.array([130, 255, 255])
lower_green = np.array([30, 100, 100])
upper_green = np.array([70, 255, 255])
lower_red = np.array([-10, 100, 100])
upper_red = np.array([10, 255, 255])
# HSV 이미지에서 청색, 초록색, 빨간색 추출하기 위한 임계값
mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
mask_green = cv2.inRange(hsv, lower_green, upper_green)
mask_red = cv2.inRange(hsv, lower_red, upper_red)
# mask와 원본 이미지 비트를 연산함
res1 = cv2.bitwise_and(frame, frame, mask=mask_blue)
res2 = cv2.bitwise_and(frame, frame, mask=mask_green)
res3 = cv2.bitwise_and(frame, frame, mask=mask_red)
cv2.imshow('original', frame)
cv2.imshow('blue', res1)
cv2.imshow('green', res2)
cv2.imshow('red', res3)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
cv2.destroyAllwindows()
def video():
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
while True:
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
out.write(frame)
cv2.imshow('frame', frame)
cv2.imshow('gray', gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
out.release()
cv2.destroyAllwindows()
return
def drawLines():
img = np.full((400, 400, 3), 255, np.uint8)
cv2.line(img, (50, 50), (200, 50), (0, 0, 255))
cv2.line(img, (50, 100), (200, 100), (255, 0, 255), 3)
cv2.line(img, (50, 150), (200, 150), (255, 0, 0), 10)
cv2.line(img, (250, 50), (350, 100), (0, 0, 255), 1, cv2.LINE_4)
cv2.line(img, (250, 70), (350, 120), (255, 0, 255), 1, cv2.LINE_8)
cv2.line(img, (250, 90), (350, 140), (255, 0, 0), 1, cv2.LINE_AA)
cv2.arrowedLine(img, (50, 200), (150, 200), (0, 0, 255), 1)
cv2.arrowedLine(img, (50, 250), (350, 250), (255, 0, 255), 1)
cv2.arrowedLine(img, (50, 300), (350, 300), (255, 0, 0), 1, cv2.LINE_8, 0,
0.05)
cv2.drawMarker(img, (50, 350), (0, 0, 255), cv2.MARKER_CROSS)
cv2.drawMarker(img, (100, 350), (0, 0, 255), cv2.MARKER_TILTED_CROSS)
cv2.drawMarker(img, (150, 350), (0, 0, 255), cv2.MARKER_STAR)
cv2.drawMarker(img, (200, 350), (0, 0, 255), cv2.MARKER_DIAMOND)
cv2.drawMarker(img, (250, 350), (0, 0, 255), cv2.MARKER_SQUARE)
cv2.drawMarker(img, (300, 350), (0, 0, 255), cv2.MARKER_TRIANGLE_UP)
cv2.drawMarker(img, (350, 350), (0, 0, 255), cv2.MARKER_TRIANGLE_DOWN)
cv2.imshow("img", img)
cv2.waitKey()
cv2.destroyAllwindows()
def create(a, b):
from Name import name
user = name()
filename = 'write_data.txt'
with open(filename, 'a') as f:
#id = input("Please input an id: ")
#Sname = input("please input a name: ")
id = a
user.id.append(a)
user.name.append(b)
user.index = user.index + 1
f.write((str)(user.id[user.index - 1]) + " " +
user.name[user.index - 1])
f.write("\n")
faceDetect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#cam = cv2.VideoCapture(0)
sampleNum = 0
while (True):
ret, img = self.cap.read()
if len(img.shape) == 3 or len(img.shape) == 4:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
faces = faceDetect.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
sampleNum = sampleNum + 1
cv2.imwrite(
"dataSet/User." + str(id) + "." + str(sampleNum) + ".jpg",
gray[y:y + h, x:x + w])
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.waitKey(100)
cv2.imshow("Face", img)
cv2.waitKey(1)
cv2.destroyAllwindows()
if (sampleNum > 100):
break
#cv2.destroyAllWindows()
cam.release()
def readVideo(skipFrame): # pathName为视频文件路径,skipFrame为视频的第skipFrame帧
selectXY = [(0, 0), (0, 0)]
camera = cv2.VideoCapture(0) # 读取摄像头
if not camera.isOpened(): # 如果为发现摄像头,则按照路径pathName读取视频文件
camera = cv2.VideoCapture(
selectDir()) # 读取视频文件,如pathName='D:/test/test.mp4'
i = 0
while (camera.isOpened()):
ret, frame = camera.read()
cv2.rectangle(frame, selectXY[0], selectXY[1], (255, 255, 0), 2)
i += 1
if (i >= 20):
if (i == skipFrame):
selectXY = get_rect(frame, title='get_rect') # 鼠标画矩形框
print(selectXY)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'): # 点击视频窗口,按q键退出
break
camera.release()
cv2.destroyAllwindows()
def draw_circle(event,x,y,flags,param):
if event == cv2.EVENT
pass
cv2.namedWindow(winname='my_drawing')
cv2.setMouseCallBack('my_drawing', draw_circle)
##showing img with open cv
img =- np.zeros((512,512,3), np.int8)
while True:
cv2.imshow('my_drawing',img)
if cv2.waitKey(20) & 0xFF == 27
break
cv2.destroyAllwindows()
def genmask(video):
while True:
mask_list = os.listdir(mask_dir)
# print(mask_list)
if (len(mask_list) == 0):
detectmask()
mask_list = os.listdir(mask_dir)
for i in range(0, len(mask_list)):
maskname = mask_dir + '/' + mask_list[i]
image = cv.imread(maskname, 1)
ret, jpeg = cv.imencode('.jpg', image)
frame = jpeg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
sleep(1)
shutil.move(maskname, unmask_dir + '/' + mask_list[i])
# os.remove(filename)
cv.waitKey(1000)
cv.destroyAllwindows()
import os
import cv2
import numpy as np
from PIL import Image
recognizer = cv2.createLBPHFaceRecognizer()
path = 'dataSet'
def getImagesWithID(path):
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
faces = []
IDs = []
for imagePath in imagePaths:
faceImg = Image.open(imagePath).convert('L')
faceNp = np.array(faceImg, 'uint8')
ID = int(os.path.split(imagePath)[-1].split('.')[1])
faces.append(faceNp)
print ID
IDs.append(ID)
cv2.imshow("training", faceNp)
cv2.waitKey(10)
return IDs, faces
Ids, faces = getImagesWithID(path)
recognizer.train(faces, np.array(Ids))
recognizer.save('recognizer/reainningData.yml')
cv2.destroyAllwindows()
# E-MAIL: [email protected] # # =========================================================== # # DESCRICAO: lab01.py # # O codigo abaixo armazena num espaco de memoria uma imagem contida no mesmo # diretorio em que foi salvo o programa. Em seguida exibe a imagem salva em # uma janela chamada 'image'. A janela permanece aberta ate que a tecla 'esc' # seja pressionada, se a tecla 's' for pressionada, a imagem e salva na pasta # do diretorio no formato PNG, sob o nome 'messigray' # # =========================================================== #importacao da biblioteca numpy (computacao cientifica, manipulacao de arrays e algebra linear) import numpy as np # importacao da biblioteca OpenCV (Open Source Computer Vision Library) import cv2 as cv # carrega a imagem 'messi5.jpg' para a variavel 'img'. O argumento '0' retorna a imagem em escala de cinza img = cv.imread('messi5.jpg',0) # exibe a imagem salva na variavel 'img' em uma janela chamada 'image' cv.imshow('image',img) k = cv.waitKey(0) # inicializa a espera por tecla pressionada if k == 27: # se 'Esc' pressionado... cv.destroyAllwindows() # fecha todas as janelas elif k == ord('s'): # se 'S' pressionado... cv.imwrite('messigray.png',img) # salva a imagem 'img' no diretorio do programa sob o nome 'messigray' no formato PNG cv.destroyAllwindows() # fecha todas as janelas
def run(source=0, source2=0, source3=0):
# ---- Initializing DNN -----
from sklearn.preprocessing import normalize
caffe.set_mode_gpu()
# cap=cv2.VideoCapture('../../vd1.mp4')
#net = caffe.Net('../../segnet_inference.prototxt','../../segnet_iter_10000.caffemodel',caffe.TEST)
#net = caffe.Net('../../enet_deploy_final.prototxt','../../segnet_iter_32000.caffemodel',caffe.TEST)
net = caffe.Net('../../enet_deploy_final.prototxt','../../segnet_iter_55000.caffemodel',caffe.TEST)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
background = [255,0,0]
car = [0,255,0]
numberplate = [0,0,255]
label_colours = np.array([background, car, numberplate])
label_colour = np.array([2, 1, 0])
cropped=[]
resizeimg=[]
# ---- End of Initializing DNN -----
# ---- Initializing Tracker -----
# initializing yolo
options = {"model": "cfg/tiny-yolo-voc.cfg", "load": "tiny-yolo-voc.weights", "threshold": 0.5} # , "gpu":1.0
tfnet = TFNet(options)
#----violation detection intial data starts----
temp_count = 0
pre_plate=''
URL = "fypandroid-gpu"
slot1_violation = "no"
slot2_violation = "no"
slot1_occupancy = "free"
slot2_occupancy = "free"
slot1_violation_new = "no"
slot2_violation_new = "no"
slot1_occupancy_new = "free"
slot2_occupancy_new = "free"
update_na = 0
slot1_num = "n/a"
slot2_num = "n/a"
updateonce = 0
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
imgavgback = cv2.imread(avgbackgrndpath, 1)
imgavgbackori = imgavgback.copy()
imgavgbackorigray = cv2.cvtColor(imgavgback,cv2.COLOR_BGR2GRAY)
imgavgback = imgavgback[80:400, 200:595]
imgrayavgback = cv2.cvtColor(imgavgback,cv2.COLOR_BGR2GRAY)
imgavgback -= 20
#emptyavg1 = np.mean(imgavgbackorigray[100:180,400:470])
#emptyavg2 = np.mean(imgavgbackorigray[205:300,400:470])
MidPoints = []
MidPoints.append([250,55]) #x,y slot 1
MidPoints.append([255,170]) #x,y slot 2
xyminmax = []
xyminmax.append([135,390,5,120]) #xmin,xmax,ymin,ymax slot 1
xyminmax.append([135,390,115,235]) #xmin,xmax,ymin,ymax slot 2
#cap = cv2.VideoCapture('cam1.mp4')
#----violation detection intial data ends----
# Create the VideoCapture object
cam = cv2.VideoCapture(source)
cam2 = cv2.VideoCapture(source2)
cap = cv2.VideoCapture(source3)
# If Camera Device is not opened, exit the program
if not cam.isOpened():
print "Video device 1 or file 1 couldn't be opened"
exit()
if not cam2.isOpened():
print "Video device 2 or file 2 couldn't be opened"
exit()
if not cap.isOpened():
print "Video device 3 or file 3 couldn't be opened"
exit()
retval, img = cam.read()
retval2, img2 = cam2.read()
ret,frame = cap.read()
if not retval:
print "Cannot capture frame device 1"
exit()
if not retval2:
print "Cannot capture frame device 2"
exit()
if not ret:
print "Cannot capture frame device 3"
exit()
cv2.namedWindow("Image", cv2.WINDOW_NORMAL)
cv2.namedWindow("Image2", cv2.WINDOW_NORMAL)
cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
cv2.imshow("Image2", img2)
cv2.imshow("frame", frame)
# values for tracking
threshld = 10
height = 480 -threshld
width = 640 -threshld
limit = height - 20
tryframes = 8
points = []
points2 = []
# Create the tracker object
tracker = [dlib.correlation_tracker() for _ in xrange(0)]
tracker2 = [dlib.correlation_tracker() for _ in xrange(0)]
# Provide the tracker the initial position of the object
# [tracker[i].start_track(img, dlib.rectangle(*rect)) for i, rect in enumerate(points)]
for k, rect in enumerate(points):
tracker[k].start_track(img, dlib.rectangle(*rect))
for k2, rect in enumerate(points2):
tracker[k2].start_track(img2, dlib.rectangle(*rect))
temp_tracker = list(tracker) #to remove the deleted trackers from tracker without affecting for loop
temp_tracker2 = list(tracker2)
trigerYolo = 0
justdetected = 100
licenseplates = []
licenseplates2 = []
predetect = False
# time.sleep(5)
# ---- End Initializing Tracker -----
# count = 0 # facilitate to set the flag to start tracking (temporary) ****************
# refimg=cv2.imread("111.jpg",0)
# retref, refthresh = cv2.threshold(refimg, -1, 255,0)
# refimg, refcontours, refhierarchy = cv2.findContours(refthresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# refcnt = refcontours[0]
while True:
# count += 1 # facilitate to set the flag to start tracking (temporary) ****************
# print count
# print "----- "+str(justdetected)+" --"
# ---- DNN starts -----
# time.sleep(.5)
start=time.time()
# Read frame from device or file
retval, img = cam.read()
retval2, img2 = cam2.read()
ret,frame=cap.read()
img = cv2.resize(img, (640,480))
img2 = cv2.resize(img2, (640,480))
s=cv2.resize(frame,(480,360))
img2ori = img2.copy()
if not retval:
print "Cannot capture frame device 1 | CODE TERMINATION....."
exit()
if not retval2:
print "Cannot capture frame device 2 | CODE TERMINATION....."
exit()
if not ret:
print "Cannot capture frame device 3 | CODE TERMINATION....."
exit()
justdetected += 1
if justdetected >100 :
justdetected =100
start_violation = 0
start_tracking = False
if justdetected > 4:
#ret=cap.set(cv2.CAP_PROP_FPS,4)
#ret=cap.set(3,360)
#ret=cap.set(4,480)
#frame = cv2.imread("myimage.png",1)
transformer.set_transpose('data', (2,0,1))
transformer.set_channel_swap('data', (2,1,0)) # if using RGB instead of BGR
transformer.set_raw_scale('data', 255.0)
net.blobs['data'].reshape(1,3,360,480)
net.blobs['data'].data[...] = transformer.preprocess('data', s)
output = net.forward()
# print("---%s in sec"%(time.time()-start))
predicted = net.blobs['prob'].data
output = np.squeeze(predicted[0,:,:,:])
ind = np.argmax(output, axis=0)
p = ind.copy()
d = ind.copy()
r = ind.copy()
g = ind.copy()
b = ind.copy()
for l in range(0,3):
p[ind==l] = label_colour[l]
cv2.imwrite("ind.png", p)
ind = cv2.imread("ind.png",0)
ctimage, contours, hierarchy = cv2.findContours(ind,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Vimgc = cv2.drawContours(s, contours, -1, (0,0,255), 3)
#label_colours = np.array([Sky, Building, Pole])
for l in range(0,3):
r[ind==l] = label_colours[l,0]
g[ind==l] = label_colours[l,1]
b[ind==l] = label_colours[l,2]
# cv2.imshow('framex',d)
rgb = np.zeros((ind.shape[0], ind.shape[1], 3))
rgb[:,:,0] = r/255.0
rgb[:,:,1] = g/255.0
rgb[:,:,2] = b/255.0
#plt.figure()
#plt.imshow(rgb,vmin=0, vmax=1)
#plt.show()
# im = cv2.imread(sys.argv[1])
# im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) / 255.
#f = numpy.load(sys.argv[2])
#param_vals = [f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))]
# letter_probs = detect(im_gray, param_vals)
#code = letter_probs_to_code(letter_probs)
# print("Number Plate ->",code)
#pre_plate=''
detectnow = False
NoAllVContours = len(hierarchy[0])
# print NoAllVContours
if(NoAllVContours<6):
for i in range(NoAllVContours):
M = cv2.moments(contours[i])
# print M['m00']
# print "#############################################################################################################################"
# print cv2.matchShapes(refcnt,contours[i],1,0.0)
if (5000>M['m00']>3000):
if (int(M['m01']/M['m00'])>170):
# if ( cv2.matchShapes(refcnt,contours[i],1,0.0) < 0.7):
x,y,w,h=cv2.boundingRect(contours[i])
cropped = s[y:y+h, x:x+w]
resizeimg=cv2.resize(cropped,(256,32))
cv2.imshow('framerty',resizeimg)
im_gray = cv2.cvtColor(resizeimg, cv2.COLOR_BGR2GRAY) / 255.
start=time.time()
f = np.load("weights.npz")
param_vals = [f[n] for n in sorted(f.files, key=lambda s: int(s[4:]))]
letter_probs = detect(im_gray, param_vals)
code = letter_probs_to_code(letter_probs)
print("---%s in sec"%(time.time()-start))
#tempcode = code
if code[0] == "Z":
code = code[1:]
print("Number Plate ->",code)
detectnow = True
if (detectnow & predetect) : #((pre_plate == code) & (temp_count == 1)):
temp_count = 0
start_tracking = True
justdetected = 0
with open('GUI/VehicleNumber.csv','w') as f:
writer=csv.writer(f)
writer.writerow([code])
else:
if (pre_plate!=code):
temp_count = 1
pre_plate=code
# elif(cv2.matchShapes(refcnt,contours[i],1,0.0)==0.0):
# cv2.imshow('framex',rgb)
# cv2.imwrite("sd.png", rgb)
# print pre_plate
predetect = detectnow
# print licenseplates
#print cap.get(cv2.CAP_PROP_FPS)
#print frame.shape
cv2.imshow('frame2',rgb)
if cv2.waitKey(1) & 0xff== ord('q'):
break
# DNN sets the flag to start tracking
# if count == 36: # facilitate to set the flag to start tracking (temporary) ****************
# start_tracking = True
# print licenseplates
# ---- DNN stops -----
# ---- Tracker starts -----
# print temp_tracker
tracker = list(temp_tracker)
tracker2 = list(temp_tracker2)
new_points = []
if trigerYolo > 0: # if detection was not happened for the last frame
yoloresult = tfnet.return_predict(img)
for detectObj in yoloresult:
if (inRegion(detectObj)):
vehicleloc = (detectObj['topleft']['x'],detectObj['topleft']['y']+20,detectObj['bottomright']['x'],detectObj['bottomright']['y']+10)
new_points.append(vehicleloc)
licenseplates.append(code)
print ("Tracking started for the request")
trigerYolo = 0
break
print("Vehicle detection fails for the request for the next frame as well.")
trigerYolo -= 1
cv2.imwrite('track/im_'+str(trigerYolo)+'.png', img)
if(start_tracking): #new vehicle trying to add if "p" pressed
cv2.imwrite('track/im_'+str(trigerYolo)+'.png', img) # to save photos at tracking
# yolo detection
yoloresult = tfnet.return_predict(img)
for detectObj in yoloresult:
if (inRegion(detectObj)):
vehicleloc = (detectObj['topleft']['x'],detectObj['topleft']['y']+20,detectObj['bottomright']['x'],detectObj['bottomright']['y']+10)
new_points.append(vehicleloc)
licenseplates.append(code)
# print licenseplates
print ("Tracking started for the request")
break # only one vehicle will be added per one request *************** optimize this to reduce issues with multi vehicles trying to enter at the same time
if (len(new_points)<1):
print "no vehicle found in frame 1: trying few other frames triggered"
trigerYolo = tryframes # trying few frames to capture a vehicle if not detected from first few frames
if len(new_points) > 0: # if there is a new detected vehicled
# Create the tracker object
new_tracker = [dlib.correlation_tracker() for _ in xrange(len(new_points))]
# Provide the tracker the new positions of the object
for j, rect2 in enumerate(new_points):
new_tracker[j].start_track(img, dlib.rectangle(*rect2))
# [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
tracker.extend(new_tracker)
print "Success: Tracking started for the newly entered vehicle."
temp_tracker = list(tracker)
# Update the tracker
for i in xrange(len(tracker)): #for number of objects to track
confdnc = tracker[i].update(img)
print confdnc
# Get the position of th object, draw a
# bounding box around it and display it.
rect = tracker[i].get_position()
pt1 = (int(rect.left()), int(rect.top()))
pt2 = (int(rect.right()), int(rect.bottom()))
if (pt1[0]< threshld or pt1[1]< threshld or pt2[0]>=width):
del licenseplates[i]
del temp_tracker[i]
print "Object removed: out of the frame/ unexpected behavior"
continue
if (pt2[1]>= limit):
licenseplates2.append(licenseplates[i])
del licenseplates[i]
del temp_tracker[i]
print "Object removed: vehicle enters next view"
new_points2 = [(10, 150, 120, 260)]
# new_tracker2 = dlib.correlation_tracker()
# # Provide the tracker the new positions of the object
# new_tracker2.start_track(img2, dlib.rectangle(*new_points2[0]))
# # [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
# tracker2.append(new_tracker2)
new_tracker2 = [dlib.correlation_tracker() for _ in xrange(len(new_points2))]
# Provide the tracker the new positions of the object
for j2, rect2_2 in enumerate(new_points2):
new_tracker2[j2].start_track(img2, dlib.rectangle(*rect2_2))
# [tracker[j].start_track(img, dlib.rectangle(*rect)) for j, rect in enumerate(points)]
tracker2.extend(new_tracker2)
print "Success: Tracking started from view 2."
continue
# if (confdnc < 3.5):
# print "Low Confidence"
# # continue
cv2.rectangle(img, pt1, pt2, (255, 255, 255), 3)
# print "Object {} tracked at [{}, {}] \r".format(i, pt1, pt2),
# show location of box if mentionedd
loc = (int(rect.left()), int(rect.top()-20))
txt = "Vehicle : [{}]".format(licenseplates[i])
cv2.putText(img, txt, loc , cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 1)
temp_tracker2 = list(tracker2)
for i2 in xrange(len(tracker2)): #for number of objects to track
confdnc2 = tracker2[i2].update(img2)
rect_2 = tracker2[i2].get_position()
pt1_2 = (int(rect_2.left()), int(rect_2.top()))
pt2_2 = (int(rect_2.right()), int(rect_2.bottom()))
cv2.rectangle(img2, pt1_2, pt2_2, (255, 255, 255), 3)
loc = (int(rect_2.left()), int(rect_2.top()-20))
txt = "Vehicle : [{}]".format(licenseplates2[i2])
cv2.putText(img2, txt, loc , cv2.FONT_HERSHEY_SIMPLEX, .5, (255,255,255), 1)
if rect_2.right() >200:
dist1 = ((MidPoints[0][0]- ((pt1_2[0]+pt2_2[0])/2))**2+(MidPoints[0][1]-((pt1_2[1]+pt2_2[1])/2))**2)
dist2 = ((MidPoints[1][0]- ((pt1_2[0]+pt2_2[0])/2))**2+(MidPoints[1][1]-((pt1_2[1]+pt2_2[1])/2))**2)
if dist1<dist2:
if slot1_num != txt:
firebase.patch(URL, {'Slot_01_vehicle_number': licenseplates2[i2]})
slot1_num = txt
if len(tracker2) == 1:
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
else:
if slot2_num != txt:
firebase.patch(URL, {'Slot_02_vehicle_number': licenseplates2[i2]})
slot2_num = txt
if len(tracker2) == 1:
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
start_violation = 1
update_na = 1
else:
start_violation = 0
if update_na == 1:
firebase.patch(URL, {'Slot_01_vehicle_number': "n/a"})
firebase.patch(URL, {'Slot_02_vehicle_number': "n/a"})
update_na = 0
cv2.namedWindow("Image", cv2.WINDOW_NORMAL) # ***** commented *******
# cv2.namedWindow("Image2", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
cv2.imshow("Image2", img2)
cv2.imshow('frame',s)
# ---- end of tracker -----
# ---- Violation detection starts ----
#print "qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq" + str(start_violation)
if start_violation == 1:
#retpark, carpark = cam2.read()
carpark = img2ori
# carpark = cv2.resize(carpark, (640,480)) # Width,Height
#carparkori = carpark.copy()
#carparkgrayori = cv2.cvtColor(carpark,cv2.COLOR_BGR2GRAY)
#vehavg1 = np.mean(carparkgrayori[100:180,400:470])
#vehavg2 = np.mean(carparkgrayori[205:300,400:470])
updateonce = 0
carpark = carpark[80:400, 200:595]
carparkgray = cv2.cvtColor(carpark,cv2.COLOR_BGR2GRAY)
#cv2.imshow('frame',frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
vehicles = cv2.subtract(carpark, imgavgback)
#vehicles = carpark - imgavgback
#vehiclesgray = imgavgbackgray - imgrayavgback
vehiclesgray = cv2.cvtColor(vehicles,cv2.COLOR_BGR2GRAY)
#retv,vehiclesgray = cv2.threshold(imgrayavgback,50,255,cv2.THRESH_BINARY)
blur = cv2.GaussianBlur(vehiclesgray,(5,5),0)
ret3,vehiclesgray = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
ret1,vehiclesgray = cv2.threshold(blur,ret3-25,255,cv2.THRESH_BINARY)
#ret2,vehiclesgray = cv2.threshold(vehiclesgray,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
#vehiclesgray = cv2.adaptiveThreshold(vehiclesgray,255,cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY,11,2)
#vehiclesgray = cv2.adaptiveThreshold(vehiclesgray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)
vcontimage, vehicontours, vehihierarchy = cv2.findContours(vehiclesgray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
NoAllContours = len(vehihierarchy[0])
#print(NoAllContours)
#print(vehicles)
#Getting the parent Vcontour numbers
VehicleNo = []
#Mid points of vehicles
VehicleMid = []
for i in range(NoAllContours):
#if (hierarchy[0,i,3]==-1):
M = cv2.moments(vehicontours[i])
if ((M['m00']>3000)):
VehicleNo.append(i)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
VehicleMid.append([cx,cy])
#Number of all child countours
NumOfVehicles = len(VehicleNo)
#for i in range(NumOfVehicles):
#Vimgc = cv2.drawContours(Pimg, contours, VehicleNo[i] , (0,0,255), 3)
if (NumOfVehicles==0):
imgavgbackori[100:180,400:470] = (0, 255, 0)
imgavgbackori[205:300,400:470] = (0, 255, 0)
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
#if ((abs(vehavg1-emptyavg1))>30):
#img[100:180,400:470] = (0, 0, 255)
#else:
#img[100:180,400:470] = (0, 255, 0)
#if ((abs(vehavg2-emptyavg2))>30):
#img[205:300,400:470] = (0, 0, 255)
#else:
#img[205:300,400:470] = (0, 255, 0)
#if (NumOfVehicles>0):
else:
counter = 0
#error = 0
for i in VehicleNo:
NoIndices = len(vehicontours[i])
vehix = []
vehiy = []
for j in range(NoIndices):
vehix.append(vehicontours[i][j][0,0])
vehiy.append(vehicontours[i][j][0,1])
#Vehicle Contour min max coordinate values
xmin = min(vehix)
xmax = max(vehix)
ymin = min(vehiy)
ymax = max(vehiy)
distance = []
for k in range(2):
vehid = ((MidPoints[k][0]-VehicleMid[counter][0])**2+(MidPoints[k][1]-VehicleMid[counter][1])**2)
distance.append(vehid)
parkingslot = distance.index(min(distance))
drawcon = 0
if (xmin < xyminmax[parkingslot][0]):
#error += 1
drawcon = 1
elif (xmax > xyminmax[parkingslot][1]):
#error += 1
drawcon = 1
elif (ymin < xyminmax[parkingslot][2]):
#error += 1
drawcon = 1
elif (ymax > xyminmax[parkingslot][3]):
#error += 1
drawcon = 1
if ((parkingslot == 0)&(drawcon == 1)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,0,255), 3)
imgavgbackori[100:180,400:470] = (255, 0, 0)
slot1_violation_new = "yes"
slot1_occupancy_new = "occupied"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'occupied'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'yes'})
slot1_violation = slot1_violation_new
if (NumOfVehicles == 1):
imgavgbackori[205:300,400:470] = (0, 255, 0)
slot2_violation_new = "no"
slot2_occupancy_new = "free"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
elif((parkingslot == 0)&(drawcon == 0)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,255,0), 3)
imgavgbackori[100:180,400:470] = (0, 0, 255)
slot1_violation_new = "no"
slot1_occupancy_new = "occupied"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'occupied'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
if (NumOfVehicles == 1):
imgavgbackori[205:300,400:470] = (0, 255, 0)
slot2_violation_new = "no"
slot2_occupancy_new = "free"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
elif ((parkingslot == 1)&(drawcon == 1)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,0,255), 3)
imgavgbackori[205:300,400:470] = (255, 0, 0)
slot2_violation_new = "yes"
slot2_occupancy_new = "occupied"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'occupied'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'yes'})
slot2_violation = slot2_violation_new
if (NumOfVehicles == 1):
imgavgbackori[100:180,400:470] = (0, 255, 0)
slot1_violation_new = "no"
slot1_occupancy_new = "free"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
elif((parkingslot == 1)&(drawcon == 0)):
veimgc = cv2.drawContours(carpark, vehicontours, i, (0,255,0), 3)
imgavgbackori[205:300,400:470] = (0, 0, 255)
slot2_violation_new = "no"
slot2_occupancy_new = "occupied"
if slot2_occupancy != slot2_occupancy_new:
firebase.patch(URL, {'Slot_02_occupancy_status': 'occupied'})
slot2_occupancy = slot2_occupancy_new
if slot2_violation != slot2_violation_new:
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot2_violation = slot2_violation_new
if (NumOfVehicles == 1):
imgavgbackori[100:180,400:470] = (0, 255, 0)
slot1_violation_new = "no"
slot1_occupancy_new = "free"
if slot1_occupancy != slot1_occupancy_new:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
slot1_occupancy = slot1_occupancy_new
if slot1_violation != slot1_violation_new:
firebase.patch(URL, {'Slot_01_violations': 'no'})
slot1_violation = slot1_violation_new
counter += 1
cv2.imshow('thresh',veimgc)
else:
imgavgbackori[100:180,400:470] = (0, 255, 0)
imgavgbackori[205:300,400:470] = (0, 255, 0)
if updateonce == 0:
firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
firebase.patch(URL, {'Slot_01_violations': 'no'})
firebase.patch(URL, {'Slot_02_violations': 'no'})
slot1_violation_new = 'no'
slot2_violation_new = 'no'
slot1_occupancy_new = 'free'
slot2_occupancy_new = 'free'
slot1_occupancy = slot1_occupancy_new
slot2_occupancy = slot2_occupancy_new
slot1_violation = slot1_violation_new
slot2_violation = slot2_violation_new
updateonce = 1
# if slot1_occupancy != slot1_occupancy_new:
# firebase.patch(URL, {'Slot_01_occupancy_status': 'free'})
# slot1_occupancy = slot1_occupancy_new
# if slot2_occupancy != slot2_occupancy_new:
# firebase.patch(URL, {'Slot_02_occupancy_status': 'free'})
# slot2_occupancy = slot2_occupancy_new
# if slot1_violation != slot1_violation_new:
# firebase.patch(URL, {'Slot_01_violations': 'no'})
# slot1_violation = slot1_violation_new
# if slot2_violation != slot2_violation_new:
# firebase.patch(URL, {'Slot_02_violations': 'no'})
cv2.imshow('image',imgavgbackori)
#cv2.imshow('imgrayneg',vehiclesgray)
# ---- Violation detection ends ----
# ---- Empty parking space detection starts ----
#ret, carpark = cam2.read()
#carparkgray = cv2.cvtColor(carpark, cv2.COLOR_BGR2GRAY)
##cv2.imshow('frame',frame)
#if cv2.waitKey(1) & 0xFF == ord('q'):
#break
#vehavg = np.mean(carparkgray[250:300,400:470])
#if abs(vehavg-emptyavg)>30:
#imgavgback[250:300,400:470] = (0, 0, 255)
#else:
#imgavgback[250:300,400:470] = (0, 255, 0)
#cv2.imshow('park',carpark)
#cv2.imshow('empty',imgavgback)
# ---- Empty parking space detection ends ----
# Relase the VideoCapture object
cam.release()
cam2.release()
cap.release()
cv2.destroyAllwindows()
def correspondence_problem(factor):
img1 = cv2.imread('book.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('image2.jpeg', cv2.IMREAD_GRAYSCALE)
## resize
img1 = cv2.resize(img1, dsize=(480, 640), interpolation=cv2.INTER_AREA)
img2 = cv2.resize(img2, dsize=(480, 640), interpolation=cv2.INTER_AREA)
# sift 선언
sift = cv2.xfeatures2d.SIFT_create()
# SIFT 검출
kp1 = sift.detect(img1, None)
kp2 = sift.detect(img2, None)
# SIFT 기술
kp1, des1 = sift.compute(img1, kp1)
kp2, des2 = sift.compute(img2, kp2)
# FLANN 매칭
FlANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FlANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1, des2, k=2)
res = None
good = []
for m, n in matches:
if m.distance < factor * n.distance:
good.append(m)
res = cv2.drawMatches(img1, kp1, img2, kp2, good, res, flags=2)
# 이미지 출력
img1_2, img2_2 = None, None
img1_2 = cv2.drawKeypoints(img1, kp1, img1_2)
img2_2 = cv2.drawKeypoints(
img2, kp2, img2_2, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#
# cv2.imshow('SIFT1 detect', img1_2)
# cv2.imshow('SIFT2 detect', img2_2)
# cv2.imshow('Feature Matching', res)
# homography to find objects
MIN_MATCH_COUNT = 30
print(len(good))
if len(good) > MIN_MATCH_COUNT:
src_pts = np.float32([kp1[m.queryIdx].pt
for m in good]).reshape(-1, 1, 2)
dst_pts = np.float32([kp2[m.trainIdx].pt
for m in good]).reshape(-1, 1, 2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# We have seen that there can be some possible errors while matching which may affect the result.
# To solve this problem, algorithm uses RANSAC or LEAST_MEDIAN (which can be decided by the flags)
matchesMask = mask.ravel().tolist()
# ravel(): return a contiguous flattened array, tolist(): transfer tensor to list.
h, w = img1.shape[:2]
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1],
[w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
dst += np.float32([w, 0])
dst = cv2.polylines(dst, [np.int32(dst)], True, 255, 2, cv2.LINE_AA)
# 그릴 대상 이미지, pts[i]위치 배열의 포인트 수,마지막과 처음 포인트 연결
else:
print("not enough matches", len(good))
matchesMask = None
draw_params = dict(matchColor=(0, 255, 0),
singlePointColor=(255, 0, 0),
matchesMask=matchesMask,
flags=0)
# img4 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
# Draw bounding box in Red
img4 = cv2.polylines(res, [np.int32(dst)], True, (255, 0, 0), 2,
cv2.LINE_AA)
cv2.imshow('drawMatches', img4)
cv2.waitKey(0)
cv2.destroyAllwindows()
def find_person_face(path_to_img=None,
path_to_save=None,
path_to_contr=None,
confid=90.0,
show=False,
cascade=args.home + '/' +
'haarcascade_frontalface_default.xml'):
label_id = 0
ids = list()
datas = list()
contr_list = dbh.sort_files(path_to_contr, ['.jpg', '.png'])
os.makedirs(path_to_save, exist_ok=True)
file_list = dbh.sort_files(path_to_img, ['.jpg', '.png'])
face_cascade = None
resize_const = 800
if len(file_list) == 0:
print('Искомых файлов в директории не найдено.')
return 0
try:
face_cascade = cv2.CascadeClassifier(cascade)
except:
print('Ошибка... [', cascade, '] не найден')
if show:
cv2.namedWindow('Controls images', cv2.WINDOW_NORMAL)
for name_img in contr_list:
original_contr = cv2.imread(name_img)
gray = cv2.cvtColor(original_contr, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=3,
minSize=(30, 30))
for (x, y, w, h) in faces:
datas.append(gray[y:y + h, x:x + w])
ids.append(label_id)
label_id += 1
if show:
cv2.imshow('Controls images', gray[y:y + h, x:x + w])
cv2.waitKey(50)
if show:
cv2.destroyAllWindows()
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.train(datas, np.array(ids))
if show:
cv2.namedWindow('original', cv2.WINDOW_NORMAL)
cv2.namedWindow('saving', cv2.WINDOW_NORMAL)
for img_name in file_list:
original = cv2.imread(img_name)
s = original.shape
ys = s[0]
xs = s[1]
if xs > resize_const and xs > ys:
dim = xs / resize_const
xs = resize_const
ys = int(ys / dim)
elif ys > resize_const and ys > xs:
dim = ys / resize_const
ys = resize_const
xs = int(xs / dim)
else:
pass
original = cv2.resize(original, (xs, ys), cv2.INTER_AREA)
if show:
cv2.imshow('original', original)
key = cv2.waitKey(50)
if key == 27:
cv2.destroyAllwindows()
return
gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=4,
minSize=(30, 30))
for (x, y, w, h) in faces:
num, conf = recognizer.predict(gray[y:y + h, x:x + w])
print('Num:', num, ' Conf:', round(conf, 5))
if conf <= confid:
cv2.imwrite(
path_to_save + '/' + 'person_' +
os.path.basename(img_name), original[y:y + h, x:x + w])
print('Saving ' + 'person_' + os.path.basename(img_name), 'to',
path_to_save)
if show:
cv2.imshow('saving', original[y:y + h, x:x + w])
key = cv2.waitKey(50)
if key == 27:
cv2.destroyAllwindows()
return
if show:
cv2.destroyAllWindows()
def perspectiveTransform(self):
self.video=str(self.fullpath)
self.camera = cv2.VideoCapture(self.video)
success, firstFrame = self.camera.read() #reads only the first image of the video for calibration function
rows,cols,ch = firstFrame.shape
self.xdist=[]
self.ydist=[]
self.pts1 = []
self.pts2 = []
self.count = 0
#mouse callback function, draws points and captures coordinates
def draw_circle(event,x,y,flags,param):
# global ix,iy,count,xdist,ydist
if event == cv2.EVENT_LBUTTONDOWN:
# break
cv2.circle(firstFrame,(x,y),2,(0,255,0),-1)
cv2.circle(firstFrame,(x,y),10,(255,0,0),1)
cv2.circle(firstFrame,(x,y),15,(255,0,0),1)
cv2.putText(firstFrame, label, (x+20, y+20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
ix=x
iy=y
self.xdist.append(ix)
self.ydist.append(iy)
a=[ix,iy]
self.pts1.append(a)
if self.count==0:
toAppend=[ix,iy]
self.pts2.append(toAppend)
if self.count == 1:
toAppendix=ix
toAppendiy=self.pts2[0][1]
toAppend=[toAppendix,toAppendiy]
self.pts2.append(toAppend)
if self.count == 2:
toAppendix=self.pts2[0][0]
toAppendiy=iy
toAppend=[toAppendix,toAppendiy]
self.pts2.append(toAppend)
self.count=self.count+1
cv2.namedWindow('firstframe')
cv2.setMouseCallback('firstframe',draw_circle)
while self.count <= 2:#keeps window open as long as count is less than 3 so user can interact with setMouseCallback
cv2.imshow('firstframe',firstFrame)
if self.count==0:
cv2.putText(firstFrame, "1. Choose top left corner: (1)", (10, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Point 1"
if self.count==1:
cv2.putText(firstFrame, "2. Choose top right corner:", (10, 40),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Point 2"
if self.count==2:
cv2.putText(firstFrame, "3. Choose bottom right corner", (10, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Point 3"
k = cv2.waitKey(1) & 0xFF
if k == ord('q'):
cv2.camera.release()
cv2.destroyAllwindows()
break
if k == ord('q'):
cv2.camera.release()
cv2.destroyAllwindows()
self.pts1 = np.float32(self.pts1)
self.pts2 = np.float32(self.pts2)
self.M = cv2.getAffineTransform(self.pts1,self.pts2)
dst = cv2.warpAffine(firstFrame,self.M,(cols,rows))
cv2.imshow('dst',dst)
print self.M
def doCalibration(self,rectify):
self.cal_TE.append("Calibration window open:")
self.rectify=rectify
self.video=str(self.fullpath)
self.camera = cv2.VideoCapture(self.video)
success, firstFrame = self.camera.read()
if rectify==True:
rows,cols,ch = firstFrame.shape
firstFrame = cv2.warpAffine(firstFrame,self.M,(cols,rows))
self.xdist=[]
self.ydist=[]
self.count = 0
#mouse callback function, draws points and captures coordinates
def draw_circle(event,x,y,flags,param):
global ix,iy,count,xdist,ydist
if event == cv2.EVENT_LBUTTONDOWN:
cv2.circle(firstFrame,(x,y),2,(0,255,0),-1)
cv2.circle(firstFrame,(x,y),10,(255,0,0),1)
cv2.circle(firstFrame,(x,y),15,(255,0,0),1)
cv2.putText(firstFrame, label, (x+20, y+20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
self.count=self.count+1
ix=x
iy=y
self.xdist.append(ix)
self.ydist.append(iy)
self.cal_TE.append("Point %d chosen with x-pixel coordinates of %d and y-pixel coordinates of %d" % (self.count, ix, iy))
cv2.namedWindow('Calibration frame')
cv2.setMouseCallback('Calibration frame',draw_circle)
while self.count <= 3:
cv2.imshow('Calibration frame',firstFrame)
if self.count==0:
cv2.putText(firstFrame, "1. Place first point (1)", (10, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Point 1"
if self.count==1:
cv2.putText(firstFrame, "2. Place second point (2)", (10, 40),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Point 2"
if self.count==2:
cv2.putText(firstFrame, "3. Place point of known distance from flume entrance and bottom wall", (10, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
label="Known point"
if self.count==3:
cv2.putText(firstFrame, "Exit pop-up and enter parameters ...", (10, 80),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
self.k = cv2.waitKey(1) & 0xFF
if self.k == ord('q'):
self.cal_TE.append("Enter calibration parameters and then click output calibration")
cv2.camera.release()
cv2.destroyAllwindows()
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 7 15:04:08 2018
@author: Administrator
"""
'''
视频捕捉
'''
import cv2 as cv
vc = cv.VideoCapture(0) # 0 - 视频捕捉设备编号
while True:
frame = vc.read()[1] # return 视频帧属性，取index=1的属性
cv.imshow('VideoCapture', frame)
if cv.waitKey(33) == 27:
'''
# 27为ESC按键， 33ms(1s看30张图片相当于连续的图片动图串成视频，即帧速率30fps/s) 表示30fps 帧速率 30帧/s
'''
break
vc.release() # 使用完后释放视频设备
cv.destroyAllwindows() # 销毁所有窗口，包括隐藏窗口 如缓存
def align_video(video_path, predictor_path, out_path, showStabilized = False):
fourcc = cv2.VideoWriter_fourcc(*"XVID")
writer = cv2.VideoWriter(out_path, fourcc, 20, (640,480), True)
# Initializing video capture object.
cap = cv2.VideoCapture(videoFileName)
if(cap.isOpened()==False):
print("Unable to load video")
winSize = 101
maxLevel = 10
fps = 30.0
# Grab a frame
ret,imPrev = cap.read()
size = imPrev.shape[0:1]
detector = dlib.get_frontal_face_detector()
landmarkDetector = dlib.shape_predictor(predictor_path)
# Initializing the parameters
points=[]
pointsPrev=[]
pointsDetectedCur=[]
pointsDetectedPrev=[]
eyeDistanceNotCalculated = True
eyeDistance = 0
isFirstFrame = True
# Initial value, actual value calculated after 100 frames
fps = 10
count =0
ret, frame1 = cap.read()
imGrayPrev = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)
try:
while(True):
if (count==0):
t = cv2.getTickCount()
# Grab a frame
ret,im = cap.read()
if im is None:
break
imDlib = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# COnverting to grayscale
imGray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
height = im.shape[0]
IMAGE_RESIZE = float(height)/RESIZE_HEIGHT
# Resize image for faster face detection
imSmall = cv2.resize(im, None, fx=1.0/IMAGE_RESIZE, fy=1.0/IMAGE_RESIZE,interpolation = cv2.INTER_LINEAR)
imSmallDlib = cv2.cvtColor(imSmall, cv2.COLOR_BGR2RGB)
# Skipping the frames for faster processing
if (count % SKIP_FRAMES == 0):
faces = detector(imSmallDlib,0)
# If no face was detected
if len(faces)==0:
print("No face detected")
# If faces are detected, iterate through each image and detect landmark points
else:
for i in range(0,len(faces)):
# Face detector was found over a smaller image.
# So, we scale face rectangle to correct size.
newRect = dlib.rectangle(int(faces[i].left() * IMAGE_RESIZE),
int(faces[i].top() * IMAGE_RESIZE),
int(faces[i].right() * IMAGE_RESIZE),
int(faces[i].bottom() * IMAGE_RESIZE))
# Detect landmarks in current frame
landmarks = landmarkDetector(imDlib, newRect).parts()
# Handling the first frame of video differently,for the first frame copy the current frame points
if (isFirstFrame==True):
pointsPrev=[]
pointsDetectedPrev = []
[pointsPrev.append((p.x, p.y)) for p in landmarks]
[pointsDetectedPrev.append((p.x, p.y)) for p in landmarks]
# If not the first frame, copy points from previous frame.
else:
pointsPrev=[]
pointsDetectedPrev = []
pointsPrev = points
pointsDetectedPrev = pointsDetectedCur
# pointsDetectedCur stores results returned by the facial landmark detector
# points stores the stabilized landmark points
points = []
pointsDetectedCur = []
[points.append((p.x, p.y)) for p in landmarks]
[pointsDetectedCur.append((p.x, p.y)) for p in landmarks]
# Convert to numpy float array
pointsArr = np.array(points,np.float32)
pointsPrevArr = np.array(pointsPrev,np.float32)
# If eye distance is not calculated before
if eyeDistanceNotCalculated:
eyeDistance = interEyeDistance(landmarks)
print(eyeDistance)
eyeDistanceNotCalculated = False
if eyeDistance > 100:
dotRadius = 3
else:
dotRadius = 2
sigma = eyeDistance * eyeDistance / 400
s = 2*int(eyeDistance/4)+1
# Set up optical flow params
lk_params = dict(winSize = (s, s), maxLevel = 5, criteria = (cv2.TERM_CRITERIA_COUNT | cv2.TERM_CRITERIA_EPS, 20, 0.03))
# Python Bug. Calculating pyramids and then calculating optical flow results in an error. So directly images are used.
# ret, imGrayPyr= cv2.buildOpticalFlowPyramid(imGray, (winSize,winSize), maxLevel)
pointsArr,status, err = cv2.calcOpticalFlowPyrLK(imGrayPrev,imGray,pointsPrevArr,pointsArr,**lk_params)
# Converting to float
pointsArrFloat = np.array(pointsArr,np.float32)
# Converting back to list
points = pointsArrFloat.tolist()
# Final landmark points are a weighted average of
# detected landmarks and tracked landmarks
for k in range(0,len(landmarks)):
d = cv2.norm(np.array(pointsDetectedPrev[k]) - np.array(pointsDetectedCur[k]))
alpha = math.exp(-d*d/sigma)
points[k] = (1 - alpha) * np.array(pointsDetectedCur[k]) + alpha * np.array(points[k])
# Drawing over the stabilized landmark points
if showStabilized is True:
for p in points:
cv2.circle(im,(int(p[0]),int(p[1])),dotRadius, (255,0,0),-1)
else:
for p in pointsDetectedCur:
cv2.circle(im,(int(p[0]),int(p[1])),dotRadius, (0,0,255),-1)
isFirstFrame = False
count = count+1
# Calculating the fps value
if ( count == NUM_FRAMES_FOR_FPS):
t = (cv2.getTickCount()-t)/cv2.getTickFrequency()
fps = NUM_FRAMES_FOR_FPS/t
count = 0
isFirstFrame = True
# Display the landmarks points
cv2.putText(im, "{:.1f}-fps".format(fps), (50, size[0]-50), cv2.FONT_HERSHEY_COMPLEX, 1.5, (0, 0, 255), 3,cv2.LINE_AA)
winName = "Aligned facial landmark detector"
cv2.imshow(winName, im)
frame_1 = cv2.resize(im,(640,480)) # manually resize frame
writer.write(frame_1)
key = cv2.waitKey(25) & 0xFF
# Use spacebar to toggle between Stabilized and Unstabilized version.
if key==32:
showStabilized = not showStabilized
# Stop the program.
if key==27:
sys.exit()
# Getting ready for next frame
imPrev = im
imGrayPrev = imGray
except Exception as e:
print("Exceptions occurs......")
writer.release()
cap.release()
cv2.destroyAllwindows()
import numpy as np
import cv2
print 'Loading image'
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
test = face_cascade.load('C:\opencv\sources\data\haarcascades\haarcascade_frontalface_default.xml')
print(test)
img = cv2.imread('test2.jpg')
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print 'Loaded image, applying cascade classifiers'
faces = face_cascade.detectMultiScale(grayimg, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)
roi_gray = grayimg[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
imgResized = cv2.resize(img, (0,0), fx=0.5, fy=0.5)
cv2.imshow('Face Detection', imgResized)
cv2.waitKey(0)
cv2.destroyAllwindows()
