def getBarcode(showImage = False):
camera_index = 0
capture = cv.CaptureFromCAM(camera_index)
time.sleep(0.1)
frame = cv.QueryFrame(capture)
if(showImage == True):
cv.imshow('Test image', frame)
width = frame.width
height = frame.height
raw = frame.tostring()
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# create a reader
scanner = zbar.ImageScanner()
# configure the reader
scanner.parse_config('enable')
# scan the image for barcodes
scanner.scan(image)
# extract results
for symbol in image:
# do something useful with results
print 'decoded', symbol.type, 'symbol', '"%s"' % symbol.data
return image[0].data
def render_deepdreamvideo():
import cv
print("CV is working")
cap = cv.VideoCapture('SampleFile.mkv')
while (cap.isOpened()):
ret, frame = cap.read()
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
cv.imshow('frame', gray)
if cv.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv.destroyAllWindows()
def main(argv):
default_file = 'images/board.JPEG'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
print('Error opening image!')
print('Usage: hough_lines.py [image_name -- default ' + default_file +
'] \n')
return -1
dst = cv.Canny(src, 50, 200, None, 3)
# Copy edges to the images that will display the results in BGR
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
cdstP = np.copy(cdst)
lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
if lines is not None:
for i in range(0, len(lines)):
rho = lines[i][0][0]
theta = lines[i][0][1]
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (int(x0 + 1000 * (-b)), int(y0 + 1000 * (a)))
pt2 = (int(x0 - 1000 * (-b)), int(y0 - 1000 * (a)))
cv.line(cdst, pt1, pt2, (0, 0, 255), 3, cv.LINE_AA)
linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
if linesP is not None:
for i in range(0, len(linesP)):
l = linesP[i][0]
cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3,
cv.LINE_AA)
y = int(round(1536 / 3))
x = int(round(2048 / 3))
src = cv.resize(cdst, (x, y)) # Resize image
cdst = cv.resize(cdst, (x, y)) # Resize image
cdstP = cv.resize(cdstP, (x, y)) # Resize image
cv.imshow("Source", src)
cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
cv.imwrite("./houghLines.png", cdstP)
cv.waitKey()
return 0
video = cv.VideoCapture(0)
background=0
for i in range(30):
ret,background=video.read()
background=np.flip(background, axis=1m)
while True:
ret,img=video.read()
img=np.flip(img, axis=1)
hsv=cv.cvtColor(img, cv.COLOR_BGR2HSV)
blur=cv.GaussianBlur(hsv, (35,35), 0)
cv.imshow("Display", img)
lower=np.array([0,120,70])
upper=np.array([10,255,255])
mask01=cv.inRange(hsv, lower, upper)
cv.imshow("Background", background)
cv.imshow("mask01", mask01)
k=cv.waitKey(1)
if k==ord('q'):
break
video.release()
cv.destroyAllWindows()
import cv as cv
import numpy as np
img = np.zeros((512, 512, 3), np.uint8)
# 填充像素
img.fill(64)
text = "OpenCV for Python"
cv.putText(img, text, (10,40),cv.FONT_HERSHEY_SIMPLEX,\
1,(0,255,255),1,cv.LINE_AA)
cv.putText(img, text, (10,80),cv.FONT_HERSHEY_PLAIN,\
1,(0,255,255),2,cv.LINE_AA)
cv.putText(img, text, (10,120),cv.FONT_HERSHEY_DUPLEX,\
1,(0,255,255),1,cv.LINE_AA)
cv.putText(img, text, (10,160),cv.FONT_HERSHEY_COMPLEX,\
1,(0,255,255),2,cv.LINE_AA)
cv.putText(img, text, (10,200),cv.FONT_HERSHEY_TRIPLEX,\
1,(0,255,255),1,cv.LINE_AA)
cv.putText(img, text, (10,240),cv.FONT_HERSHEY_COMPLEX_SMALL,\
1,(0,255,255),2,cv.LINE_AA)
cv.putText(img, text, (10,280),cv.FONT_HERSHEY_SCRIPT_SIMPLEX,\
1,(0,255,255),1,cv.LINE_AA)
cv.putText(img, text, (10,320),cv.FONT_HERSHEY_SCRIPT_COMPLEX,\
1,(0,255,255),2,cv.LINE_AA)
cv.imshow('imgae', img)
cv.waitKey()
cv.destroyAllWindows()
import cv as cv2
# Loading the cascades
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
# Defining a function that will do the detections
def detect(gray, frame):
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray, 1.1, 3)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0),
2)
return frame
# Doing some Face Recognition with the webcam
video_capture = cv2.VideoCapture(0)
while True:
_, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
canvas = detect(gray, frame)
cv2.imshow('Video', canvas)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
import cv as cv2
import numpy as np
img = cv2.imread('opencv_logo.png', 0)
img = cv2.medianBlur(img, 5)
cimg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(img,
cv2.HOUGH_GRADIENT,
1,
20,
param1=50,
param2=30,
minRadius=0,
maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
# draw the outer circle
cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0), 2)
# draw the center of the circle
cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
cv2.imshow('detected circles', cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
import cv, cv2
img = cv.imread("cones_plus_shadowed_barrel.jpg", cv2.CV_LOAD_IMAGE_COLOR)
cv.namedWindow('Normal Image')
cv.imshow('Normal Image', img)
hsv_img = img
cv.CvtColor(img,hsv_img,cv.CV_RGB2HSV)
cv.namedWindow('HSV Image')
cv.imshow('HSV Image', hsv_img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv
import time
import numpy as np
cv_car = cv.CascadeClassifier(
r'C:\Users\DEBIPRASAD\Desktop\Projetc Work\ComputerVision-Projects-master\CarPedestrianDetection\cascades\haarcascade_car.xml'
)
capture = cv.VideoCapture(
r'C:\Users\DEBIPRASAD\Desktop\Projetc Work\ComputerVision-Projects-master\CarPedestrianDetection\files\cars.avi'
)
while capture.isOpened():
response, frame = capture.read()
if response:
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
cars = cv_car.detectMultiScale(gray, 1.2, 3)
for (x, y, w, h) in cars:
cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 0), 3)
cv.imshow('cars', frame)
if cv.waitkey(1) & 0xFF == ord('q'):
break
else:
break
capture.release()
cv.destroyAllWindows()
import cv as cv2
import numpy as np
img = cv2.imread('opencv_logo.png',0)
img = cv2.medianBlur(img,5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
param1=50,param2=30,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
# draw the center of the circle
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
cv2.imshow('detected circles',cimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
# capture frames from the camera
for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
# grab the raw NumPy array representing the image, then initialize the timestamp
# and occupied/unoccupied text
image = frame.array
gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv.CASCADE_SCALE_IMAGE
)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv.rectangle( image, (x, y), (x+w, y+h), (0, 255, 0), 2)
# show the frame
cv.imshow("Frame", image)
key = cv.waitKey(1) & 0xFF
# clear the stream in preparation for the next frame
rawCapture.truncate(0)
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
a_separate_r_log_normalize = normalize(a_separate_r_log)
# resize filters to show
a_separate_c_log_f = cv.resize(a_separate_c_log_normalize, (512, 512))
a_separate_r_log_f = cv.resize(a_separate_r_log_normalize, (512, 512))
a_log = cv.resize(a_log_normalize, (512, 512))
b_log = cv.resize(b_log_normalize, (512, 512))
c_log = cv.resize(c_log_normalize, (512, 512))
# write filters
cv.imwrite(out_path + 'a.jpg', a_log)
cv.imwrite(out_path + 'b.jpg', b_log)
cv.imwrite(out_path + 'c.jpg', c_log)
cv.imwrite(out_path + 'a_separate_c.jpg', a_separate_c_log_f)
cv.imwrite(out_path + 'a_separate_r.jpg', a_separate_r_log_f)
'''
# read filters
a_fft = cv.imread('a.jpg', 0)
b_fft = cv.imread('b.jpg', 0)
c_fft = cv.imread('c.jpg', 0)
a_separate_c_fft = cv.imread('a_separate_c.jpg', 0)
a_separate_r_fft = cv.imread('a_separate_r.jpg', 0)
# show filters
cv.imshow('a', a_fft)
cv.imshow('b', b_fft)
cv.imshow('c', c_fft)
cv.imshow('a_separate_c',a_separate_c_fft)
cv.imshow('a_separate_r',a_separate_r_fft)
'''
