def hough_circle():
print('시작')
img1 = cv2.imread(
'C:\Sources\PycharmProjects\Capstone1\data\m00021718_r1.jpg')
img2 = img1.copy()
img2 = cv2.GaussianBlur(img2, (3, 3), 0)
imgray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(imgray,
cv2.HOUGH_GRADIENT,
1,
10,
param1=70,
param2=50,
minRadius=0,
maxRadius=0)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv2.circle(img1, (i[0], i[1]), i[2], (255, 255, 0), 1)
cv2.imshow('HoughCircles', img1)
cv2.waitKey(0)
cv2.destroyAllWindow()
else:
print('원을 찾지 못했습니다.')
def get_frame_from_primary_display(x=25, y=25, width=825, height=615):
"""Get Frame From Monitor from (x, y) to (x + width, y + height)
Use ImageGrab.grab
Args:
x (int): Frame x-pos
y (int): Frame y-pos
width (int): Frame Width
height (int): Frame Height
Returns:
np.array: Monitor Screen For Input Area
"""
if DEBUG:
last_time = time.time()
while (True):
# PressKey(DirectionKey.W.value) # frame
# bbox=(x, y, width, height), 40은 TitleBar의 높이를 의미
screen = np.array(ImageGrab.grab(bbox=(x, y, width, height)))
new_screen = process_img(screen)
if DEBUG:
print('Loop took {} secdons', format(time.time() - last_time))
last_time = time.time()
cv2.imshow('OSORI-GTA5', np.array(new_screen))
# 문자를 "아스키 코드 번호"로 변환하려면 ord() 함수를 사용
if cv2.waitKey(25) & 0xFF == ord(
'q'): # waitKey(25) will display a frame for 25 ms
cv2.destroyAllWindow()
breakd
def main(): # 全ての処理を行う。
binarization()
cv2.imshow('differential', img)
elapsed_time = time.time() - start
print(elapsed_time)
cv2.waitKey()
cv2.destroyAllWindow()
def fusionsensor(WaterLevl1,WaterLev2,WaterLev3,WaterLev4,Sitdec1,Sitdec2,Motionsensor): # Fusoin sensor
# Water level and sitting toilet detection
if WaterLevel1 >= 900:
speech = Speech("Water level in tank 1 is now level 1",lang1)
speech.play(sox_effects) # Speech fully play
speech = Speech("ระดับนำ้ในถังที่ 1 ตอนนี้ ระดับที่ 1",lang2) # Thai language for the speech s$
speech.play(sox_effects) # Speech fully play
speech = Speech("水箱1中的水位現在為1級",lang3) # Chinese language speech synthesys
speech.play(sox_effects)
if WaterLevel2 >= 900:
speech = Speech("Water level in tank 1 is now full",lang1)
speech.play(sox_effects) # Speech fully play
speech = Speech("ระดับนำ้ในถังที่ 1 ตอนนี้เต็มแล้ว",lang2) # Thai language for the speech s$
speech.play(sox_effects) # Speech fully play
speech = Speech("水箱1級現已滿。",lang3) # Chinese language speech synthesys
speech.play(sox_effects)
if WaterLevel3 >= 900:
speech = Speech("Water level in tank 1 is now level ",lang1)
speech.play(sox_effects) # Speech fully play
speech = Speech("ระดับนำ้ในถังที่ 2 ตอนนี้ ระดับที่ 1",lang2) # Thai language for the speech s$
speech.play(sox_effects) # Speech fully play
speech = Speech("水箱2中的水位現在為1級",lang3) # Chinese language speech synthesys
speech.play(sox_effects)
if WaterLevel4 >= 900:
speech = Speech("Water level in tank 2 is now full",lang1)
speech.play(sox_effects) # Speech fully play
speech = Speech("ระดับนำ้ในถังที่ 2 ตอนนี้เต็มแล้ว",lang2) # Thai language for the speech s$
speech.play(sox_effects) # Speech fully play
speech = Speech("水箱2級現已滿。",lang3) # Chinese language speech synthesys
speech.play(sox_effects)
if Sitdec1 < 1023:
speech = Speech("Sitting on the closet",lang1)
speech.play(sox_effects)
speech = Speech("ตอนนี้มีคนนั่งบนส้วม",lang2)
speech.play(sox_effects)
speech = Speech("現在，有人坐在馬桶上。",lang3)
speech.play(sox_effects)
if Sitdec2 < 1023:
speech = Speech("Sitting on the closet",lang1)
speech.play(sox_effects)
speech = Speech("ตอนนี้มีคนนั่งบนส้วม",lang2)
speech.play(sox_effects)
speech = Speech("現在，有人坐在馬桶上。",lang3)
speech.play(sox_effects)
if Motionsensor == 'True': # Sensor read true state ment after retected the motion
ret,frame = cap.read() # Reading the frame from the camera picture input
cv2.imshow('Smarttoilet Cam guard',frame)
print(str(Today) + str(timedata)) # Showing today time data
cv2.imwrite(str(Today) +"/"+ str(timedata) + ".png",frame) # Time as name picture data
speech = Speech("Hello Welcome to smart toilet we keep your picture data as user identification",lang1)
speech.play(sox_effects) # Speech fully play
speech = Speech("สวัสดีค่ะ ยินดีต้อนรับสู่ห้องนำ้อัจฉริยะค่ะเราขอเก็บข้อมูลรูปภาพของคุณเพื่อใช้ในการระบุตัวผู้ใช้งาน",lang2) # Thai language for the speech synthesys output function
speech.play(sox_effects) # Speech fully play
speech = Speech("您好，歡迎來到智能浴室。我們希望收集您的圖像數據以便識別 ",lang3) #Chinese language speech synthesys
speech.play(sox_effects)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
elif Motionsensor == 'False':
cv2.destroyAllWindow() #Destroy all window after done software
def get_face_with_video():
cam = cv2.VideoCapture(0) # 调用计算机摄像头，一般默认为0
width = int(cam.get(cv2.CAP_PROP_FRAME_WIDTH) + 0.5)
height = int(cam.get(cv2.CAP_PROP_FRAME_HEIGHT) + 0.5)
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 定义编码
out = cv2.VideoWriter('output.mp4', fourcc, 20.0,
(width, height)) # 创建videowriter对象
while (cam.isOpened() is True):
ret, frame = cam.read() # 逐帧捕获
if ret is True:
# 输出当前帧
detectFace(frame)
detectFace_1(frame)
cv2.imshow('MyCamera', frame)
if (cv2.waitKey(1) & 0xFF) == ord('q'):
break
else:
break
out.release()
cam.release()
cv2.destroyAllWindow()
def generate():
face_cascade = cv2.CascadeClassifier(
'cascades/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('cascades/haarcascade_eye.xml')
camera = cv2.VideoCapture(0)
count = 0
while (True):
ret, frame = camera.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
img = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
f = cv2.resize(gray[y:y + h, x:x + w], (200, 200))
cv2.imwrite('data/lia/%s.pgm' % str(count), f)
count += 1
cv2.imshow('camera', frame)
if cv2.waitKey(1000 / 12) & 0xff == ord('q'):
break
camera.release()
cv2.destroyAllWindow()
def display():
print("Press ESC to Delete")
print("Press S to Save")
imgFile = cv2.imread('test_image.png')
cv2.imshow('image', imgFile)
key = cv2.waitKey(0)
if key == 27: cv2.destroyWindow('image')
if key == 115:cv2.destroyAllWindow()
def shoot(self):
if self.ret:
print("찰칵")
cv2.imwrite("galary/" + str(self.cnt) + ".png", self.frame)
cv2.imshow(str(self.cnt - 1) + '.png', self.frame)
k = cv2.waitKey(0)
if k == 27: # esc key
cv2.destroyAllWindow()
def main():
img1 = np.zeros((512, 512, 3), np.uint8)
cv2.line(img1, (0, 99), (99, 0), (255, 0, 0), 2)
cv2.rectangle(img1, (45, 50), (100, 345), (0, 0, 255), 20)
cv2.imshow('Lena', img1)
cv2.waitKey(0)
cv2.destroyAllWindow('Lena')
def check_vid(rtsp):
cap = cv2.VideoCapture(rtsp)
while True:
ret, frame = cap.read()
cv2.imshow('frame', frame)
k = cv2.waitKey(3) & 0xFF
if k == ord('q'):
self.calib_switch = False
cv2.destroyAllWindow()
def main():
start = time.time()
bilateral()
end = time.time() - start
print(end)
print(img)
print(img2)
cv2.imshow("bilateral", img)
cv2.imshow("gray", img2)
cv2.waitKey()
cv2.destroyAllWindow()
def __build__(self):
count = 0
data_set = []
label_set = []
train_data = []
assert filename, "filename is missing"
while (True):
count += 1
warm_up = 30
if count % 10 == 0:
print(count)
time = cv2.getTickCount()
printscreen = np.array(ImageGrab.grab(bbox=(0, 400, 1000, 1200)))
printscreen = cv2.cvtColor(printscreen, cv2.COLOR_BGR2RGB)
printscreen = cv2.resize(printscreen, (self.width, self.height))
# print(printscreen.shape)
cv2.imshow("window", printscreen)
# Save images
Save_Image.create_save_image(count, printscreen, warm_up)
# Save .npy or .pickle (-1, 224, 224, 3)
printscreen = np.array(printscreen).reshape(
self.width, self.height, 3)
if count <= part_1 + warm_up and count > warm_up: # 30 530
tower = 1
data_set.append(printscreen)
label_set.append(tower)
elif count > part_1 + (2 * warm_up) and count <= part_2 + (
2 * warm_up): # 560 1060
minion = 2
data_set.append(printscreen)
label_set.append(minion)
elif count > part_2 + (3 * warm_up) and count <= part_3 + (
3 * warm_up): # 1090 1590
ezreal = 3
data_set.append(printscreen)
label_set.append(ezreal)
elif count >= part_3 + (3 * warm_up): #1590
train_data.append([data_set, label_set])
train_data = np.array(train_data)
np.save("data/" + filename, train_data)
print("LOL_Data.npy saved",
"shape is {}".format(train_data.shape))
break
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindow()
break
def get_pic_x_y(): # 运行这个函数，上面那个被这个调用
cv2.namedWindow("image")
cv2.setMouseCallback("image", on_mouse)
cv2.imshow("image", img1)
while (True):
try:
cv2.waitKey(100)
except Exception:
cv2.destroyWindow("image")
break
cv2.waitKey(100)
cv2.destroyAllWindow()
def main():
#--- 画像入力処理 ---#
print("Please input picture")
input_picture = raw_input('>>> ')
input_img = cv2.imread(input_picture, 0)
lbp_img = copy.deepcopy(input_img)
img_hor = len(input_img)
img_ver = len(input_img[0])
histgram = [0 for col in range(256)]
points = 8
radius = 1
#--- LBP処理部分 ---#
for i in range(0, img_hor):
for j in range(0, img_ver):
# reset array
mask = [0 for col in range(points)]
lbp_img[i][j] = 2**8 - 1
# when ( left / right / top / bottom ) exist, calc LBP
if i > radius and i + radius < img_ver and j > radius and j + radius < img_hor:
#--- LBP算出部分 ---#
for part in range(0, points):
# measure distance between [j][j] and its neighborhoods
x_len = int(radius * math.cos(360 / points * part) + 0.5)
y_len = int(radius * math.sin(360 / points * part) + 0.5)
# get mask pattern -正負判定はsign()でもok-
if input_img[i + x_len][j + y_len] - input_img[i][j] > 0:
mask[part] = 1
# loop time to find min value
print(mask)
for k in range(0, points):
lbp_val = 0
for x in range(0 + k, points):
lbp_val += mask[x] * (2**(x - k))
for y in range(0, k):
lbp_val += mask[y] * (2**(y + (points - k)))
lbp_img[i][j] = min(lbp_img[i][j], lbp_val)
print(lbp_img[i][j])
#--- ヒストグラム作成 ---#
for i in range(0, img_hor):
for j in range(0, img_ver):
histgram[lbp_img[i][j]] += 1
#--- 結果表示部分 ---#
cv2.imshow("Local Binary Pattern", lbp_img)
# when input key was 's', save result image or fin
if cv2.waitKey(0) == ord('s'):
cv2.imwrite("LBP.png", lbp_img)
cv2.destroyAllWindow()
def thresh_binary():
image = cv2.imread("/home/zk/opencvtest/opencvlearn/image/dog.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
t, thresh1 = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)
t, thresh2 = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
t, thresh3 = cv2.threshold(gray, 128, 255, cv2.THRESH_TRUNC)
t, thresh4 = cv2.threshold(gray, 128, 255, cv2.THRESH_TOZERO)
t, thresh5 = cv2.threshold(gray, 128, 255, cv2.THRESH_TOZERO_INV)
cv2.imshow('Thresh_binary', thresh1)
cv2.imshow('Thresh_binary_inv', thresh2)
cv2.imshow('Thresh_trunc', thresh3)
cv2.imshow('Thresh_tozero', thresh4)
#cv2.imshow('Thresh_tozero_inv',thresh5)
cv2.imshow('img', image)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindow()
def screenRecorder():
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter("output.avi", fourcc, 5.0, (1366, 768))
while True:
img = ImageGrab.grab()
img_np = np.array(27)
frame = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRB)
cv2.imshow("SCreen Recorder", frame)
out.write(frame)
if cv2.waitKey(1) == 27:
break
out.release()
cv2.destroyAllWindow()
def savevideo(name):
cap = cv.VideoCapture(0)
fourcc = cv.VideoWriter_fourcc(*'XVID')
out = cv.VideoWriter(name, 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(20) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv.destroyAllWindow()
def tracking():
img1 = cv2.imread('./data/test/ruptured/mark/m03913745_1.jpg')
hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
lower_white = np.array([0, 0, 99])
upper_white = np.array([0, 0, 100])
mask_white = cv2.inRange(hsv, lower_white, upper_white)
res = cv2.bitwise_and(img1, img1, mask=mask_white)
cv2.imshow('original', img1)
cv2.imshow('WHITE', res)
cv2.waitKey(0)
cv2.destroyAllWindow()
def binary():
img = cv2.imread('images.jpg',
0) #dd.jpg dalam format grayscale(parameter kedua)
a, thresh = cv2.threshold(img, 125, 255, cv2.THRESH_BINARY)
b, thresh_inv = cv2.threshold(img, 125, 255, cv2.THRESH_BINARY_INV)
c, thresh_trunch = cv2.threshold(img, 125, 255, cv2.THRESH_TRUNC)
d, thresh_tozero = cv2.threshold(img, 125, 255, cv2.THRESH_TOZERO)
e, thresh_tozero_inv = cv2.threshold(img, 125, 255, cv2.THRESH_TOZERO_INV)
cv2.imshow('threshold', thresh)
cv2.imshow('thresh_inv', thresh_inv)
cv2.imshow('thresh_trunch', thresh_trunch)
cv2.imshow('thresh_tozero', thresh_tozero)
cv2.imshow('thresh_tozero_inv', thresh_tozero_inv)
cv2.imshow('original', img)
cv2.waitKey(0)
cv2.destroyAllWindow()
def main(): # 全ての処理を行う。
if __name__ == "__main__":
start = time.time()
smoothing_color()
end = time.time() - start
print(end)
gray = False
if gray == True:
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imshow('average', gray_img)
cv2.waitKey()
cv2.destroyAllWindow()
else:
cv2.imshow('average', img)
cv2.waitKey()
cv2.destroyAllWindow()
def facerun(request):
cascPath = "/Users/sahilsagar/Desktop/ML/Project/Snapchat/face.xml"
face=cv2.CascadeClassifier(cascPath)
cam=cv2.VideoCapture(0)
while True:
check,frame=cam.read()
faces=face.detectMultiScale(frame,1.3,5)
for x,y,w,h in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,255,220),2)
cv2.imshow("face",frame)
key=cv2.waitKey(1)
if key==ord('q'):
break
cam.release()
cv2.destroyAllWindow()
return redirect('/')
def main(): # 全ての処理を行う。
start = time.time()
smoothing()
end = time.time()
gray = False
print(end - start)
if gray == True:
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
cv2.imshow('gauss', gray_img)
cv2.waitKey()
cv2.destroyAllWindow()
else:
cv2.imshow('gauss', img)
cv2.waitKey()
cv2.destroyAllWindow()
def video():
#cap = cv2.VideoCapture(0) #webcam
cap = cv2.VideoCapture('tes.mp4') #play dari file
while (True):
ret, frame = cap.read()
gray = cv2.cvtColor(frame,
cv2.COLOR_BGR2GRAY) #RGB diubah ke GrayScale
#cv2.imwrite('tangkapRGB.png', frame) #img disimpan
#cv2.imwrite('tangkapGRAY.png', gray)
#cv2.imshow("Hasil capture", gray)
cv2.imshow("Hasil capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindow()
def main():
#WindowName="live"
#cv2.namedWindow(WindowName)
cap = cv2.VideoCapture(1)
if cap.isOpened():
ret, frame = cap.read()
else:
ret = False
while ret:
ret, frame = cap.read()
#output = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
#frame = imutils.resize(frame, width=600)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, greenLower, greenUpper)
mask = cv2.erode(mask, None, iterations=2)
mask = cv2.dilate(mask, None, iterations=2)
cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
center = None
if len(cnts) > 0:
c = max(cnts, key=cv2.contourArea)
((x, y), radius) = cv2.minEnclosingCircle(c)
if x < 300:
print("move left " + str(-1 * (x - 300)) + " spaces")
else:
print("move right " + str(-1 * (300 - x)) + " spaces")
cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)
#cv2.imshow(WindowName,output)
cv2.imshow("color original", frame)
cv2.imshow("Mask", mask)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindow()
cap.release()
def imgBlending(imgfile1, imgfile2):
img1 = cv2.imread(imgfile1)
img2 = cv2.imread(imgfile2)
cv2.namedWindow('imgPane')
cv2.createTrackbar('MIXING', 'imgPane', 0, 100, onMouse)
mix = cv2.getTrackbarPos('MIXING', 'imgPane')
while True:
img = cv2.addWeighted(img1,
float(100 - mix) / 100, img2,
float(mix) / 100, 0)
cv2.imshow('imgPane', img)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
mix = cv2.getTrackbarPos('MIXING', 'imgPane')
cv2.destroyAllWindow()
def detect_camera(cfgfile, weightfile, imgfile):
m = Darknet(cfgfile)
m.print_network()
m.load_weights(weightfile)
print('Loading weights from %s... Done!' % (weightfile))
num_classes = 80
if num_classes == 20:
namesfile = 'data/voc.names'
elif num_classes == 80:
namesfile = 'data/coco.names'
else:
namesfile = 'data/names'
use_cuda = 0
if use_cuda:
m.cuda()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
cv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(cv_img)
sized = pil_img.resize((m.width, m.height))
for i in range(2):
start = time.time()
boxes = do_detect(m, sized, 0.5, 0.4, use_cuda)
finish = time.time()
if i == 1:
print('Predicted in %f seconds.' % (finish - start))
class_names = load_class_names(namesfile)
pil_img = plot_boxes(pil_img, boxes, None, class_names)
img = cv2.cvtColor(numpy.asarray(pil_img), cv2.COLOR_RGB2BGR)
cv2.imshow("OpenCV", img)
if cv2.waitKey(1) & 0xff == ord('q'):
break
cv2.destroyAllWindow()
def video_save_webcam():
cap = cv2.VideoCapture(0)
fourcc = cv2.cv.CV_FOURCC(
*'DIVX'
) #cv2.VideoWriter(['namafile', [nilai fourcc], [nilai fps], [nilai lebar,panjang]])
out = cv2.VideoWriter('output.avi', fourcc, 5, (640, 480))
raw_input('Tekan enter untuk mulai esc untuk saving dan keluar')
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
frame = cv2.flip(frame, 180) #mengatur drajat
out.write(frame)
cv2.imshow("Hasil capture", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
cv2.destroyAllWindow()
def main():
path = "/home/g_r00t/PRIYANK/ex_images/"
imgpath1 = path + "4.2.01.tiff"
img = cv2.imread(imgpath1)
rows, columns, channels = img.shape
angle = 0
while True:
if angle == 360:
angle = 0
R1 = cv2.getRotationMatrix2D((columns / 2, rows / 2), angle, 1)
print(R1)
output = cv2.warpAffine(img, R1, (columns, rows))
cv2.imshow('Rotation Image', output)
angle = angle + 1
time.sleep(0.01)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindow()
def main(): # 全ての処理を行う。
final()
cv2.imshow('differential', img)
cv2.waitKey()
cv2.destroyAllWindow()
if sorted_ctrs == []:
img_last_class = ['nothing']
for j, ctr in enumerate(sorted_ctrs):
x, y, w, h = cv2.boundingRect(ctr)
if w > 35 and h > 40 and j < 2:
# img = cv2.cvtColor(img_or, cv2.COLOR_BGR2GRAY)
img = img_or[y:y + h, x:x + w, :]
img = cv2.resize(img, (100, 250))
img = img.reshape(1, 100, 250, 3)
classes = model.predict(img)
class_index = np.where(classes > 0.5)[1][0]
image_class = img_class(class_index)
if image_class != img_last_class:
class_count[class_index] = class_count[class_index] + 1
# if sum(sum(sum(img_or[y:y+h,x:x+w,:]-cv2.resize(img_last, (w, h)) )))>100:
cv2.rectangle(img_or, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img_or, image_class, (x, y + 20), font, 0.8,
(0, 255, 0), 2, cv2.LINE_AA)
img_last_class = image_class
# images2=np.hstack((img_or,cv2.cvtColor(img_diff, cv2.COLOR_GRAY2BGR)))
images2 = np.hstack(
(img_or, print_text(class_count.astype(int), np.copy(blank_img))))
cv2.imshow(winName, images2)
i = i + 1
key = cv2.waitKey(1) & 0xFF
if key == 27: # comment this 'if' to hide window
cv2.destroyAllWindow(winName)
break
cv2.waitKey(0)
import cv2
img = cv2.imread("sample2.jpg")
grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imwrite("GrayImage.jpg", grayImg)
cv2.imshow("OriginalImage", img)
cv2.imshow("GrayScale Image", grayImg)
cv2.waitKey(0)
cv2.destroyAllWindow()
import numpy as np def sum_rgb(src): b,g,r = cv2.split(src) s = np.zeros(src.shape,dtype=np.uint8) dst = np.zeros(src.shape, dtype=np.uint8) s = cv2.addWeighted(r, 1./3., g, 1./3., 0.0) s = cv2.addWeighted(s, 2./3., b, 1./3., 0.0) ret, dst = cv2.threshold(s, 100, 100, cv2.THRESH_TRUNC) return dst if __name__ == '__main__': namedwindow = "example" ll = './image/OpticalFlow0.jpg' cv2.namedWindow(namedwindow,1) src = cv2.imread(ll) dst = sum_rgb(src) cv2.imshow(namedwindow,dst) while True: c = cv2.waitKey(10) if c == 27: break cv2.destroyAllWindow()