def grab_screen():
last_time = time.time()
frame, accum_time, fps = 0, 0, 0
while True:
# screenshot normalization
screen = np.array(ImageGrab.grab(bbox=(0, 40, 800, 640)))
# screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.GaussianBlur(screen, (3, 3), 0)
# calculate fps
this_time = time.time()
print('loop took {} seconds'.format(this_time - last_time))
accum_time += this_time - last_time
last_time = time.time()
frame += 1
if accum_time >= 1:
fps = frame
print('fps:', frame)
frame, accum_time = 0, 0
cv2.putText(screen, 'fps:{}'.format(fps), (10, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.8, (0, 255, 0), 2)
# show screenshot
cv2.imshow('screen', screen)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destoryAllWindows()
break
def faceDetect():
face_cascade = cv2.CascadeClassifier(HAAR_CASCADE_XML_FILE_FACE)
video_capture = cv2.VideoCapture(gst_str, cv2.CAP_GSTREAMER)
if video_capture.isOpened():
cv2.namedWindow("faceDetect", cv2.WINDOW_NORMAL)
cv2.resizeWindow("faceDetect", 800, 600)
while True:
return_key, image = video_capture.read()
if not return_key:
break
grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detected_faces = face_cascade.detectMultiScale(grayscale_image, 1.3, 5)
#create rectangle around face
for(x_pos, y_pos, width, height) in detected_faces:
cv2.rectangle(image, (x_pos, y_pos), (x_pos + width, y_pos + height), (0, 0, 0), 2)
cv2.imshow("faceDetect", image)
# key esc as quit
key = cv2.waitKey(30) & 0xff
if key == 27:
break
video_capture.release()
cv2.destoryAllWindows()
else:
print("open camera fail")
def get_live_video(queue, state):
#emotion = "neutral"
recent_frames = deque()
alt_frame = True
video = cv2.VideoCapture(0)
detector = dlib.get_frontal_face_detector()
count = 1
while True:
(grabbed, frame) = video.read()
if not grabbed:
print("not grabbed")
break
if alt_frame:
faces = detector(frame)
frame_copy = frame.copy()
for face in faces:
x1 = face.left()
y1 = face.top()
x2 = face.right()
y2 = face.bottom()
face = frame[y1:y2, x1:x2]
cv2.rectangle(frame_copy, (x1, y1), (x2, y2), (255, 0, 0),
thickness=7)
#cv2.putText(frame_copy, predicted_emotion, (int(x1), int(y1)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
# if not predicted_emotion.empty():
# emotion = predicted_emotion.get()
# cv2.putText(frame_copy, emotion, (int(x1), int(y1)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
cv2.imshow('Frame', frame_copy)
frame = cv2.resize(face, (224, 224)).astype("float32")
norm_image = cv2.normalize(frame,
None,
alpha=0,
beta=1,
norm_type=cv2.NORM_MINMAX,
dtype=cv2.CV_32F)
recent_frames.append(norm_image)
if len(recent_frames) > 5:
recent_frames.popleft()
#e.wait()
#print(e.is_set(), "event state in process 1")
# if e.is_set():
# #print("pushing to queue")
# #queue.put(norm_image)
# queue.put(recent_frames)
# #queue.put(count)
if not state.empty():
state.get()
queue.put(recent_frames)
count += 1
alt_frame = not alt_frame
if cv2.waitKey(10) == ord('q'): #wait until keyboard interrupt
break
cv2.destoryAllWindows()
def trackbar():
img = np.zeros((200, 512, 3), np.uint8)
cv2.namedWindow('color_palette')
cv2.createTrackbar('B', 'color_palette', 0, 255, onChange)
cv2.createTrackbar('G', 'color_palette', 0, 255, onChange)
cv2.createTrackbar('R', 'color_palette', 0, 255, onChange)
switch = '0: OFF\n1: ON'
cv2.createTrackbar(switch, 'color_palette', 0, 1, onChange)
while True:
cv2.imshow('color_palette', img)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
b = cv2.getTrackbarPos('B', 'color_palette')
g = cv2.getTrackbarPos('G', 'color_palette')
r = cv2.getTrackbarPos('G', 'color_palette')
s = cv2.getTrackbarPos('G', 'color_palette')
if s == 0:
img[:] = 0
else:
img[:] = [b, g, r]
cv2.destoryAllWindows()
def __del__(self):
self.sock.close()
#服务器端连接成功后尝试创建一个窗口用于显示接收道德视频
try:
cv2.destoryAllWindows()
except:
pass
def Drone_tracking_ex (cap) :
ret, frame = cap.read()
#Set Roi
c, r, w, h = 900, 650, 70, 70
track_window = (c, r, w, h)
#mask / histogram be made
roi = frame[r : r+h, c: c+w]
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, np.array((0., 30., 32.)), np.array((180., 255., 255.)))
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 80, 1)
while True :
ret, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
dst = cv2.calcBcakProject([hsv], [0], roi_hist, [0, 180], 1)
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
x, y, w, h = track_window
cv2.rectangle(frame, (x, y), (x+w, y+h), 255, 2)
cv2.putText(frame, 'Tracked', (x-25, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv.CV_AA)
cv2.imshow('Tracking', frame)
if cv2.waitKey(1) & 0xFF == ord('q') :
break
cap.release()
cv2.destoryAllWindows()
def face_rec():
names = ['wang', 'll']
[faces_sample, lables] = read_images(GENERATE_PATH)
lables = np.asarray(lables, dtype=np.int32)
model = cv2.face.LBPHFaceRecognizer_create()
model.train(np.asarray(faces_sample), np.asarray(lables))
camera = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier(
'./haarcascades/haarcascade_frontalface_default.xml')
while True:
status, frame = camera.read()
faces = face_cascade.detectMultiScale(frame, 1.3, 5)
for (x, y, w, h) in faces:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
roi = gray[x:x + w, y:y + h]
# 已经训练结果对脸部进行判断
try:
params = model.predict(roi)
print('标签：%s， 相似率：%.2f' % (params[0], params[1]))
cv2.putText(frame, names[params[0]], (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
except:
continue
cv2.imshow('camera', frame)
if cv2.waitKey(100) & 0xff == ord('q'):
break
cv2.destoryAllWindows()
def showImage():
imgfile = 'images/cat.jpg'
img = cv2.imread(imgfile, cv2.IMREAD_COLOR)
cv2.namedWindow('cat', cv2.WINDOW_NORMAL)
cv2.imshow('cat', img)
cv2.waitKey(0)
cv2.destoryAllWindows()
def show_target(name):
"""
展示检测的目标
:param name:
:return:
"""
cv2.imshow('Show', name)
cv2.waitKey(0)
cv2.destoryAllWindows()
def main(args):
rospy.init_node('NCS_node', anonymous=False)
ic = NCS_node()
try:
while (1):
ic.ncs()
except KeyboardInterrupt:
print "shutting down"
cv2.destoryAllWindows()
def display(filename):
im = cv2.imread(filename, 1) #画像の読み込み
if im is None: #エラー処理
print(filename, 'does not exist!')
sys.exit('Error')
height, width = im.shape[0:2] #画像サイズを取得
print('height:', height, 'width:', width) #画像サイズ表示
cv2.imshow(filename, im)
cv2.waitKey(0) #キー入力待機
cv2.destoryAllWindows() #すべてのウィンドウズを閉じる
def show(self):
if self.cap.isOpened():
while True:
_, img = self.cap.read()
cv2.imshow("frame", img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
self.cap.release()
cv2.destoryAllWindows()
def showImage():
imgfile= '/home/parksanghyeon/Downloads/practice1.png'
img = cv2.imread(imgfile,cv2.IMREAD_COLOR) #second argument means output IMREAD_COLOR=1
#RGB
img2 = cv2.imread(imgfile,cv2.IMREAD_GRAYSCALE) #second argument means output IMREAD_GRAYSCALE=0
#GRAY
#img3 = cv2.imread(imgfile,cv2.IMREAD_UNCHANGED)#second argument means output IMREAD_UNCHANGED=-1
#RGBA
cv2.namedWindow('practice1',cv2.WINDOW_NORMAL)
cv2.namedWindow('practice2',cv2.WINDOW_AUTOSIZE)
#cv2.namedWindow('practice3',cv2.WINDOW_NORMAL)
cv2.imshow('cutting',img)
subimg = img[300:400, 350:750]
cv2.imshow('cutting', subimg)
img[300:400,0:400] = subimg
cv2.imshow('modified',img)
# b, g, r = cv2.split(img)
b = img[:,:,0]
g = img[:,:,1]
r = img[:,:,2]
print(img[100,100])
print(b[100, 100], g[100, 100], r[100, 100])
cv2.imshow('blue channel', b)
cv2.imshow('green channel', g)
cv2.imshow('red channel',r)
merged_img = cv2.merge((b,g,r))
cv2.imshow('merged', merged_img)
#cv2.imshow('practice1', img1)
#cv2.imshow('practice2', img2)
#cv2.imshow('practice3', img3)
print(img.shape)
print(img2.shape)
k =cv2.waitKey(0) & 0xFF
if k == 27:
cv2.destroyAllWindows()
if k == ord('c'):
cv2.imwrite('/home/parksanghyeon/Downloads/practice2.png',img1)
cv2.destoryAllWindows() #if i type the 'c', it means copy practice1. to pratice3
def stop(self):
self.cap.release
cv2.destoryAllWindows()
# if __name__=="__main__":
# test = Vision()
# while True:
# test.update()
# if cv2.waitKey(1) & 0xFF == ord('q'):
# break
# test.stop()
def main():
img = cv2.imread("Bikesgray.jpg", 0)
edges, img_grad = Sobel_op(img)
ret, thresh = cv2.threshold(edges, 120, 255, cv2.THRESH_BINARY)
H, v_h = HOG(edges, img_grad)
cv2.imshow("th", thresh)
cv2.imshow("H_v", v_h)
cv2.waitKey(0)
cv2.destoryAllWindows()
def demo_img(net, detector, transform, img, save_dir):
_t = {'inference': Timer(), 'misc': Timer()}
scale = torch.Tensor(
[img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
with torch.no_grad():
x = transform(img).unsqueeze(0)
if args.cuda:
x = x.cuda()
scale = scale.cuda()
_t['inference'].tic()
out = net(x) # forward pass
boxes, scores = detector.forward(out, priors)
inference_time = _t['inference'].toc()
boxes = boxes[0]
scores = scores[0]
boxes *= scale
boxes = boxes.cpu().numpy()
scores = scores.cpu().numpy()
_t['misc'].tic()
for j in range(1, num_classes):
max_ = max(scores[:, j])
inds = np.where(scores[:, j] > args.threshold)[0]
if inds is None:
continue
c_bboxes = boxes[inds]
c_scores = scores[inds, j]
c_dets = np.hstack((c_bboxes, c_scores[:,
np.newaxis])).astype(np.float32,
copy=False)
#keep = nms(c_dets, args.threshold, force_cpu=args.cpu)
keep = nms_py(c_dets, args.threshold)
c_dets = c_dets[keep, :]
c_bboxes = c_dets[:, :4]
for bbox in c_bboxes:
# Create a Rectangle patch
label = labels[j - 1]
score = c_dets[0][4]
cv2.rectangle(img, (int(bbox[0]), int(bbox[1])),
(int(bbox[2]), int(bbox[3])), COLORS[1], 2)
cv2.putText(
img, '{label}: {score:.2f}'.format(label=label, score=score),
(int(bbox[0]), int(bbox[1])), FONT, 1, COLORS[1], 2)
nms_time = _t['misc'].toc()
#status = ' inference time: {:.3f}s \n nms time: {:.3f}s \n FPS: {:d}'.format(inference_time, nms_time, int(1/(inference_time+nms_time)))
status = 't_inf: {:.3f} s || t_misc: {:.3f} s \r'.format(
inference_time, nms_time)
cv2.putText(img, status[:-2], (10, 20), FONT, 0.7, (0, 0, 0), 5)
cv2.putText(img, status[:-2], (10, 20), FONT, 0.7, (255, 255, 255), 2)
print(status)
cv2.imwrite(save_dir, img)
cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destoryAllWindows()
def contour():
img = cv2.imread('D:\PycharmProject\data\IU.jpg')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thr = cv2.threshold(imgray, 127, 255, 0)
_, contours, _ = cv2.findContours(thr, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0, 0, 255), 1)
cv2.imshow('thresh', thr)
cv2.imshow('contour', img)
cv2.waitKey(0)
cv2.destoryAllWindows()
def getFaceFromCamera(outDir):
createDir(outDir)
camera = cv2.VideoCapture(0)
haar = cv2.CascadeClassifier("/home/crq/opencv-python/opencv/data/haarcascades_cuda/haarcascade_frontalface_default.xml")
#haar = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
count = 1
while 1:
if count <= 200:
print('process...')
success, img = camera.read()
if img == None:
print("img is none")
continue
print(success)
# cv2.imshow('img', img)
grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('img', grayImage)
# key = cv2.waitKey(30)
faceImage = haar.detectMultiScale(grayImage, 1.3, 5)
# print(faceImage)
for f_x,f_y,f_w,f_h in faceImage:
print(f_x, f_y, f_w, f_h)
face = img[f_y:f_y+f_h, f_x:f_x+f_w]
#print(img)
#cv2.imshow('source', img)
#cv2.imshow('img', face)
face = cv2.resize(face, (size, size))
cv2.imwrite(os.path.join(outDir, str(count)+'.jpg'), face)
#print(img)
if img == None:
print("before recangle img is none")
cv2.rectangle(img, (f_x, f_y), (f_x + f_w, f_y + f_h), (255, 0, 0), 2)
if img == None:
print("rectangle fail")
count += 1
#cv2.imshow('img', img)
if img == None:
print("img is none")
continue
cv2.imshow('source', img)
key = cv2.waitKey(30) & 0xff
if key == 27:
break
else:
break
camera.release()
cv2.destoryAllWindows()
def picture():
old_frame = cv2.imread("3.jpg")
frame = cv2.imread("3.jpg")
# ShiTomasi 角点检测参数
feature_params = dict(maxCorners=100,
qualityLevel=0.05,
minDistance=7,
blockSize=7)
# lucas kanade光流法参数
lk_params = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
10, 0.03))
# 创建随机颜色
color = np.random.randint(0, 255, (100, 3))
#找到原始灰度图
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
#获取图像中的角点，返回到p0中
p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params)
# 创建一个蒙版用来画轨迹
mask = np.zeros_like(old_frame)
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #灰度化
# 计算光流
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None,
**lk_params)
# 选取好的跟踪点
good_new = p1[st == 1]
good_old = p0[st == 1]
# 画出轨迹
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel() #多维数据转一维,将坐标转换后赋值给a，b
c, d = old.ravel()
mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2) #画直线
frame = cv2.circle(frame, (a, b), 1, color[i].tolist(), -1) #画点
img = cv2.add(frame, mask) # 将画出的线条进行图像叠加
cv2.imshow('frame', img) #显示图像
cv2.waitKey(0)
cv2.destoryAllWindows() #关闭所有窗口
def showImage2():
imgfile = 'images/cat.jpg'
img = cv2.imread(imgfile, cv2.IMREAD_COLOR)
cv2.imshow('cat', img)
while (True):
k = cv2.waitKey(0) & 0xFF
if k == 27:
cv2.destroyAllWindows()
break
elif k == ord('c'):
cv2.imwrite('images/cat_copy.jpg', img)
cv2.destoryAllWindows()
else:
print(k)
def addImage(imgfile1, imgfile2):
img1 = cv2.imread(imgfile1)
img2 = cv2.imread(imgfile2)
cv2.imshow('img1', img1)
cv2.imshow('img2', img2)
add_img1 = img1 + img2
add_img2 = cv2.add(img1, img2)
cv2.imshow('img1+img2', add_img1)
cv2.imshow('add(img1,img2)', add_img2)
cv2.waitKey(0)
cv2.destoryAllWindows()
def take_snapshot():
number = random.randint(0, 100)
videoCaptureObject = cv2.VideoCapture(0,cv2.CAP_DSHOW)
result = True
while(result):
ret, frame = videoCaptureObject.read()
img_name = "img_" + str(number) + ".png"
cv2.imwrite(img_name, frame)
start_time = time.time
result = False
return img_name
print("Snapshot taken")
videoCaptureObject.release()
cv2.destoryAllWindows()
def camPreview(previewName, camID): cv2.namedWindow(previewName) cam = cv2.VideoCapture(camID) if cam.isOpened(): rval, frame = cam.read() else: rval = False while rval: cv2.imshow(previewName, frame) rval, frame = cam.read() key = cv2.waitKey(20) if key == 27: break cv2.destoryAllWindows(previewName)
def showImage(img_flag):
img_file = '../resources/img/sana01.jpeg'
window_title = 'sana'
img = cv2.imread(img_file, img_flag)
cv2.namedWindow(window_title, cv2.WINDOW_NORMAL)
cv2.imshow(window_title, img)
key = cv2.waitKey(0) & 0xFF
if key == 27:
cv2.destoryAllWindows()
elif key == ord('c'):
cv2.imwrite('../resources/img/sana_copy.jpg', img)
cv2.destoryAllWindows()
def drawing():
img = np.zeros((512, 512, 3), np.uint8)
#다양한 색상과 선두께를 가진 도형 그리기
cv2.line(img, (0, 0), (511, 511), (255, 0, 0), 5)
cv2.rectangle(img, (384, 0), (510, 128), (0, 255, 0), 3)
cv2.circle(img, (477, 63), 63, (0, 0, 255), -1)
cv2.ellipse(img, (256, 256), (100, 50), 0, 0, 180, (255, 0, 0), -1)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, ' OpenCV', (10, 500), font, 4, (255, 255, 255), 2)
cv2.putText(img, ' OpenCV', (10, 500), font, 4, (255, 255, 255), 2)
cv2.imshow('drawing', img)
cv2.waitKey(0)
cv2.destoryAllWindows()
def save():
cap = cv.VideoCapture(0)
fourcc = cv.VideoWriter_fourcc(*'XVID')
out = cv.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
out.write(frame)
cv.imshow('frame', frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv.destoryAllWindows()
def pyramid():
img = cv2.imread('D:\PycharmProject\data\suji.jpg',cv2.IMREAD_ANYCOLOR)
tmp = img.copy()
win_titles =['org', 'level1', 'level2', 'level3']
g_down = []
g_down.append(tmp)
for i in range(3):
tmp1 = cv2.pyrDown(tmp)
g_down.append(tmp1)
tmp = tmp1
for i in range(4):
cv2.imshow(win_titles[i], g_down[i])
cv2.waitKey(0)
cv2.destoryAllWindows()
def main():
try:
cap = cv2.VideoCapture(VDEV)
except:
"Failed to open" + VDEV
showVideoInfo(cap)
while (True):
ret, frame = cap.read()
cv2.imshow("preview", frame)
rotate_frame = cv2.rotate(frame, cv2.ROTATE_90_CLOCKWISE)
cv2.imshow("rotate", rotate_frame)
if cv2.waitKey(1) & 0xFF == ord('q'): break
cap.release()
cv2.destoryAllWindows()
def imgvector():
img = cv2.imread("/home/zk/opencvtest/opencvlearn/people.jpg")
vector = img.shape
print(vector)
#vet2 = img.resize(500,330,3)
#print(img.shape)
raw = img.flatten()
x = raw.shape
means = cv2.mean(img)
print('means ', means)
print('raw.shape ', raw.shape)
print('raw', raw)
cv2.imshow("img", img)
#cv2.imshow("means",means)
#cv2.imshow("vet2",vet2)
if cv2.waitKey(0) == 27:
cv2.destoryAllWindows()
def movTest():
vc = cv2.VideoCapture("mp4/test01.mp4")
if(vc.isOpened()):
open,frame = vc.read()#一帧一帧读取图像
else:
open = False
while open:
ret,frame = vc.read()
if(frame is None):
break
if(ret == True):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow("result",gray)
if(cv2.waitKey(1) & 0xFF == 27):
break
vc.release()
cv2.destoryAllWindows()
'''
img = cv2.imread("cctv.jpg", 0)
shape = img.shape
img = cv2.GaussianBlur(img, (3, 3), 0)
canny = cv2.Canny(img, 10, 250)
cv2.imshow('Canny', canny)
cv2.imwrite("cctv.jpg", canny)
image = cv2.imread('cctv.jpg')
height = image.shape[0]#图像的高
width = image.shape[1]#图像的宽
print height
print width
res = cv2.resize(image,(200,100), interpolation=cv2.INTER_LINEAR)
cv2.imshow('iker', res)
cv2.imshow('image', image)
cv2.waitKey(0)
cv2.destoryAllWindows()
'''
CV_INTER_NN - 最近邻插值,
CV_INTER_LINEAR - 双线性插值 (缺省使用)
CV_INTER_AREA - 使用象素关系重采样。当图像缩小时候，该方法可以避免波纹出现。当图像放大时，类似于 CV_INTER_NN 方法..
CV_INTER_CUBIC - 立方插值.
'''
def main():
"""
This is the main program. It is what detects the motion on the camera and alarms if motion is detected. The original form of this was
obtain from: http://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/
:return:
"""
alarm = False
ap = argparse.ArgumentParser()
ap.add_argument("--v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=250, help="minimum area size")
args = vars(ap.parse_args())
if args.get("video", None) is None:
camera = cv2.VideoCapture(0)
time.sleep(0.25)
else:
camera = cv2.VideoCapture(args["video"])
firstFrame = None
while True:
(grabbed, frame) = camera.read()
text = "NO MOTION DETECTED"
if not grabbed:
break
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
if firstFrame is None:
firstFrame = gray
continue
frameDelta = cv2.absdiff(firstFrame, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
if cv2.contourArea(c) < args["min_area"]:
continue
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "MOTION DETECTED"
cv2.putText(frame, "Room Status: {}".format(text), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"), (10, frame.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
cv2.imshow("Security Feed", frame)
if text == "MOTION DETECTED" and not alarm:
send_message()
winsound.Beep(2500, 1000)
alarm = True
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
camera.release()
cv2.destoryAllWindows()
def method():
# define the upper and lower boundaries for a color
# to be considered "blue"
blueLower = np.array([14,65,183], dtype = "uint8")
blueUpper = np.array([81,116,233], dtype = "uint8")
# load camera
camera = cv2.VideoCapture(0)
#keep looping
while True:
# grab the current frame
(grabbed, frame) = camera.read()
# check to see if we have reached the end of the video
if not grabbed:
break
# determine which pixels fall within the blue boundaries
# and then blur the binary image
blue = cv2.inRange(frame, blueLower, blueUpper)
blue = cv2.GaussianBlur(blue, (3, 3), 0)
# find contours in the image
(cnts, _) = cv2.findContours(blue.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# check to see if any contours were found
if len(cnts) > 0:
# sort the contours and find the largest one -- we
# will assume this contour correspondes to the area
# of object
sorted_cnts = sorted(cnts, key = cv2.contourArea, reverse = True)
displayed_rectangles = 0
for cnt in sorted_cnts:
# compute the (rotated) bounding box around then
# contour and then draw it
rect = np.int32(cv2.cv.BoxPoints(cv2.minAreaRect(cnt)))
cv2.drawContours(frame, [rect], -1, (0, 255, 0), 2)
displayed_rectangles = displayed_rectangles + 1
if displayed_rectangles >= 2:
break
# show the frame and the binary image
cv2.imshow("Tracking", frame)
cv2.imshow("Binary", blue)
# if your machine is fast, it may display the frames in
# what appears to be 'fast forward' since more than 32
# frames per second are being displayed -- a simple hack
# is just to sleep for a tiny bit in between frames;
# however, if raspberry is slow, comment out the line
time.sleep(0.025)
# if the 'q' key is pressed, stop the loop
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destoryAllWindows()
def dtmove(cap=cv2.VideoCapture(0)):
occflag = 0
motionCounter = 0
avg = None
times = 0
time.sleep(1)
for i in range(0, 50):
ret, frame = cap.read()
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
avg = cv2.GaussianBlur(gray, (21, 21), 0)
grabold = avg.copy()
diflx = avg.copy()
while True:
timestamp = datetime.datetime.now()
ret, frame = cap.read()
grab = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(grab, (21, 21), 0)
differ = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
ret, thresh = cv2.threshold(differ, 50, 255, cv2.THRESH_BINARY)
thresh = cv2.dilate(thresh, None, iterations=8)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
if cv2.contourArea(c) < 500:
continue
# 计算轮廓的边界框，在当前帧中画出该框
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
occflag = 1
# 背景重新获取
if occflag == 0:
(difavg, _, _, _) = cv2.mean(differ)
print "m", difavg
if difavg > 2:
avg = gray.copy()
else:
if times >= 30:
times = 0
diflx = cv2.absdiff(gray, cv2.convertScaleAbs(grabold))
(difzx, _, _, _) = cv2.mean(diflx)
print "d", difzx
grabold = gray.copy()
if difzx < 2:
avg = gray.copy()
times = times + 1
occflag = 0
cv2.imshow("farme", frame)
cv2.imshow("thresh", thresh)
cv2.imshow("avg", avg)
cv2.imshow("differ", differ)
key = cv2.waitKey(15) & 0xFF
if key == ord("s"):
ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
cv2.imwrite("/home/lucky_d/pyex/images/" + ts + ".jpg", frame)
print "imgsaved!as" + ts + ".jpg"
if key == ord("q"):
break
cap.release()
cv2.destoryAllWindows()
return
def destory(self):
self._cap.release()
cv2.destoryAllWindows()
