def found_face(self):
# global frame_copy
if (not self.camera_is_on()) or (not self.find_face_is_on()):
return False
self.flushCameraBuffer() # this reduces the frame delay
frame = cv.QueryFrame(self.capture)
if frame is None:
self.close_camera()
return False
if not frame:
cv.WaitKey(0)
if not self.frame_copy:
self.frame_copy = cv.CreateImage((frame.width, frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, self.frame_copy)
else:
cv.Flip(frame, self.frame_copy, 0)
if self.showVideo:
result = self.detect_and_draw(self.frame_copy)
else:
result = self.detect_no_draw(self.frame_copy)
cv.WaitKey(10)
return result
def scanner_procces(frame, set_zbar):
set_width = 100.0 / 100
set_height = 90.0 / 100
coord_x = int(frame.width * (1 - set_width) / 2)
coord_y = int(frame.height * (1 - set_height) / 2)
width = int(frame.width * set_width)
height = int(frame.height * set_height)
get_sub = cv.GetSubRect(frame,
(coord_x + 1, coord_y + 1, width - 1, height - 1))
cv.Rectangle(frame, (coord_x, coord_y),
(coord_x + width, coord_y + height), (255, 0, 0))
cm_im = cv.CreateImage((get_sub.width, get_sub.height), cv.IPL_DEPTH_8U, 1)
cv.ConvertImage(get_sub, cm_im)
image = zbar.Image(cm_im.width, cm_im.height, 'Y800', cm_im.tostring())
set_zbar.scan(image)
for symbol in image:
print '\033[1;32mResult : %s symbol "%s" \033[1;m' % (symbol.type,
symbol.data)
cv.ShowImage("webcame", frame)
#cv.ShowImage("webcame2", get_sub)
cv.WaitKey(10)
def run(self):
started = time.time()
while True:
currentframe = cv.QueryFrame(self.capture)
instant = time.time() #Get timestamp o the frame
self.processImage(currentframe) #Process the image
if not self.isRecording:
if self.somethingHasMoved():
self.trigger_time = instant #Update the trigger_time
if instant > started + 10: #Wait 5 second after the webcam start for luminosity adjusting etc..
print "Something is moving !"
if self.doRecord: #set isRecording=True only if we record a video
self.isRecording = True
cv.DrawContours(currentframe, self.currentcontours,
(0, 0, 255), (0, 255, 0), 1, 2, cv.CV_FILLED)
else:
if instant >= self.trigger_time + 10: #Record during 10 seconds
print "Stop recording"
self.isRecording = False
else:
cv.PutText(currentframe,
datetime.now().strftime("%b %d, %H:%M:%S"),
(25, 30), self.font, 0) #Put date on the frame
cv.WriteFrame(self.writer, currentframe) #Write the frame
if self.show:
cv.ShowImage("Image", currentframe)
c = cv.WaitKey(1) % 0x100
if c == 27 or c == 10: #Break if user enters 'Esc'.
break
def pc_and_tape(self):
# Execute vision code
cap = cv.VideoCapture(0)
# Initialize detectors
pc = PC()
while True:
ret, frame = cap.read()
last = 0
c_names, c_values = pc.find_cubes_with_contours(frame)
t_names, t_values = pc.find_cubes_with_contours(frame)
self.send(t_names, t_values)
# Keep old coordinates when cube is lost
if c_values[0] == 0:
self.send(c_names, [last])
else:
last = c_values[0]
self.send(c_names, c_values)
if cv2.WaitKey(1) == 27:
break
cap.release()
cv2.destroyAllWindows()
def grab_images(video_file, frame_inc=100, delay=100):
"""
Walks through the entire video and save image for each increment
"""
my_video = init_video(video_file)
if my_video != None:
# Display the video and save evry increment frames
cpt = 0
img = cv2.QueryFrame(my_video)
if img != None:
cv2.NamedWindow("Vid", cv2.CV_WINDOW_AUTOSIZE)
else:
return None
nFrames = int(
cv2.GetCaptureProperty(my_video, cv2.CV_CAP_PROP_FRAME_COUNT))
while cpt < nFrames:
for ii in range(frame_inc):
img = cv2.QueryFrame(my_video)
cpt += 1
cv2.ShowImage("Vid", img)
out_name = "" + str(cpt) + ".jpg"
cv2.SaveImage(out_name, img)
print out_name, str(nFrames)
cv2.WaitKey(delay)
else:
return None
def run(self):
started = time.time()
while True:
curframe = cv.QueryFrame(self.capture)
instant = time.time() #Get timestamp o the frame
self.processImage(curframe) #Process the image
if not self.isRecording:
if self.somethingHasMoved():
self.trigger_time = instant #Update the trigger_time
if instant > started + 5: #Wait 5 second after the webcam start for luminosity adjusting etc..
print("Something is moving !")
if self.doRecord: #set isRecording=True only if we record a video
self.isRecording = True
else:
if instant >= self.trigger_time + 10: #Record during 10 seconds
print("Stop recording")
self.isRecording = False
else:
cv.PutText(curframe,
datetime.now().strftime("%b %d, %H:%M:%S"),
(25, 30), self.font, 0) #Put date on the frame
cv.WriteFrame(self.writer, curframe) #Write the frame
if self.show:
cv.ShowImage("Image", curframe)
cv.ShowImage("Res", self.res)
cv.Copy(self.frame2gray, self.frame1gray)
c = cv.WaitKey(1)
if c == 27 or c == 1048603: #Break if user enters 'Esc'.
break
def display_video(my_video, frame_inc=100, delay=100):
"""
Displays frames of the video in a dumb way.
Used to see if everything is working fine
my_video = cv2Capture object
frame_inc = Nmber of increments between each frame displayed
delay = time delay between each image
"""
cpt = 0
img = cv2.QueryFrame(my_video)
if img != None:
cv2.NamedWindow("Vid", cv2.CV_WINDOW_AUTOSIZE)
else:
return None
nFrames = int(cv2.GetCaptureProperty(my_video,
cv2.CV_CAP_PROP_FRAME_COUNT))
while cpt < nFrames:
for ii in range(frame_inc):
img = cv2.QueryFrame(my_video)
cpt + 1
cv2.ShowImage("Vid", img)
cv2.WaitKey(delay)
def display_img(img, delay=1000):
"""
One liner that displays the given image on screen
"""
cv2.NamedWindow("Vid", cv2.CV_WINDOW_AUTOSIZE)
cv2.ShowImage("Vid", img)
cv2.WaitKey(delay)
def show(area):
cv2.Rectangle(img, (area[0][0], area[0][1]),
(area[0][0] + area[0][2], area[0][1] + area[0][3]),
(255, 0, 0), 2)
cv2.NamedWindow('Face Detection', cv2.CV_WINDOW_NORMAL)
cv2.imread('Face Detection', img)
cv2.WaitKey()
def draw_model_fitter(f):
cv.NamedWindow("Model Fitter", cv.CV_WINDOW_AUTOSIZE)
# Copy image
i = cv.CreateImage(cv.GetSize(f.image), f.image.depth, 3)
cv.Copy(f.image, i)
for pt_num, pt in enumerate(f.shape.pts):
# Draw normals
cv.Circle(i, (int(pt.x), int(pt.y)), 2, (0,0,0), -1)
cv.ShowImage("Shape Model",i)
cv.WaitKey()
class cvBridgeDemo():
def __init__(self):
self.node_name = "cv_bridge_demo"
rospy.init_node(self.node_name)
# What we do during shutdown
rospy.on_shutdown(self.cleanup)
# Create the OpenCV display window for the RGB image
self.cv_window_name = self.node_name
cv.NamedWindow(self.cv_window_name, cv.CV_WINDOW_NORMAL)
cv.MoveWindow(self.cv_window_name, 25, 75)
# And one for the depth image
cv.NamedWindow("Depth Image", cv.CV_WINDOW_NORMAL)
cv.MoveWindow("Depth Image", 25, 350)
# Create the cv_bridge object
self.bridge = CvBridge()
# Subscribe to the camera image and depth topics and set
# the appropriate callbacks
self.image_sub = rospy.Subscriber("/camera/rgb/image_raw", Image,
self.image_callback)
self.depth_sub = rospy.Subscriber("/camera/depth/image_raw", Image,
self.depth_callback)
rospy.loginfo("Waiting for image topics...")
def image_callback(self, ros_image):
# Use cv_bridge() to convert the ROS image to OpenCV format
try:
frame = self.bridge.imgmsg_to_cv(ros_image, "bgr8")
except CvBridgeError, e:
print e
# Convert the image to a Numpy array since most cv2 functions
# require Numpy arrays.
frame = np.array(frame, dtype=np.uint8)
# Process the frame using the process_image() function
display_image = self.process_image(frame)
# Display the image.
cv2.imshow(self.node_name, display_image)
# Process any keyboard commands
self.keystroke = cv.WaitKey(5)
if 32 <= self.keystroke and self.keystroke < 128:
cc = chr(self.keystroke).lower()
if cc == 'q':
# The user has press the q key, so exit
rospy.signal_shutdown("User hit q key to quit.")
def display_rgb(dev, data, timestamp):
global keep_running
cv.Image = frame_convert.video_cv(data)
img = cv.CreateImage(cv.GetSize(cv.Image), cv.IPL_DEPTH_16S, 3)
cv.ShowImage('RGB', cv.Image)
for x in range(1, 5):
name = "img%d" % (x)
cv.SaveImage('name.png', cv.Image)
time.sleep(1)
if cv.WaitKey(10) == 27:
keep_running = False
def repeat():
global capture
global camera_index
global count
frame = cv2.GetMat(cv2.QueryFrame(capture))
framegray = cv2.CreateMat(480, 640, cv2.CV_8UC1)
cv2.CvtColor(frame, framegray, cv2.CV_BGR2GRAY)
sys.stdout.write(framegray.tostring())
c = cv2.WaitKey(1)
if c == 27:
print(count)
sys.exit()
def show_images(images):
""" Shows all images in a window"""
if images == None:
logging.error(
'Cannot Show Images (No image saved). Image-Type: %s (tools.py)' %
str(type(images).__name__))
elif type(images).__name__ == 'list':
for i in range(len(images)):
print type(images[i])
if type(images[i]).__name__ == 'ndarray':
tmpimage = []
tmpimage[i] = array2cv(images[i])
cv.ShowImage("Image", tmpimage[i])
if cv.WaitKey() == 27:
cv.DestroyWindow("Image")
else:
cv.ShowImage("Image", images[i])
if cv.WaitKey() == 27:
cv.DestroyWindow("Image")
elif type(images).__name__ == 'cvmat':
cv.ShowImage("Image", images)
if cv.WaitKey() == 27:
cv.DestroyWindow("Image")
elif type(images).__name__ == 'iplimage':
cv.ShowImage("Image", images)
if cv.WaitKey() == 27:
cv.DestroyWindow("Image")
elif type(images).__name__ == 'ndarray':
images = array2cv(images)
cv.ShowImage("Image", images)
if cv.WaitKey() == 27:
cv.DestroyWindow("test")
elif type(images).__name__ == 'str':
logging.error(
'TypeError: Cannot Show Images (No image saved?). Image-Type: %s (tools.py)'
% str(type(images).__name__))
else:
logging.error(
'TypeError: Cannot Show Images. Image-Type: %s (tools.py)' %
str(type(images).__name__))
def run(self):
while True:
img = self.capture.read()
#blur the source image to reduce color noise
cv2.blur(img, img, 3)
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv2.CreateImage(cv2.GetSize(img), 8, 3)
cv2.CvtColor(img, hsv_img, cv2.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
greenLower = (20, 190, 165)
greenUpper = (30, 225, 220)
thresholded_img = cv2.CreateImage(cv2.GetSize(hsv_img), 8, 1)
cv2.InRangeS(hsv_img, greenLower, greenUpper, thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
moments = cv2.Moments(thresholded_img, 0)
area = cv2.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 100000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv2.GetSpatialMoment(moments, 1, 0) / area
y = cv2.GetSpatialMoment(moments, 0, 1) / area
#print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area)
#create an overlay to mark the center of the tracked object
overlay = cv2.CreateImage(cv2.GetSize(img), 8, 3)
cv2.Circle(overlay, (x, y), 2, (255, 255, 255), 20)
cv2.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
cv2.Merge(thresholded_img, None, None, None, img)
#display the image
cv2.ShowImage(color_tracker_window, img)
if cv2.WaitKey(10) == 27:
break
def show_modes_of_variation(model, mode):
# Get the limits of the animation
start = -2*math.sqrt(model.evals[mode])
stop = -start
step = (stop - start) / 100
b_all = np.zeros(model.modes)
b = start
while True:
b_all[mode] = b
s = model.generate_example(b_all)
ShapeViewer.show_shapes([s])
# Reverse direction when we get to the end to keep it running
if (b < start and step < 0) or (b > stop and step > 0):
step = -step
b += step
c = cv.WaitKey(10)
if chr(255&c) == 'q': break
def analyze_webcam(width, height):
print("""
' ' : extract colors of detected face
'b' : toggle onlyBlackCubes
'd' : toggle dodetection
'm' : shift right
'n' : shift left
'r' : reset everything
'q' : print hsvs
'p' : resolve colors
'u' : toggle didassignments
's' : save image
""")
# 0 for laptop camera
# 1 for usb camera
capture = cv2.VideoCapture(0)
# Set the capture resolution
#cv2.SetCaptureProperty(capture, cv2.CV_CAP_PROP_FRAME_WIDTH, width)
#cv2.SetCaptureProperty(capture, cv2.CV_CAP_PROP_FRAME_HEIGHT, height)
# Create the window and set the size to match the capture resolution
cv2.namedWindow("Fig", cv2.WINDOW_NORMAL)
#cv2.ResizeWindow("Fig", width, height)
# Capture frame-by-frame
ret, frame = capture.read()
rf = RubiksFinder(width, height)
while True:
ret, frame = capture.read()
if not frame:
cv2.WaitKey(0)
break
rf.analyze_frame(frame)
if not rf.process_keyboard_input():
break
cv2.DestroyWindow("Fig")
def onNextFrame(self,e):
frPrStart = time.time()
frameImg=[]
frameImg.append(cFrame[:,:,0])#0
#print [tObj.kID for tObj in tObjList.itervalues()]
curr_frame_num_write = GlProp.vidstream.get(cv.CAP_PROP_POS_FRAMES)
ret,frame=GetGSFrame(GlProp.vidstream) #read() method advances the frame index by 1
frameImg.append(frame)#1
if len(GlProp.vidfile)>0:
setpos=GlProp.vidstream.set(cv.CAP_PROP_POS_FRAMES,GlProp.fIdx+GlProp.fDir)
GlProp.fIdx = GlProp.vidstream.get(cv.CAP_PROP_POS_FRAMES)
else:
setpos=True
if ret & setpos & (GlProp.fIdx+GlProp.fDir > -1) & (GlProp.fIdx+GlProp.fDir < GlProp.vidstream.get(cv.CAP_PROP_FRAME_COUNT)):
dFrame=CalculateBGDiff(cFrame[:,:,0],frame,GlProp.trckThrsh,GlProp.TrckParameter)
frameImg.append(dFrame)#2
CurContours = updateTracking(curr_frame_num_write,dFrame,minSize=GlProp.szThrsh,minDist=GlProp.mDist,currObjs=tObjList,trackData=GlProp.trackData)
if GlProp.fIdx % GlProp.bgRatio == 0: #only update background if the frame # is a multiple of the bgRatio
cFrame[:,:,0] = BGUpdateFnc(cFrame[:,:,0],frame,tObjList,CurContours,GlProp.alpha)
if GlProp.cvCB["TrackWindow"] | (GlProp.trackingON & self.val_CB.GetValue()):
frameImg.append(draw_tObjs(frame,CurContours,tObjList,GlProp.mDist))#3
if GlProp.trackingON & self.val_CB.GetValue():
GlProp.vidWriter.write(frameImg[GlProp.cvFrame["TrackWindow"]])
cv.WaitKey(20)
for cvWindow in GlProp.cvCB:
if GlProp.cvCB[cvWindow]:
cv2.imshow(cvWindow,frameImg[GlProp.cvFrame[cvWindow]])
cv2.waitKey(1)
else:
GlProp.frametimer.Stop()
GlProp.processratetimer.Stop()
if GlProp.trackingON:
self.stopTracking()
#del frame
#del frameImg
GlProp.processRate = GlProp.processRate + np.array([1, time.time()-frPrStart])
c = (float(imgSize[0]/2.0), float(imgSize[1]/2.0))
imgRes = cv.CreateImage((rad*3, int(360)), 8, 3)
#cv.LogPolar(image,imgRes,c,50.0, cv.CV_INTER_LINEAR+cv.CV_WARP_FILL_OUTLIERS)
cv.LogPolar(image,imgRes,c,60.0, cv.CV_INTER_LINEAR+cv.CV_WARP_FILL_OUTLIERS)
return (imgRes)
# Window creation for showing input, output
cv.NamedWindow("input", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("output", cv.CV_WINDOW_AUTOSIZE)
cv.NamedWindow("normalized", cv.CV_WINDOW_AUTOSIZE)
eyesList = os.listdir('images/eyes')
key = 0
while True:
eye = getNewEye(eyesList)
frame = cv.LoadImage("images/eyes/"+eye)
iris = cv.CloneImage(frame)
output = getPupil(frame)
iris = getIris(output)
cv.ShowImage("input", frame)
cv.ShowImage("output", iris)
normImg = cv.CloneImage(iris)
normImg = getPolar2CartImg(iris,radius)
cv.ShowImage("normalized", normImg)
key = cv.WaitKey(3000)
# seems like Esc with NumLck equals 1048603
if (key == 27 or key == 1048603):
break
cv.DestroyAllWindows()
while True:
frameCounter += 1
if cap.get(cv2.CAP_PROP_FRAME_COUNT) == frameCounter:
cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
frameCounter = 0
_, img = cap.read()
imgHsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h_min = cv2.getTrackbarPos("HUE Min", "HSV")
h_max = cv2.getTrackbarPos("HUE Max", "HSV")
s_min = cv2.getTrackbarPos("SAT Min", "HSV")
s_max = cv2.getTrackbarPos("SAT Max", "HSV")
v_min = cv2.getTrackbarPos("VALUE Min", "HSV")
v_max = cv2.getTrackbarPos("VALUE Max", "HSV")
print(h_min)
lower = np.array([h_min, s_min, v_min])
upper = np.array([h_max, s_max, v_max])
mask = cv2.inRange(imgHsv, lower, upper)
result = cv2.bitwise_and(img, img, mask=mask)
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
hStack = np.hstack([img, mask, result])
cv2.imshow('Horizontal Stacking', hStack)
if cv2.waitKey(1) and 0xFF == ord('q'):
cv2.WaitKey(0)
cap.release()
cv2.destroyAllWindows()
frame = cv2.QueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
#
### check OS
if (osName == "nt"):
cv2.Flip(frame, frame, 0)
else:
cv2.Flip(frame, None, 1)
#
### detecting faces here
detect_and_draw(frame, cascade)
#
### handle key events
k = cv2.WaitKey(5)
if k % 0x100 == 27:
# user has press the ESC key, so exit
cv2.DestroyWindow('Camera')
break
import numpy as np
import cv2
# multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades
#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml
cap = cv2.VideoCapture(0)
from time import time as timer
import tensorflow as tf
import numpy as np
import sys
import cv2 as cv
import os
vidFile = cv.CaptureFromFile('Test_Avi')
nFrames = int(cv.GetCaptureProperty(vidFile, cv.CV_CAP_PROP_FRAME_COUNT))
fps = cv.GetCaptureProperty(vidFile, cv.CV_CAP_PROP_FPS)
waitPerFrameInMillisec = int(1 / fps * 1000 / 1)
print('Num. Frames = ', nFrames)
print('Frame Rate = ', fps, ' frames per sec')
for f in xrange(nFrames):
frameImg = cv.QueryFrame(vidFile)
cv.ShowImage("My Video Window", frameImg)
cv.WaitKey(waitPerFrameInMillisec)
# When playing is done, delete the window
# NOTE: this step is not strictly necessary,
# when the script terminates it will close all windows it owns anyways
cv.DestroyWindow("My Video Window")
import serial #import the pyserial module
#Module -1: Image Processing
hc = cv2.imread(
'/home/george/PycharmProjects/Embeded image processing system/haarcascade_frontalface_alt2.xml'
)
img = cv2.imshow('/home/jayneil/beautiful-faces.jpg', 0)
faces = cv2.HaarDetectObjects(img, hc, cv2.CreateMemStorage())
a = 1
print(faces)
for (x, y, w, h), n in faces:
cv2.Rectangle(img, (x, y), (x + w, y + h), 255)
cv2.SaveImage("faces_detected.jpg", img)
dst = cv2.imread('faces_detected.jpg')
cv2.NamedWindow('Face Detected', cv2.CV_WINDOW_AUTOSIZE)
cv2.imshow('Face Detected', dst)
cv2.WaitKey(5000)
cv2.DestroyWindow('Face Detected')
#Module -2: Trigger Pyserial
if faces == []:
ser = serial.Serial('/dev/ttyUSB0', 9600)
print(ser)
ser.write('N')
else:
ser = serial.Serial('/dev/ttyUSB0', 9600)
print(ser)
ser.write('Y')
def run(self):
# Capture first frame to get size
frame = cv.QueryFrame(self.capture)
frame_size = cv.GetSize(frame)
width = frame.width
height = frame.height
surface = width * height #Surface area of the image
cursurface = 0 #Hold the current surface that have changed
grey_image = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
moving_average = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_32F, 3)
difference = None
while True:
color_image = cv.QueryFrame(self.capture)
cv.Smooth(color_image, color_image, cv.CV_GAUSSIAN, 3,
0) #Remove false positives
if not difference: #For the first time put values in difference, temp and moving_average
difference = cv.CloneImage(color_image)
temp = cv.CloneImage(color_image)
cv.ConvertScale(color_image, moving_average, 1.0, 0.0)
else:
cv.RunningAvg(color_image, moving_average, 0.020,
None) #Compute the average
# Convert the scale of the moving average.
cv.ConvertScale(moving_average, temp, 1.0, 0.0)
# Minus the current frame from the moving average.
cv.AbsDiff(color_image, temp, difference)
#Convert the image so that it can be thresholded
cv.CvtColor(difference, grey_image, cv.CV_RGB2GRAY)
cv.Threshold(grey_image, grey_image, 70, 255, cv.CV_THRESH_BINARY)
cv.Dilate(grey_image, grey_image, None, 18) #to get object blobs
cv.Erode(grey_image, grey_image, None, 10)
# Find contours
storage = cv.CreateMemStorage(0)
contours = cv.FindContours(grey_image, storage,
cv.CV_RETR_EXTERNAL,
cv.CV_CHAIN_APPROX_SIMPLE)
backcontours = contours #Save contours
while contours: #For all contours compute the area
cursurface += cv.ContourArea(contours)
contours = contours.h_next()
avg = (
cursurface * 100
) / surface #Calculate the average of contour area on the total size
if avg > self.ceil:
print("Something is moving !")
#print avg,"%"
cursurface = 0 #Put back the current surface to 0
#Draw the contours on the image
_red = (0, 0, 255)
#Red for external contours
_green = (0, 255, 0)
# Gren internal contours
levels = 1 #1 contours drawn, 2 internal contours as well, 3 ...
cv.DrawContours(color_image, backcontours, _red, _green, levels, 2,
cv.CV_FILLED)
cv.ShowImage("Target", color_image)
# Listen for ESC or ENTER key
c = cv.WaitKey(7) % 0x100
if c == 27 or c == 10:
break
def main():
global current_image
global current_img_file_name
global has_roi
global roi_x0
global roi_y0
global roi_x1
global roi_y1
iKey = 0
files = glob.glob(image_file_glob)
if len(files) == 0:
print("No files match glob pattern")
return
files = [os.path.abspath(f) for f in files]
files.sort()
# init GUI
cv.NamedWindow(window_name, 1)
cv.SetMouseCallback(window_name, on_mouse, None)
sys.stderr.write("Opening directory...")
# init output of rectangles to the info file
#os.chdir(input_directory)
sys.stderr.write("done.\n")
current_file_index = 0
while True:
current_img_file_name = files[current_file_index]
num_of_rec = 0
sys.stderr.write(
"Loading current_image (%d/%d) %s...\n" %
(current_file_index + 1, len(files), current_img_file_name))
try:
current_image = cv.LoadImage(current_img_file_name, 1)
except IOError:
sys.stderr.write("Failed to load current_image %s.\n" %
current_img_file_name)
return -1
# Work on current current_image
#cv.ShowImage(window_name, current_image)
redraw()
# Need to figure out waitkey returns.
# <Space> = 32 add rectangle to current image
# <left> = 81 save & next
# <right> = 83 save & prev
# <a> = 97 add rect to table
# <b> = 98 toggle file is background or not
# <d> = 100 remove old rect
# <q> = 113 exit program
# <s> = 115 save rect table
# <x> = 136 skip image
iKey = cv.WaitKey(0) % 255
# This is ugly, but is actually a simplification of the C++.
#sys.stderr.write(str(iKey) + '\n')
if draging:
continue
if iKey == 81:
current_file_index -= 1
if current_file_index == -1:
current_file_index = len(files) - 1
clear_roi()
elif iKey == 83:
current_file_index += 1
if current_file_index == len(files):
current_file_index = 0
clear_roi()
elif iKey == 113:
cv.DestroyWindow(window_name)
return 0
elif iKey == 97:
rect_table.setdefault(current_img_file_name, set()).add(
(roi_x0, roi_y0, roi_x1 - roi_x0, roi_y1 - roi_y0))
clear_roi()
write_rect_table()
redraw()
elif iKey == 98:
if current_img_file_name in background_files:
background_files.remove(current_img_file_name)
else:
background_files.add(current_img_file_name)
elif iKey == 100:
remove_rect(cur_mouse_x, cur_mouse_y)
elif iKey == 115:
write_rect_table()
elif iKey == 136:
sys.stderr.write("Skipped %s.\n" % current_file_index)
def run(self):
self.on_segment()
cv.WaitKey(0)
for (rho, theta) in lines[:100]:
a = math.cos(theta) #Calculate orientation in order to print them
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (cv.Round(x0 + 1000 * (-b)), cv.Round(y0 + 1000 * (a)))
pt2 = (cv.Round(x0 - 1000 * (-b)), cv.Round(y0 - 1000 * (a)))
cv.Line(color_dst_standard, pt1, pt2, cv.CV_RGB(255, 0, 0), 2,
4) #Draw the line
#---- Probabilistic ----
color_dst_proba = cv.CreateImage(cv.GetSize(im), 8, 3)
cv.CvtColor(im, color_dst_proba, cv.CV_GRAY2BGR) # idem
rho = 1
theta = pi / 180
thresh = 50
minLength = 120 # Values can be changed approximately to fit your image edges
maxGap = 20
lines = cv.HoughLines2(dst, cv.CreateMemStorage(0), cv.CV_HOUGH_PROBABILISTIC,
rho, theta, thresh, minLength, maxGap)
for line in lines:
cv.Line(color_dst_proba, line[0], line[1], cv.CV_RGB(255, 0, 0), 2, 8)
cv.ShowImage('Image', im)
cv.ShowImage("Cannied", dst)
cv.ShowImage("Hough Standard", color_dst_standard)
cv.ShowImage("Hough Probabilistic", color_dst_proba)
cv.WaitKey(0)
while True:
ret, frame = VideoCapture.read()
#The above code reads the video frame bye frame
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = FaceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
#The above snippet is used to search the face in the video
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Video", frame)
#The above snippet is used to draw rectangles around the face
if cv2.WaitKey(1) & 0xFF == ord('q'):
break
#The above code needs the user to press 'q' to quit "You can Keep any variable instead of 'q'"
VideoCapture.release()
cv2.destroyAllWindows()
#The above code is used to terminate the video.
# Display the received image
shape = metadata['shape']
nchannels = metadata['nChannels']
depth = metadata['depth']
digest = metadata['md5']
h = hashlib.md5()
h.update(binary_data)
dig = h.hexdigest()
if dig == digest:
print "Correct MD5 sum on binary data: %s" % dig
else:
print "Incorrect MD5 sum: %s (should be %s)" % (dig, digest)
img = cv.CreateImageHeader(shape, depth, nchannels)
cv.SetData(img, binary_data)
cv.ShowImage(name, img)
cv.WaitKey(30)
if not server:
# Send an image to the server
img = random.choice(images)
metadata = {
"shape": (img.width, img.height),
"nChannels": img.nChannels,
"depth": img.depth
}
binary_data = img.tostring()
h = hashlib.md5()
h.update(binary_data)
metadata['md5'] = h.hexdigest()
print "Sending image with checksum: %s" % metadata['md5']
client.send(metadata, binary_data)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH, 1280)
cv.SetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT, 720)
frame = cv.QueryFrame(capture)
test = cv.CreateImage(cv.GetSize(frame), 8, 3)
cv.NamedWindow("output")
previous_x = 0
previous_y = 0
while (1):
frame = cv.QueryFrame(capture)
cv.Flip(frame, frame, 1)
# we make all drawings on imdraw.
imdraw = cv.CreateImage(cv.GetSize(frame), 8, 3)
# we get coordinates from imgyellowthresh
imgyellowthresh = getthresholdedimg(frame)
# eroding removes small noises
cv.Erode(imgyellowthresh, imgyellowthresh, None, 1)
(leftmost, rightmost, topmost, bottommost) = getpositions(imgyellowthresh)
if (leftmost - rightmost != 0) or (topmost - bottommost != 0):
lastx = posx
lasty = posy
posx = cv.Round((rightmost + leftmost) / 2)
posy = cv.Round((bottommost + topmost) / 2)
if lastx != 0 and lasty != 0:
win32api.SetCursorPos((posx, posy))
cv.Add(test, imdraw, test)
cv.ShowImage("output", test)
if cv.WaitKey(10) >= 0:
break
cv.DestroyWindow("output")