def getCommand():
print("getCommand running ...")
cv.wait(3)
global line_image
global prev_left
global prev_right
# calculate left and right line
image_bnw = cam.get_image('bnw',BNW_THRESHOLD)
# image_bnw = cam.get_image('canny')
line_list = find_all_lines(image_bnw)
# in case no lines found use previous lines
if (not line_list):
print('No lines detected - using previous Lines!')
line_list = [prev_left, prev_right]
# get new lines - return format [[left offset, alpha][right offset, alpha]]
lines_LR = find_lines_LR(line_list,prev_left,prev_right,DIV_ITER)
# update lines and draw them in global line_image
image_color = cam.get_image('color')
# if current lines are detected use them ...
# left line
if(len(lines_LR[0]) == 2):
prev_left = [float(i) for i in lines_LR[0]]
draw_lines(image_color,prev_left)
# ... or if no lines use prev ...
# else:
# lines_LR[0] = prev_left;
# righ tline
if(len(lines_LR[1]) == 2):
prev_right = [float(i) for i in lines_LR[1]]
draw_lines(image_color, prev_right)
# ... or if no lines use prev ...
# else:
# lines_LR[1] = prev_right;
# or write prev_left and prev_right into lines_LR
# pref_left & prev_right beinhalten hier die Informationen mit denen
# weitergearbeitet wird, also schreibe
# write camera-image with drawed lines to global variable
line_image = image_color
# Annahme: Hier sind die Werte rho und theat
print('lines_LR: LEFT: ' + str(lines_LR[0]) + ' RIGHT: ' + str(lines_LR[1]))
vanishing_point = get_vp(lines_LR)
print('getCommand returns: ' + str(vanishing_point))
return vanishing_point
def find_marker(self, image_msg):
if not self.active:
return
now = rospy.Time.now()
if now - self.last_stamp < self.publish_duration:
return
else:
self.last_stamp = now
try:
self.currentImg = self.bridge.compressed_imgmsg_to_cv2(
image_msg, "bgr8")
except CvBridgeError as e:
print e
gray = cv2.cvtColor(currentImg, cv2.COLOR_BGR2GRAY)
detection, corners = cv2.findCirclesGrid(gray, (7, 3))
copy = currentImg.copy()
if detection:
cv2.drawChessboardCorners(copy, (7, 3), corners, detection)
twoone = []
for i in range(0, 21):
point = [corners[i][0][0], corners[i][0][1]]
twoone.append(point)
twoone = np.array(twoone)
self.originalmatrix()
detection, rvec, tvec = self.gradient(twoone)
distance(copy, tvec)
cv2.imshow('detection', copy)
cv2.wait(1)
def run(self):
log_lib.debug("Starting ...")
start_time = time.time()
while not self.stop_event.is_set():
try:
log_lib.debug(
"Trying to pull data from Queue. Current Queue size {}".
format(queue.qsize()))
if queue.qsize() == 0:
time.sleep(sleeping_time)
continue
frame_data = queue.get(block=True)
log_lib.debug(
"Pulled a frame of size of {} B from Queue. Current Queue size {}"
.format(len(frame_data), queue.qsize()))
frame = cv2.imdecode(np.fromstring(frame_data, dtype=np.uint8),
-1)
cv2.imshow('Window', frame)
cv2.wait(1)
#time.sleep(sleeping_time)
except Exception as e:
log_lib.fatal(str(e))
break
try:
cv2.destroyAllWindows()
except:
pass
elapsed_time = time.time() - start_time
log_lib.info("Completed in %s" %
utils_lib.elapsed_time_string(elapsed_time))
import cv2
import numpy as np
import time
cap = cv2.VideoCapture(0)
i=0
while(i==0):
#i=1+1
ret,image = cap.read()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray=cv2.medianBlur(gray,5)
#output = image.copy()
cv2.imshow("output", image)
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT,1,20,80,60,20,20)
#print(circles)
if circles is not None:
circles = np.int32(circles[0, :])
print(circles)
if [80] in circles: continue
for [x, y, r] in circles:
cv2.circle(image, (x, y), r, (0, 255, 0), 1)
cv2.imshow("output", image)
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cv2.wait(1)
cv2.destroyAllWindows()
def show_img(img, name='default'):
cv2.imshow(name, img)
cv2.wait()
import cv2 as cv
img = cv.imread('Photos/user.jpg')
cv.imshow('User', img)
cv.wait()
import cv2
#reading the image from te same dir where the project is and at a same time changing it to grayscale.
img = cv2.imread("cat.jpg",0)
#showing the output with an output window name output and passing the img
cv2.imshow("output",img)
#use to wait till any interruot is given through key
cv2.waitKey(0)
#this statement will create a new image with format of png
cv2.imwrite("qwerty.png",img)
#crerate a black white inage and will chnge the rbg value from 127 to 255 in 1 and rest in 0
ret,bw = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
cv2.imshow("blackWhite",bw)
cv2.wait(0)
#destroy all the windows bedore exiting from the program
cv2.destroyAllWindows()
for files in TraningFiles:
file = os.listdir("TrainingData" + "/" + files)
for Image in file:
IMAGE = face_recognition.load_image_file(
"TrainingData" + "/" +
TraningFiles[TraningFiles.index(files)] + "/" + Image)
try:
image = cv2.imread(
"TrainingData" + "/" +
TraningFiles[TraningFiles.index(files)] + "/" + Image, 0)
(top, right, bottom,
left) = face_recognition.face_locations(IMAGE)
cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255),
2)
cv2.imshow('Training Data', image)
cv2.wait(500)
TrainingData.append(face_recognition.face_encodings(IMAGE)[0])
labels.append(TraningFiles[TraningFiles.index(files)])
cv2.destroyAllWindows()
except:
print(
"I wasn't able to locate any faces in at least one of the images. Check the image files: "
+ "TrainingData" + "/" +
TraningFiles[TraningFiles.index(files)] + "/" + Image)
trainedfile = open('TrainingData.pkl', 'wb')
Labelfile = open('LabelData.pkl', 'wb')
pickle.dump(TrainingData, trainedfile)
pickle.dump(labels, Labelfile)
else:
trainedfile = open('TrainingData.pkl', 'rb')
Labelfile = open('LabelData.pkl', 'rb')
thickness=1)
cv.polylines(img2, [np.int32(res2)],
isClosed=True,
color=(0, 0, 255),
thickness=1)
cv.polylines(img3, [np.int32(res3)],
isClosed=True,
color=(0, 0, 255),
thickness=1)
cv.polylines(img4, [np.int32(res4)],
isClosed=True,
color=(0, 0, 255),
thickness=1)
cv.imshow("image", img0)
cv.wait(0)
cv.imshow("image", img1)
cv.waitKey(0)
cv.imshow("image", img2)
cv.waitKey(0)
cv.imshow("image", img3)
cv.waitKey(0)
cv.imshow("image", img4)
cv.waitKey(0)
img5 = np.zeros((640, 640, 3), np.uint8)
img5.fill(255)