def _close(self):
if self.verbose:
print('Closing window')
print('\n--------------------------------------')
self._print_stats()
print('--------------------------------------\n')
cv2.destroyWindow(self.name)
def colour_picker(colourSTR ="(unspecified)", colourGrabWindowSize = 5, colourHueWindowSize = 40, colourSaturationWindowSize= 40, colourValueWindowSize =40):
global cam
global hsv
print "Right click the ",colourSTR," blob. Hit escape when done."
cv2.namedWindow("image")
cv2.setMouseCallback("image", mouseCallBack, param=None)
try:
while True:
#Get image from webcam and convert to greyscale
ret, img = cam.read()
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
cv2.imshow("image", img)
cMinY = max(0, rightButtonY - colourGrabWindowSize)
cMaxY = min(len(hsv) - 1, rightButtonY + colourGrabWindowSize)
cMinX = max(0, rightButtonX - colourGrabWindowSize)
cMaxX = min(len(hsv[0]) - 1, rightButtonX + colourGrabWindowSize)
cHue = int(npy.mean(hsv[redMinY:redMaxY, redMinX:redMaxX, 0]))
cSaturation = int(npy.mean(hsv[redMinY:redMaxY, redMinX:redMaxX, 1]))
cValue = int(npy.mean(hsv[redMinY:redMaxY, redMinX:redMaxX, 2]))
#Sleep infinite loop for ~10ms
#Exit if user presses <Esc>
if cv2.waitKey(10) == 27:
break
finally:
cv2.destroyWindow("image")
return np.array(cHue, cSaturation, cValue)
def resetAll ( self ):
if self.ebb:
self.ebb.closeSerial()
if self._wormFinder:
self._wormFinder = None
self.runTracking = False
cv2.destroyWindow('gaussian')
if self._cap.isWritingVideo:
self.record()
self.ui.buttonRec.setStyleSheet('QPushButton {}')
# self._cap.stopWritingVideo()
if self._cap._capture.isOpened():
self._cap._capture.release()
self.showImage = False
self.runTracking = False
if self.motorsOn:
self.motorized()
self.motorsOn = False
self.ui.buttonMotorized.setStyleSheet('QPushButton {}')
self.ui.videoFrame.setText("Select source if video not displayed here on boot up")
self.ui.fps.setText("")
logger.info("Reset button pressed")
self.setAble('source', True)
self.setAble('reset', False)
def show_depth_img():
if show_depth_img_flag == 1:
cv2.namedWindow("Depth Img", cv2.WINDOW_NORMAL)
cv2.imshow("Depth Img", depth_img)
# cv2.imshow("Depth Img", depth_img_averaging.average())
else:
cv2.destroyWindow("Depth Img")
def start(self):
# in case the cv library is not available must return
# immediately in order to avoid any problems (required)
if not cv2: return
# retrieves the reference to the first video device
# present in the current system, this is going to be
# used for the capture of the image and delta calculus
self.camera = cv2.VideoCapture(0)
# creates both windows that are going to be used in the
# display of the current results,
cv2.namedWindow(self.win_image, cv2.CV_WINDOW_AUTOSIZE)
cv2.namedWindow(self.win_delta, cv2.CV_WINDOW_AUTOSIZE)
# sets the initial previous image as an invalid image as
# there's no initial image when the loop starts
self.previous = None
# iterates continuously for the running of the main loop
# of the current program (this is the normal behavior)
while True:
result = self.tick()
if not result: break
key = cv2.waitKey(10)
if key == 27: break
# destroys the currently displayed windows on the screen
# so that they can no longer be used in the current screen
cv2.destroyWindow(self.win_image)
cv2.destroyWindow(self.win_delta)
def run(self):
rate = rospy.Rate(10)
done = False
cv2.namedWindow("kinect_view")
cv2.setMouseCallback("kinect_view", self.mouse_call)
while (not rospy.is_shutdown() and not done):
if self.image is None:
continue
image = np.copy(self.image)
state = self.states[self.state].replace('_', ' ')
cv2.putText(image, 'Click the {}'.format(self.target_object), (10, self.image.shape[1] - 100), self.font, 1, (255, 100, 80), 2)
self.draw_corners(image)
if self.is_done:
cv2.polylines(image, np.int32([self.corners]), True, (0, 255, 0), 6)
done = True
print 'DONE'
cv2.imshow("kinect_view", image)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
rate.sleep()
if done:
cv2.destroyWindow("kinect_view")
def position_interpolator(background):
global positions
if not isfile(POSITIONS_DUMP_FILENAME):
def callback(event, x, y, flags, parameters):
if event == 1: #cv2.EVENT_RBUTTONDOWN:
positions.append(Coordinate(x, y))
cv2.namedWindow("Interpolator")
cv2.setMouseCallback("Interpolator", callback)
while True:
cv2.imshow("Interpolator", background.array)
if cv2.waitKey() & 0xFF == 27:
break
cv2.destroyWindow("Interpolator")
with open(POSITIONS_DUMP_FILENAME, "w") as positions_dump_file:
pickle.dump(positions, positions_dump_file)
else:
with open(POSITIONS_DUMP_FILENAME, "r") as positions_dump_file:
positions = pickle.load(positions_dump_file)
t = map(lambda i: i * STEP, range(len(positions)))
x = map(lambda p: p.x, positions)
y = map(lambda p: p.y, positions)
f_x = interpolate.interp1d(t, x, kind = "quadratic")
f_y = interpolate.interp1d(t, y, kind = "quadratic")
return PositionInterpolator(f_x, f_y)
def run(self):
while True:
f, orig_img = self.capture.read()
orig_img = cv2.flip(orig_img, 1)
img = cv2.GaussianBlur(orig_img, (5,5), 0)
img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2HSV)
img = cv2.resize(img, (len(orig_img[0]) / self.scale_down, len(orig_img) / self.scale_down))
red_lower = np.array([0, 150, 0],np.uint8)
red_upper = np.array([5, 255, 255],np.uint8)
red_binary = cv2.inRange(img, red_lower, red_upper)
dilation = np.ones((15, 15), "uint8")
red_binary = cv2.dilate(red_binary, dilation)
contours, hierarchy = cv2.findContours(red_binary, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
max_area = 0
largest_contour = None
for idx, contour in enumerate(contours):
area = cv2.contourArea(contour)
if area > max_area:
max_area = area
largest_contour = contour
if not largest_contour == None:
moment = cv2.moments(largest_contour)
if moment["m00"] > 1000 / self.scale_down:
rect = cv2.minAreaRect(largest_contour)
rect = ((rect[0][0] * self.scale_down, rect[0][1] * self.scale_down), (rect[1][0] * self.scale_down, rect[1][1] * self.scale_down), rect[2])
box = cv2.cv.BoxPoints(rect)
box = np.int0(box)
cv2.drawContours(orig_img,[box], 0, (0, 0, 255), 2)
cv2.imshow("ColourTrackerWindow", orig_img)
if cv2.waitKey(20) == 27:
cv2.destroyWindow("ColourTrackerWindow")
self.capture.release()
break
def display_loop(framequeue, quick_catchup, quick_catchup_pixels):
# Open a window in which to display the images
display_window_name = "slowjector"
cv2.namedWindow(display_window_name, cv2.cv.CV_WINDOW_NORMAL)
last_delta_count = 0
listq = deque()
while True:
sleep(0.001) # Small amount of sleeping for thread-switching
data = framequeue.get()
# Source thread will put None if it receives c-C; if this happens, exit the
# loop and shut off the display.
if data is None:
break
# Frames are pushed onto a queue (FIFO)
listq.append(data)
data = listq.popleft()
# Otherwise, it puts a tuple (delta_count, image)
delta_count, image = data
# Draw the image
cv2.imshow(display_window_name, image)
# Optionally, catch up to the live feed after seeing some motion stop by
# popping all images off of the queue.
if (quick_catchup and
delta_count <= quick_catchup_pixels and
last_delta_count > quick_catchup_pixels):
listq.clear()
last_delta_count = delta_count
# Clean up by closing the window used to display images.
cv2.destroyWindow(display_window_name)
def detect():
q = QRobot()
cap=cv2.VideoCapture(1)
success, frame = cap.read()
color = (0, 255, 0)
classfier=cv2.CascadeClassifier("/home/echo/Desktop/qrobot/qrobot_py/haarcascade_frontalface_alt.xml")
while success:
success, frame = cap.read()
size=frame.shape[:2]
image=np.zeros(size,dtype=np.float16)
image = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.equalizeHist(image, image)
divisor=8
h, w = size
minSize=(w/divisor, h/divisor)
faceRects = classfier.detectMultiScale(image, 1.2, 2, cv2.CASCADE_SCALE_IMAGE,minSize)
if len(faceRects)>0:
for faceRect in faceRects:
x, y, w, h = faceRect
cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
q.left_wing_fast_up(1)
q.right_wing_fast_up(1)
q.heart_light_color(randint(0,255), randint(0,255), randint(0,255))
q.eye_emotion(randint(0,50), 1)
q.send_data()
cv2.imshow("test", frame)
key=cv2.waitKey(10)
c = chr(key & 255)
if c in ['q', 'Q', chr(27)]:
break
del q
cap.release()
cv2.destroyWindow("test")
def click_and_crop(event, x, y, flags, param):
# grab references to the global variables
global refPt, cropping, cropImage
# if the left mouse button was clicked, record the starting
# (x, y) coordinates and indicate that cropping is being
# performed
if event == cv2.EVENT_LBUTTONDOWN:
refPt = [(x, y)]
cropping = True
# check to see if the left mouse button was released
elif event == cv2.EVENT_LBUTTONUP:
# record the ending (x, y) coordinates and indicate that
# the cropping operation is finished
refPt.append((x, y))
cropping = False
# draw a rectangle around the region of interest
cv2.rectangle(cropImage, refPt[0], refPt[1], (0, 255, 0), 2)
cv2.imshow("crop1", cropImage)
msg = Target()
msg.bb.x = refPt[0][0]
msg.bb.y = refPt[0][1]
msg.bb.width = x - refPt[0][0]
msg.bb.height = y - refPt[0][1]
msg.bb.confidence = 1
msg.img = bridge.cv2_to_imgmsg(cropImage, 'bgr8')
pub1 = rospy.Publisher("tld_gui_bb", Target, queue_size = 10)
cv2.waitKey(1000)
cv2.destroyWindow('crop1')
pub1.publish(msg)
def callback_depth(msg):
# treating the image containing the depth data
global depthImgIndex, lastDepthImgs, depth_img_Avg, got_depth
# getting the image
try:
img = CvBridge().imgmsg_to_cv2(msg, "passthrough")
except CvBridgeError as e:
print (e)
return
cleanimage = clean(img, 255)
if show_depth:
# shows the image after processing
cv2.imshow("Depth", img)
cv2.waitKey(1)
else:
cv2.destroyWindow("Depth")
# storing the image
lastDepthImgs[depthImgIndex] = np.copy(cleanimage)
depthImgIndex += 1
if depthImgIndex > NB_DEPTH_IMGS:
depthImgIndex = 0
# creates an image which is the average of the last ones
depth_img_Avg = np.copy(lastDepthImgs[0])
for i in range(0, NB_DEPTH_IMGS):
depth_img_Avg += lastDepthImgs[i]
depth_img_Avg /= NB_DEPTH_IMGS
got_depth = True # ensures there is an depth image available
if got_color and got_depth:
filter_by_depth()
def get_cams(self, image):
cv2.namedWindow("Screw")
cv2.startWindowThread()
cv2.setMouseCallback("Screw", self._get_screw)
self.screw_location = Point(0,0)
while True:
img = copy.copy(image)
cv2.circle(img, self.screw_location.to_image(), 3, (0,0,255), 3)
cv2.imshow('Screw', img)
k = cv2.waitKey(33)
if k==10:
# enter pressed
break
elif k==-1:
pass
else:
print k
print 'Press Enter to continue..'
cv2.destroyWindow('Screw')
arc = self.screw_location - self.board_center
cam_angle = arc.angle()
self.cam_angle = cam_angle
if len(self.base_cams) != 0:
self.cams = self.rotate(self.base_cams, cam_angle)
# self.cams = [c for c in self.cams if c.y <= 0]
self.locate_cams(image)
def run(self):
cv2.namedWindow('ANALOG', flags=cv2.WINDOW_KEEPRATIO)
cv2.setMouseCallback('ANALOG', self.on_mouse)
while True:
image = self.img.copy()
text = 'Press "e" to exit and save, "r" to reset current data'
cv2.putText(img=image, org=(100, 100), text=text,
fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
color=(255, 255, 255), thickness=2, lineType=cv2.LINE_AA)
h = 150
for t in self.training_data:
txt = "deg {0}, value: {1}".format(t['degree'], t['value'])
cv2.putText(img=image, org=(100, h), text=txt,
fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
color=(255, 255, 255), thickness=2, lineType=cv2.LINE_AA)
h += 30
cv2.imshow('ANALOG', image)
key = cv2.waitKey(33) & 0xFF
if key == ord('e'):
break
elif key == ord('r'):
self.training_data = []
cv2.destroyWindow('ANALOG')
return self.training_data
def prepare_face_data(self, face_label):
height = 480
width = 640
rate = rospy.Rate(10)
facecount = 0
face_images = []
face_labels = []
cv2.namedWindow("Training Set Preparation", 1)
while (not rospy.is_shutdown()) and (facecount < TRAIN_SIZE+TEST_SIZE):
if(self.is_image_present):
im = self.image
else:
im = np.ones((height,width,3), np.uint8)
(rows,cols,channels) = im.shape
im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(im_gray, 1.3, 5)
if (len(faces) == 1):
for (x,y,w,h) in faces:
if ((w>50) and (h>50)):
cv2.rectangle(im,(x,y),(x+w,y+h),(255,0,0),2)
face_gray = np.array(im_gray[y:y+h, x:x+w], 'uint8')
face_sized = cv2.resize(face_gray, (200, 200))
face_images.append(face_sized)
face_labels.append(face_label)
facecount += 1
cv2.imshow("Training Set Preparation", im)
cv2.waitKey(3)
rate.sleep()
cv2.destroyWindow("Training Set Preparation")
return face_images, face_labels
def demoHaarLike(namevideo, width, height):
cv2.namedWindow("preview")
vc = cv2.VideoCapture(0)
outvideo=cv2.VideoWriter(namevideo,-1,60,(width,height))
idx=0
if vc.isOpened(): # try to get the first frame
rval, frame = vc.read()
else:
rval = False
while rval:
# frame=cv2.imread('0.bmp')
cv2.imshow("preview", frame)
rval, frame = vc.read()
frame=HandDetectionImproved(frame)
# outvideo.write(frame)
key = cv2.waitKey(1)
if key==ord('s'):
cv2.imwrite(str(idx)+'.bmp',frame)
idx=idx+1
if key == 27: # exit on ESC
break
cv2.destroyWindow("preview")
vc.release()
outvideo.release()
def detect_motion_new(self, winName, interval=1):
#Implemented after talking with Dr Smart about image averaging and background extraction
min_contour_area = 25
max_contour_area = 1250
retval = False
threshold = 65
try:
_image_static = None
_image_static = self.save_image(persist=False)
_image_static = cv2.cvtColor(numpy.array(_image_static), cv2.COLOR_RGB2GRAY)
_image_static = cv2.GaussianBlur(_image_static, (21, 21), 0)
accumulator = numpy.float32(_image_static)
while True:
sleep(interval)
_image_static = self.save_image(persist=False)
_image_static = cv2.cvtColor(numpy.array(_image_static), cv2.COLOR_RGB2GRAY)
_image_static = cv2.GaussianBlur(_image_static, (21, 21), 0)
cv2.accumulateWeighted(numpy.float32(_image_static), accumulator, 0.1)
_image_static = cv2.convertScaleAbs(accumulator)
_image_dynamic = self.save_image(persist=False)
_image_dynamic1 = cv2.cvtColor(numpy.array(_image_dynamic), cv2.COLOR_RGB2GRAY)
_image_dynamic1 = cv2.GaussianBlur(_image_dynamic1, (21, 21), 0)
# ideas from http://docs.opencv.org/master/d4/d73/tutorial_py_contours_begin.html#gsc.tab=0
_delta = cv2.absdiff(_image_dynamic1, _image_static)
_threshold = cv2.threshold(_delta, 17, 255, cv2.THRESH_BINARY)[1]
# dilate the thresholded image to fill in holes, then find contour on thresholded image
_threshold = cv2.dilate(_threshold, None, iterations=5)
(img, contours, _) = cv2.findContours(_threshold.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
dyn = cv2.cvtColor(numpy.array(_image_dynamic), cv2.COLOR_RGB2GRAY)
# loop over the contours
for contour in contours:
# if the contour is too small, ignore it
_area = cv2.contourArea(contour)
if _area < min_contour_area: # or _area > max_contour_area:
continue # skip to the next
# compute the bounding box for the contour, draw it on the frame,
(x, y, w, h) = cv2.boundingRect(contour)
#cv2.rectangle(dyn, (x, y), (x + w, y + h), (0, 12, 255), 2)
cv2.ellipse(dyn, (x+5, y+25), (10, 20), 90, 0, 360, (255, 0, 0), 2)
cv2.imshow(winName, numpy.hstack([dyn, _threshold]))
key = cv2.waitKey(10)
if key == 27:
cv2.destroyWindow(winName)
break
except Exception as ex:
print(ex)
def onmouse(self, event, x, y, flags, param):
"""
Mouse callback when mouse event detected in the window.
Note: This function is only used for ROI setting.
"""
if event == cv2.EVENT_LBUTTONDOWN:
self.dragStart = x, y
self.sel = 0,0,0,0
elif self.dragStart:
#print flags
if flags & cv2.EVENT_FLAG_LBUTTON:
minpos = min(self.dragStart[0], x), min(self.dragStart[1], y)
maxpos = max(self.dragStart[0], x), max(self.dragStart[1], y)
self.sel = minpos[0], minpos[1], maxpos[0], maxpos[1]
img = cv2.cvtColor(self.img, cv2.COLOR_GRAY2BGR)
cv2.rectangle(img, (self.sel[0], self.sel[1]), (self.sel[2], self.sel[3]), (0,255,255), 1)
cv2.imshow(self.test_winname, img)
else:
patch = self.img[self.sel[1]:self.sel[3], self.sel[0]:self.sel[2]]
self.hist_lines(patch, showhist=True)
cv2.destroyWindow("patch")
cv2.imshow("patch", patch)
self.get_dft(img2dft=patch, showdft=True)
print "Press a to accept the ROI"
self.roiNeedUpadte = True
self.dragStart = None
def remove_sin_noise(self):
"""
Warning: this function is unoptimized and may runs slowly.
Show the origin image with 3 trackbar.
These bars control the direction, amplitude and phase
of the compensation sin wave, respectively.
A result window shows the result.
"""
h, w = self.img.shape[:2]
small_img = cv2.resize(self.img, (w / 2, h / 2))
cv2.imshow(self.test_winname, small_img)
cv2.createTrackbar("A*100", self.test_winname,
0, 100, self.update_sine_win)
cv2.createTrackbar("B", self.test_winname,
1, 100, self.update_sine_win)
cv2.createTrackbar("amp", self.test_winname,
0, 255, self.update_sine_win)
cv2.createTrackbar("pha", self.test_winname,
0, 360, self.update_sine_win)
self.update_sine_win()
while True:
ch = cv2.waitKey()
if ch == 27: # Esc
break
cv2.destroyWindow(self.test_winname)
def capture_new_frame(self):
assert self.camera.isOpened()
# get feed from camera frame by frame
status,self.frame = self.camera.read()
#just mirror the image (vertical flip)
if status==True:
self.frame=cv2.flip(self.frame,flipCode=1)
#else break from the loop as the frame was
#not grabbed :D nothing to process
if not self.hand_histogram_captured:
# Draw hand detection filter
result = self.draw_hand_filter(self.frame)
cv2.imshow('Cover all squares with your hand', result)
else:
self.detect_finger_tip(self.frame)
# Scan Human Skin to calc histogram if SPACE is pressed
pressed_key = cv2.waitKey(1)
if pressed_key == 32:
self.scan_skin()
cv2.destroyWindow('Cover all squares with your hand')
self.hand_histogram_captured = True
# If ESC is pressed destroy all Opencv windows (Close Debugging Windows)
elif pressed_key == 27:
cv2.destroyAllWindows()
self.DEBUGGING = False
def vertCrop(edge,a):
# find the top and bottom of joint
h = edge.shape[0]; w = edge.shape[1]
top,bot = 0,0
mid = int(w/2)
print "mid=",mid,"h=", h, 'w=', w
for x in xrange(h/12,h-(h/12)):
if top != 0 and bot != 0: break
if top == 0:
if edge[x,mid] == 255.0: top = x
if bot == 0:
if edge[h-x-1,mid] == 255.0: bot = h-x-1
print 'top=',top,'bot=',bot
# edge[top,mid] = edge[bot,mid] = 111
# crop image to just the joint gap vertically
cratio = h/12
ctop = top-(cratio)
cbot = bot+(cratio)
print 'ctop=',ctop,'cbot=',cbot
cedge = edge[range(ctop,cbot)]
cv.imshow("New Edge", cedge)
a = a[range(ctop,cbot)]
cv.imshow("Cropped Real", a)
cv.waitKey(0)
cv.destroyWindow("Cropped Real")
cv.destroyWindow("New Edge")
return a,ctop,cbot
def findDigits(imagefile, digitheight, fontfile="C:\\Windows\\Fonts\\Arial.ttf"):
im = cv2.imread(imagefile)
out = np.zeros(im.shape,np.uint8)
gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
contours,hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_SIMPLE)
model = createDigitsModel(fontfile, digitheight)
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
if h>w and h>(digitheight*4)/5 and h<(digitheight*6)/5: #+/-20%
cv2.rectangle(im,(x,y),(x+w,y+h),(0,255,0),1)
roi = thresh[y:y+h,x:x+w] # crop
roi = cv2.resize(roi,(digitheight,digitheight/2))
roi = roi.reshape((1,digitheight*(digitheight/2)))
roi = np.float32(roi)
retval, results, neigh_resp, dists = model.find_nearest(roi, k=1)
string = str(int((results[0][0])))
#cv2.drawContours(out,[cnt],-1,(0,255,255),1)
cv2.putText(out,string,(x,y+h),0,1,(0,255,0))
cv2.imshow('in',im)
cv2.imshow('out',out)
cv2.waitKey(0)
cv2.destroyWindow( 'in' )
cv2.destroyWindow( 'out' )
def get_coords(img, winname=None):
out = img.copy()
coords = []
if winname is None:
winname = 'choose coords'
def onmouse(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
coords.append((x, y))
cv2.circle(out, (x, y), radius=10, color=(0, 0, 255),
thickness=-1)
cv2.imshow(winname, out)
cv2.imshow(winname, out)
cv2.setMouseCallback(winname, onmouse)
while True:
k = cv2.waitKey(1) & 0xFF
if k == 27:
out = img.copy()
coords = []
if k == ord(' '):
cv2.destroyWindow(winname)
return coords
def get_polyline(image,window_name):
cv2.namedWindow(window_name)
class GetPoly:
xys = []
done = False
def callback(self,event, x, y, flags, param):
if self.done == True:
pass
elif event == cv2.EVENT_LBUTTONDOWN:
self.xys.append((x,y))
elif event == cv2.EVENT_MBUTTONDOWN:
self.done = True
gp = GetPoly()
cv2.setMouseCallback(window_name,gp.callback)
print "press middle mouse button or 'c' key to complete the polygon"
while not gp.done:
im_copy = image.copy()
for (x,y) in gp.xys:
cv2.circle(im_copy,(x,y),2,(0,255,0))
if len(gp.xys) > 1 and not gp.done:
cv2.polylines(im_copy,[np.array(gp.xys).astype('int32')],False,(0,255,0),1)
cv2.imshow(window_name,im_copy)
key = cv2.waitKey(50)
if key == ord('c'): gp.done = True
cv2.destroyWindow(window_name)
return gp.xys
def show_hls(img, winname=None):
if winname is None:
winname = 'Choose HLS bounds'
h, w, _ = img.shape
f = 720.0 / h
img_720p = cv2.resize(img, dsize=None, fx=f, fy=f)
def onmouse(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
bgr = img[y, x]
hls = cv2.cvtColor(np.asarray([[bgr]], dtype=img.dtype),
cv2.COLOR_BGR2HLS)[0, 0]
out = img_720p.copy()
cv2.putText(out, text='BGR={}, HLS={}'.format(bgr, hls),
org=(0, 300), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.5, color=(255, 255, 255))
cv2.imshow(winname, out)
cv2.imshow(winname, img_720p)
cv2.setMouseCallback(winname, onmouse)
while True:
k = cv2.waitKey(1) & 0xFF
if k == 27:
cv2.destroyWindow(winname)
return
def interactive_imshow(img, lclick_cb=None, rclick_cb=None, **kwargs):
"""
Args:
img (np.ndarray): an image (expect BGR) to show.
lclick_cb, rclick_cb: a callback ``func(img, x, y)`` for left/right click event.
kwargs: can be {key_cb_a: callback_img, key_cb_b: callback_img}, to
specify a callback ``func(img)`` for keypress.
Some existing keypress event handler:
* q: destroy the current window
* x: execute ``sys.exit()``
* s: save image to "out.png"
"""
name = 'tensorpack_viz_window'
cv2.imshow(name, img)
def mouse_cb(event, x, y, *args):
if event == cv2.EVENT_LBUTTONUP and lclick_cb is not None:
lclick_cb(img, x, y)
elif event == cv2.EVENT_RBUTTONUP and rclick_cb is not None:
rclick_cb(img, x, y)
cv2.setMouseCallback(name, mouse_cb)
key = chr(cv2.waitKey(-1) & 0xff)
cb_name = 'key_cb_' + key
if cb_name in kwargs:
kwargs[cb_name](img)
elif key == 'q':
cv2.destroyWindow(name)
elif key == 'x':
sys.exit()
elif key == 's':
cv2.imwrite('out.png', img)
def test_find_holes(self):
ft = FishTracker()
BAG_NAME = '../../fish_detective/bags/2016-04-06-16-57-50.bag' #1437
# BAG_NAME = '../../fish_detective/training/bags/three_fish.bag'
bag = rosbag.Bag(BAG_NAME)
imgs = self.get_ros_img(bag)
zero = imgs.next()
# import IPython; IPython.embed()
ft.find_holes(zero)
fishes = ft.track_holes(zero)
cv2.namedWindow("Calibration")
cv2.startWindowThread()
cv2.imshow('Calibration', fishes)
k = cv2.waitKey(0)
one = imgs.next()
ft.find_holes(one)
fishes = ft.track_holes(one)
cv2.imshow('Calibration', fishes)
k = cv2.waitKey(0)
for i in range(60):
im = imgs.next()
ft.find_holes(im)
fishes = ft.track_holes(im)
cv2.imshow('Calibration', fishes)
k = cv2.waitKey(1)
cv2.destroyWindow('Calibration')
def get_hls_range(img, winname=None):
if winname is None:
winname = 'Choose HLS bounds'
h, w, _ = img.shape
f = 720.0 / h
img_720p = cv2.resize(img, dsize=None, fx=f, fy=f)
def on_trackbar_changed(x):
lowerb = tuple(
cv2.getTrackbarPos(ch + '_min', winname) for ch in 'HLS')
upperb = tuple(
cv2.getTrackbarPos(ch + '_max', winname) for ch in 'HLS')
out = img_util.apply_mask_hls(img_720p, lowerb, upperb)
cv2.imshow(winname, out)
cv2.imshow(winname, img_720p)
for ch in 'HLS':
cv2.createTrackbar(ch + '_min', winname, 0, 255, on_trackbar_changed)
cv2.createTrackbar(ch + '_max', winname, 255, 255, on_trackbar_changed)
while True:
cv2.waitKey()
lowerb = tuple(
cv2.getTrackbarPos(ch + '_min', winname) for ch in 'HLS')
upperb = tuple(
cv2.getTrackbarPos(ch + '_max', winname) for ch in 'HLS')
cv2.destroyWindow(winname)
return lowerb, upperb
def ExpansionVertical(img, punto, direccion):
punto = Punto(punto.x, punto.y)
puntos = []
puntos += ExpansionVerticalSimple(img, punto, punto.y - 1, punto.y -23, direccion)[::-1] # invertida
puntos += [punto]
puntos += ExpansionVerticalSimple(img, punto, punto.y + 1, punto.y +23, direccion)
linea = Linea(punto)
linea.puntos = puntos
if debug:
imagen = img.copy()
imagen = cv2.cvtColor(imagen, cv2.COLOR_GRAY2BGR)
for punto in puntos:
cv2.circle(imagen,(punto.x,punto.y),1,(0,0,255),-1)
cv2.imshow("debug",imagen)
esperar()
cv2.destroyWindow("debug")
if len(puntos) < 35:
# Supongo discontinuidad
return False, linea
else:
linea.recta, linea.error = minimosCuadrados(puntos)
if linea.error > 0.5:
return False, linea
return True, linea
def create_process_window(img):
window_name = "res"
cv2.namedWindow(window_name)
createBars(window_name,img)
process(img)
cv2.waitKey(-1)
cv2.destroyWindow(window_name)
def run(self):
"""Will run the tracking program on the video from vid_src."""
running = True
cv2.namedWindow("Drone Camera")
while running:
image = self.drone.image.copy()
red, green, blue = cv2.split(image)
image = cv2.merge((blue, green, red))
self.currFrame = image
x = cv2.waitKey(33)
if x != -1:
print("User override")
key = chr(x & 255)
if key == 't':
time = datetime.now().strftime('%Y-%m-%d-%H%M%S')
filename = "cap-" + time + ".jpg"
path = ".." + os.path.join(os.sep, "res", "captures",
filename)
print(path)
cv2.imwrite(path, image)
print("Image Saved")
elif key in {'i', 'I'}:
self.drone.move_up()
time.sleep(0.3)
self.drone.hover()
elif key in {'k', 'K'}:
self.drone.move_down()
time.sleep(0.3)
self.drone.hover()
elif key in {'j', 'J'}:
self.drone.turn_left()
time.sleep(0.3)
self.drone.hover()
elif key in {'l', 'L'}:
self.drone.turn_right()
time.sleep(0.3)
self.drone.hover()
elif key in {'w', 'W'}:
self.drone.move_forward()
time.sleep(0.3)
self.drone.hover()
elif key in {'s', 'S'}:
self.drone.move_backward()
time.sleep(0.3)
self.drone.hover()
elif key in {'a', 'A'}:
self.drone.move_left()
time.sleep(0.3)
self.drone.hover()
elif key in {'d', 'D'}:
self.drone.move_right()
time.sleep(0.3)
self.drone.hover()
elif key == 'q' or key == ' ':
self.parent.quit()
frame = self.update()
cv2.imshow("Drone Camera", frame)
with self.lock:
running = self.running
print("Quitting MCS")
cv2.destroyWindow("Drone Camera")
cv2.waitKey(10)
def ImageProcessing(n_boards, board_w, board_h, board_dim):
#Initializing variables
board_n = board_w * board_h
opts = []
ipts = []
npts = np.zeros((n_boards, 1), np.int32)
intrinsic_matrix = np.zeros((3, 3), np.float32)
distCoeffs = np.zeros((5, 1), np.float32)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
# prepare object points based on the actual dimensions of the calibration board
# like (0,0,0), (25,0,0), (50,0,0) ....,(200,125,0)
objp = np.zeros((board_h * board_w, 3), np.float32)
objp[:, :2] = np.mgrid[0:(board_w * board_dim):board_dim,
0:(board_h * board_dim):board_dim].T.reshape(-1, 2)
#Loop through the images. Find checkerboard corners and save the data to ipts.
for i in range(1, n_boards + 1):
#Loading images
print('Loading... Calibration_Image' + str(i) + '.png')
image = cv2.imread('Calibration_Image' + str(i) + '.png')
if (type(image) is not None):
#Converting to grayscale
grey_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
#Find chessboard corners
found, corners = cv2.findChessboardCorners(
grey_image, (board_w, board_h),
cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_NORMALIZE_IMAGE)
print(found)
if found == True:
#Add the "true" checkerboard corners
opts.append(objp)
#Improve the accuracy of the checkerboard corners found in the image and save them to the ipts variable.
cv2.cornerSubPix(grey_image, corners, (20, 20), (-1, -1),
criteria)
ipts.append(corners)
#Draw chessboard corners
cv2.drawChessboardCorners(image, (board_w, board_h), corners,
found)
#Show the image with the chessboard corners overlaid.
cv2.imshow("Corners", image)
char = cv2.waitKey(10)
cv2.destroyWindow("Corners")
print('')
print('Finished processes images.')
#Calibrate the camera
print('Running Calibrations...')
print(' ')
ret, intrinsic_matrix, distCoeff, rvecs, tvecs = cv2.calibrateCamera(
opts, ipts, grey_image.shape[::-1], None, None)
#Save matrices
print('Intrinsic Matrix: ')
print(str(intrinsic_matrix))
print(' ')
print('Distortion Coefficients: ')
print(str(distCoeff))
print(' ')
#Save data
print('Saving data file...')
np.savez('calibration_data',
distCoeff=distCoeff,
intrinsic_matrix=intrinsic_matrix)
print('Calibration complete')
#Calculate the total reprojection error. The closer to zero the better.
tot_error = 0
for i in range(len(opts)):
imgpoints2, _ = cv2.projectPoints(opts[i], rvecs[i], tvecs[i],
intrinsic_matrix, distCoeff)
error = cv2.norm(ipts[i], imgpoints2, cv2.NORM_L2) / len(imgpoints2)
tot_error += error
print("total reprojection error: ", tot_error / len(opts))
#Undistort Images
#Scale the images and create a rectification map.
newMat, ROI = cv2.getOptimalNewCameraMatrix(intrinsic_matrix,
distCoeff,
image_size,
alpha=crop,
centerPrincipalPoint=1)
mapx, mapy = cv2.initUndistortRectifyMap(intrinsic_matrix,
distCoeff,
None,
newMat,
image_size,
m1type=cv2.CV_32FC1)
for i in range(1, n_boards + 1):
#Loading images
print('Loading... Calibration_Image' + str(i) + '.png')
image = cv2.imread('Calibration_Image' + str(i) + '.png')
if (type(image) is not None):
# undistort
dst = cv2.remap(image, mapx, mapy, cv2.INTER_LINEAR)
cv2.imshow('Undisorted Image', dst)
char = cv2.waitKey(0)
cv2.destroyAllWindows()
started = False
while rval:
if started == False:
start = time.time()
framesPast = 0
started = True
rval, frame = vc.read()
framesPast = framesPast + 1
if frame[240, 300][0] < 150 and frame[240, 300][1] < 150 and frame[
240, 300][2] < 150 and switchingW == False:
print time.time() - start
ser.write('1')
print framesPast
switchingW = True
started = False
if frame[240, 300][0] > 150 and frame[240, 300][1] > 150 and frame[
240, 300][2] > 150 and switchingW == True:
print time.time() - start
ser.write('0')
print framesPast
switchingW = False
started = False
# ser.write('2') debug command
# print ser.readline() debug command
# cleanup
cv2.destroyWindow("preview")
vc.release()
ser.close()
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate
) # preprocess to get grayscale and threshold images
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5d", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True: # show steps ###################################################
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # clear the contours list
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
# end if # show steps #####################################################################
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("7", imgContours)
# end if # show steps #####################################################################
if (len(listOfListsOfMatchingCharsInPlate) == 0
): # if no groups of matching chars were found in the plate
if Main.showSteps == True: # show steps ###############################################
print "chars found in plate number " + str(
intPlateCounter
) + " = (none), click on any image and press a key to continue . . ."
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
# end if # show steps #################################################################
possiblePlate.strChars = ""
continue # go back to top of for loop
# end if
for i in range(0, len(listOfListsOfMatchingCharsInPlate)
): # within each list of matching chars
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX
) # sort chars from left to right
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) # and remove inner overlapping chars
# end for
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("8", imgContours)
# end if # show steps #####################################################################
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
# end if # show steps #####################################################################
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
print "chars found in plate number " + str(
intPlateCounter
) + " = " + possiblePlate.strChars + ", click on any image and press a key to continue . . ."
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end if # show steps #####################################################################
# end of big for loop that takes up most of the function
if Main.showSteps == True:
print "\nchar detection complete, click on any image and press a key to continue . . .\n"
cv2.waitKey(0)
# end if
return listOfPossiblePlates
def mouseDoubleClick(eX, eY, dragObj):
if dragObj.active:
if pointInRect(eX, eY, dragObj.outRect.x, dragObj.outRect.y, dragObj.outRect.w, dragObj.outRect.h):
dragObj.returnflag = True
cv2.destroyWindow(dragObj.wname)
import cv2
import numpy as np
import copy
eyes_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
img = cv2.imread("selfie.jpg")
original = copy.deepcopy(img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
eyes = eyes_cascade.detectMultiScale(gray, 1.1, 3)
for (x, y, w, h) in eyes:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
winName1 = "Original image"
winName2 = "Filtered image"
cv2.startWindowThread()
cv2.namedWindow(winName1)
cv2.namedWindow(winName2)
cv2.imshow(winName1, original)
cv2.imshow(winName2, img)
# pressing any key will break GUI loop and close window
cv2.waitKey(0)
cv2.destroyWindow(winName1)
cv2.destroyWindow(winName2)
def _show_image(win, img, destroy=True):
cv2.imshow(win, img)
cv2.waitKey(0)
if destroy:
cv2.destroyWindow(win)
import numpy as np import cv2 img = np.zeros((512, 512, 3), np.uint8) 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, (447, 63), 63, (0, 0, 255), -1) cv2.ellipse(img, (256, 256), (100, 50), 0, 0, 188, 255, -1) pts = np.array([[10, 5], [20, 30], [70, 20], [50, 10]], np.int32) pts = pts.reshape((-1, 1, 2)) font = cv2.FONT_HERSHEY_SCRIPT_SIMPLEX cv2.putText(img, 'OpenCV', (10, 500), font, 4, (255, 255, 255, 0), 2) winname = 'example' cv2.namedWindow(winname) cv2.imshow(winname, img) cv2.waitKey(0) cv2.destroyWindow(winname)
img = frame
while 1:
cv2.imshow('image', img)
# get current positions of trackbars
h = cv2.getTrackbarPos('Lower_H', 'image')
s = cv2.getTrackbarPos('Lower_S', 'image')
v = cv2.getTrackbarPos('Lower_V', 'image')
u_h = cv2.getTrackbarPos('Upper_H', 'image')
u_s = cv2.getTrackbarPos('Upper_S', 'image')
u_v = cv2.getTrackbarPos('Upper_V', 'image')
it = cv2.getTrackbarPos('iter', 'image')
lower_limit = np.array([h, s, v])
upper_limit = np.array([u_h, u_s, u_v])
aux = cv2.inRange(hsv, lower_limit, upper_limit)
aux = cv2.erode(aux, None, iterations=it)
aux = cv2.dilate(aux, None, iterations=it)
img2 = cv2.bitwise_and(frame, frame, mask=aux)
# original mais segmentada
img = cv2.addWeighted(frame, 0.1, img2, 0.9, 0)
k = cv2.waitKey(5) & 0xFF
if k == 27:
cv2.destroyWindow('image')
def get_marginpos(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
ret = [x, y]
margin_pos.append(ret)
print(margin_pos)
cv2.namedWindow("image")
cv2.namedWindow("face")
cv2.setMouseCallback("image", get_marginpos)
# 获取表情包的背景图片，并进行二值化处理
emoji_back = cv2.imread("pics/back.jpg")
ret, emoji_back = cv2.threshold(emoji_back, 100, 255, cv2.THRESH_BINARY)
face = cv2.imread("pics_processed/20210205/wyh_face_binary.jpg")
# 获取填充的坐标
cv2.imshow("image", face)
key = cv2.waitKey(0)
if key == 27:
cv2.destroyWindow("image")
margin_area = np.array([margin_pos])
image_margin = cv2.fillPoly(face, [margin_area], (255, 255, 255))
cv2.imshow("face", image_margin)
key = cv2.waitKey(0)
if key == 27:
cv2.destroyWindow("face")
# Each location contains positions in order: top, right, bottom, left
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
# Get color by name using our fancy function
color = name_to_color(match)
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color,
FRAME_THICKNESS)
# Now we need smaller, filled grame below for a name
# This time we use bottom in both corners - to start from bottom and move 50 pixels down
top_left = (face_location[3], face_location[2])
bottom_right = (face_location[1], face_location[2] + 22)
# Paint frame
cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
# Wite a name
cv2.putText(image, match,
(face_location[3] + 10, face_location[2] + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200, 200, 200),
FONT_THICKNESS)
# Show image
cv2.imshow(filename, image)
cv2.waitKey(0)
cv2.destroyWindow(filename)
dir = os.getcwd()
fname = ut.m_name(dir)
cimg = ut.m_color(fname, 8000, 16000, pmin=0.01, pmax=0.97)
cv2.imshow('color', cimg)
print 'now image "color" is xmin 8000 xmax 16000'
loop_flag = True
while loop_flag:
xmin, xmax = raw_input(
'Please write xmin and xmax or if you want to save, please write xmin "0" '
).split()
x = [xmin, xmax]
if xmin == '0':
while True:
YorN = raw_input('save the image y/n? ')
if YorN == 'y':
cv2.imwrite('../' + fname + '.tif', cimg)
loop_flag = False
break
elif YorN == 'n':
loop_flag = False
break
else:
print 'Please enter y/n'
else:
xmin = int(x[0])
xmax = int(x[1])
cimg2 = ut.m_color(fname, xmin, xmax, pmin=0.01, pmax=0.97)
cv2.destroyWindow('color2')
cv2.imshow('color2', cimg2)
cv2.circle(frame,(xlmh,ylmh),radius,(0,255,127),-1)
#right middle
xrmh=int(x+0.65*w)
yrmh=int(y+0.07*h)
radius=int(0.15*w)
cv2.circle(frame,(xrmh,yrmh),radius,(240,32,160),-1)
#middle
xmh=int(x+0.5*w)
ymh=int(y+0.05*h)
radius=int(0.15*w)
cv2.circle(frame,(xmh,ymh),radius,(0,0,255),-1)
cv2.namedWindow('Faces found')
cv2.imshow("Faces found", frame)
if cv2.waitKey(1)> 10:
videoLoop = False
cv2.destroyWindow('Faces found')
break
cam.release()
cv2.destroyAllWindows()
from PIL import Image
#Open image from the directories and returns it's histogram's
for j in (1, 104):
h1 = Image.open(r"C:\Users\dell\Desktop\DATASET\shot" + str(j) +
".jpg").histogram()
h2 = Image.open(r"C:\Users\dell\Desktop\SHOTS\shot.jpg").histogram()
#Finding rms value of the two images opened before
rms = math.sqrt(
reduce(operator.add, map(lambda a, b:
(a - b)**2, h1, h2)) / len(h1))
#print(int(rms))
#If the RMS value of the images are under our limit
if (rms < 450):
print("Accident")
cv2.imwrite(r"C:\Users\dell\Desktop\email\accident.jpg", frame)
SendMail(accident.jpg)
#Updates the frames
t_minus = t
t = t_plus
t_plus = cv2.cvtColor(cam.read()[1], cv2.COLOR_RGB2GRAY)
#Destroys the window after key press
key = cv2.waitKey(10)
if key == ord("q"):
cv2.destroyWindow(winName)
break
def __del__(self):
cv2.destroyWindow(self._window_name)
ret, frame = capture.read()
if ret == True:
cv2.imshow('My Camera', frame)
key = cv2.waitKey(10)
if key == 27:
flag = 1
break
if key == ord('x'):
filename = "face.jpg"
cv2.imwrite(filename, frame)
print('正在进行认证')
break
del (capture)
cv2.destroyWindow("camera")
if flag == 0:
url = 'https://api-cn.faceplusplus.com/facepp/v3/search'
payload = {
'api_key': 'XXX',
'api_secret': 'XXX',
'faceset_token': '7f0cc482207099671fc446129b8453ce'
}
files = {'image_file': open('face.jpg', 'rb')}
r = requests.post(url, files=files, data=payload)
data = json.loads(r.text)
print(data)
print(r.text)
# print(data["results"][0]["face_token"]=='dc83bd034ef87241e8fcb99bf088ed5f')
# if data["results"][0]["face_token"] == "ba24b26bc85ce3048a4e0b6ebee4acb2" and data["results"][0]["confidence"]>=data["thresholds"]["1e-5"]:
if data["results"][0][
def trackDrone(self):
# Start video thread
self.read_video_thread.start()
# Reset the counter so the time from when the sync light is detected is the same for both videos
self.current_frame = 0
frame = self.frame_queue.get(block=True)
# Here I resize the frame to 40% of its size, so the entire frame can be displayed onto the screen
# The 4k footage is too large for my laptop screen, so this scales it down
resizedFrame = self.rescale_frame(frame, 30)
# Ask the user to draw a box around the drone
bbox = cv2.selectROI("Select Drone", resizedFrame, False)
# Reposition the bounding box from 40% resolution to 100% resolution
bbox = self.resize_bbox(bbox, 3.33)
cv2.destroyWindow("Select Drone")
# Initialize tracker with first frame and bounding box
ok = self.tracker.init(frame, bbox)
# This while loop will run until the footage is finished, processing all of the drone footage
while True:
# Check if queue is empty but the read thread for the video is not done
if self.frame_queue.empty() and not self.done_reading:
# If so, sleep for a second for the producer to catch up and try again
time.sleep(1)
print("slept for 1 second")
continue
# Check if queue is empty and the read thread for video 1 is done
if self.frame_queue.empty() and self.done_reading:
# If so, destroy the window and break out of loop
cv2.destroyWindow("Tracking")
print("done tracking this drone at " + str(tm) + " seconds")
break
# Get frame from the frame queue
frame = self.frame_queue.get(block=True)
# Start timer
timer = cv2.getTickCount()
# Update tracker
ok, bbox = self.tracker.update(frame)
# Calculate Frames per second (FPS)
fps = cv2.getTickFrequency() / (cv2.getTickCount() - timer)
tm = self.current_frame / 30
# Draw bounding box
if ok:
# Tracking success
p1 = (int(bbox[0]), int(bbox[1]))
p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
if self.current_frame % 15 == 0:
x_coord = bbox[0] + bbox[2] / 2
y_coord = bbox[1] + bbox[3] / 2
print("drone coordinate at time " + str(tm) + ": [" +
str(x_coord) + ", " + str(y_coord) + "] (" +
str((x_coord / 3860) * 15) + ", " +
str((y_coord / 2160) * 10) + ")")
self.data_points.append((x_coord, y_coord, tm))
cv2.rectangle(frame, p1, p2, (255, 0, 0), 2, 1)
else:
# Tracking failure
cv2.putText(frame, "Tracking failure detected", (100, 80),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
skip = 5
# This will loop until the drone is reselected, and creates a new tracker that will
# continue to track the drone
while True:
# Rescale the frame so the user can see the entire frame to reselect the drone
resizedFrame = self.rescale_frame(frame, 30)
# Ask the user to draw a box around the drone
bbox = cv2.selectROI("Reselect Drone", resizedFrame, False)
# Reposition the bounding box from 40% resolution to 100% resolution
bbox = self.resize_bbox(bbox, 3.33)
# Close the ROI selection window
cv2.destroyWindow("Reselect Drone")
# This will skip 1 second of video if the drone is out of frame,
# which is detected if the user presses escape on the selectROI function
if bbox[0] == 0.0 and bbox[1] == 0.0 and bbox[
2] == 0.0 and bbox[3] == 0.0:
print("skipping " + str(skip) + " frames")
for i in range(1, skip):
if self.current_frame % 15 == 0:
tm = self.current_frame / 30
self.data_points.append((None, None, tm))
# If the video has totally been processed, exit the function
# Kind of a hacky way to do it, but I need to break out of 3 loops here
# so returning is easier than functionalizing the small parts of this giant function
if self.frame_queue.empty() and self.done_reading:
self.read_video_thread.join()
return self.data_points
frame = self.frame_queue.get(block=True)
self.current_frame += 1
if skip == 5:
skip = 15
elif skip == 15:
skip = 30
elif skip == 30:
skip = 60
elif skip == 60:
skip = 90
# else:
# skip += 30
# If the user selected a bounding box, create a new tracker
# and continue to track the drone
else:
# Creates a new KCF tracker (this is due to the update function not working
# the same in python as it does in C++: in C++, we could simply update the
# tracker with the new bounding box instead of creating a new one, but this
# is a known limitation in python's implementation of OpenCV and this is my workaround)
self.tracker = cv2.TrackerKCF_create()
# Initialize the tracker with the newly selected bounding box
ok = self.tracker.init(frame, bbox)
break
# Display tracker type on frame
cv2.putText(frame, self.tracker_type + " Tracker", (100, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
# Display FPS on frame
cv2.putText(frame, "FPS : " + str(int(fps)), (100, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 170, 50), 2)
resizedFrame = self.rescale_frame(frame, 20)
# Display result
cv2.imshow("Tracking", resizedFrame)
self.current_frame += 1
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
break
# Allows the user to reset the bounding box in the case that the tracker
# begins to track a different object or isn't tracking the drone accurately
# enough
elif "r" == chr(k):
# Rescale the frame so the user can see the entire frame to reselect the drone
resizedFrame = self.rescale_frame(frame, 40)
# Ask the user to draw a box around the drone
bbox = cv2.selectROI("Reselect Drone", resizedFrame, False)
# Reposition the bounding box from 40% resolution to 100% resolution
bbox = self.resize_bbox(bbox, 2.5)
# Close the ROI selection window
cv2.destroyWindow("Reselect Drone")
# Creates a new KCF tracker (this is due to the update function not working
# the same in python as it does in C++: in C++, we could simply update the
# tracker with the new bounding box instead of creating a new one, but this
# is a known limitation in python's implementation of OpenCV and this is my workaround)
self.tracker = cv2.TrackerKCF_create()
# Initialize the tracker with the newly selected bounding box
ok = self.tracker.init(frame, bbox)
self.read_video_thread.join()
return self.data_points
def run(source=0, dispLoc=False):
# Create the VideoCapture object
cam = cv2.VideoCapture(source)
# If Camera Device is not opened, exit the program
if not cam.isOpened():
print "Video device or file couldn't be opened"
exit()
print "Press key `p` to pause the video to start tracking"
while True:
# Retrieve an image and Display it.
retval, img = cam.read()
if not retval:
print "Cannot capture frame device"
exit()
if (cv2.waitKey(10) == ord('p')):
break
cv2.namedWindow("Image", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
cv2.destroyWindow("Image")
# Co-ordinates of objects to be tracked
# will be stored in a list named `points`
points = get_points.run(img, multi=True)
if not points:
print "ERROR: No object to be tracked."
exit()
cv2.namedWindow("Image", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
# Initial co-ordinates of the object to be tracked
# Create the tracker object
tracker = [dlib.correlation_tracker() for _ in xrange(len(points))]
# Provide the tracker the initial position of the object
[
tracker[i].start_track(img, dlib.rectangle(*rect))
for i, rect in enumerate(points)
]
while True:
# Read frame from device or file
retval, img = cam.read()
if not retval:
print "Cannot capture frame device | CODE TERMINATION :( "
exit()
# Update the tracker
for i in xrange(len(tracker)):
tracker[i].update(img)
# Get the position of th object, draw a
# bounding box around it and display it.
rect = tracker[i].get_position()
pt1 = (int(rect.left()), int(rect.top()))
pt2 = (int(rect.right()), int(rect.bottom()))
cv2.rectangle(img, pt1, pt2, (255, 255, 255), 3)
print "Object {} tracked at [{}, {}] \r".format(i, pt1, pt2),
if dispLoc:
loc = (int(rect.left()), int(rect.top() - 20))
txt = "Object tracked at [{}, {}]".format(pt1, pt2)
cv2.putText(img, txt, loc, cv2.FONT_HERSHEY_SIMPLEX, .5,
(255, 255, 255), 1)
cv2.namedWindow("Image", cv2.WINDOW_NORMAL)
cv2.imshow("Image", img)
# Continue until the user presses ESC key
if cv2.waitKey(1) == 27:
break
# Relase the VideoCapture object
cam.release()
with anki_vector.Robot() as robot:
robot.camera.init_camera_feed()
robot.behavior.set_head_angle(degrees(25.0))
robot.behavior.set_lift_height(0.0)
while robot.camera.image_streaming_enabled():
# Perform some pre-processing steps to minimize noise and simplify the processing steps
img = cv2.cvtColor(np.array(robot.camera.latest_image.raw_image),
cv2.COLOR_RGB2BGR)
img = cv2.flip(img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
#Select the ROI and get the average background of it over the first 40 frames
if num_frames < 40:
if num_frames == 0:
x1, y1, x2, y2 = cv2.selectROI('ROI selector', img, False)
cv2.destroyWindow('ROI selector')
print('Calibrating Background...\nDo not move the camera')
gray = gray[int(y1):int(y1 + y2), int(x1):int(x1 + x2)]
get_background(gray)
num_frames += 1
continue
gray = gray[int(y1):int(y1 + y2), int(x1):int(x1 + x2)]
cv2.rectangle(img, (x1 + x2, y1), (x1, y1 + y2), (0, 255, 0), 2)
#Peform the background subtraction so we can extract the hand
mask, hand_contour = background_sub(gray)
#Check to see if the area is a certain size, otherwise it will recognize random noise
if hand_contour is not None and hand_contour.sum() > 11000:
#Identify the finger contours, this is performed using a circle technique is this paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.454.3689&rep=rep1&type=pdf
finger_contours, y_bar, circumference = get_finger_contours(
hand_contour)
#Count the number of finger contours if they're at least 25% of the circuference of the circle and excluding the wrist
def showfrm(self):
# initializing file variable
self.fi = open('D:/Folder/main/!!Nirma/Sem_4/PSC/project/log.txt', 'a')
# Log entry
x = datetime.datetime.now()
ope = 'Started'
stri = '\n' + str(x.date()) + ' ' + str(x.hour) + ':' + str(x.minute) + ':' + str(x.second) + ' ' + ope
self.fi.write(stri)
# Log Entry
ope = 'WebCam On'
stri = '\n' + str(x.date()) + ' ' + str(x.hour) + ':' + str(x.minute) + ':' + str(x.second) + ' ' + ope
self.fi.write(stri)
# Method call to make elements visible
self.AllShow()
# Hide the button which will not be in use now
self.btstart.hide()
# display the button which will be used now on
self.bt5.show()
# initializing the file to determine parts of face
p = "C:/Users/dhruv/AppData/Local/Programs/Python/Python38/shape_predictor_68_face_landmarks.dat"
# Returns Returns the default face detector.
detector = dlib.get_frontal_face_detector()
# take in the input of face image and returns the set of point locations that define the face
predictor = dlib.shape_predictor(p)
# cv2 method to access the WebCam, will not work if the WebCam is occupied
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
# haar file that contains the features of face
face_cascade = cv2.CascadeClassifier("C:/Users/dhruv/AppData/Local/Programs/Python/Python38/Cascade/haarcascade_frontalface_default.xml")
while True:
#read the image from WebCam
check, freeframe = cap.read()
# use copy method to create the copy of the WebCam frame ; not using the '=' because,
# the second variable is just pointing the address of the first variable
frame = copy.copy(freeframe)
# converting to gray image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# returns the array that contain screen coordinates where face is detected
rects = detector(gray, 0)
# again copying the frame
image = copy.copy(frame)
# face detection by compare the image with the features
# if you find the inaccuracy in detection of face, try re adjusting the scaleFactor between 1.0 to 2.0
faces = face_cascade.detectMultiScale(frame, scaleFactor=1.2,minNeighbors=5)
# draw rectangle around the face
for x, y, w, h in faces:
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (self.b, self.g, self.r), 3)
for (i, rect) in enumerate(rects):
shape = predictor(gray, rect)
# this will convert the encoded facial landmarks from face_utils to numPy array
shape = face_utils.shape_to_np(shape)
# print(shape)
for (x, y) in shape:
# as per the desired part to be detected, re format the startind & endind to specifically locate a part of list of array
if self.startind < self.dotcount <= self.endind:
# draw dot/point
cv2.circle(image, (x, y), 2, (self.b, self.g, self.r), -1)
else:
pass
#loop counter
self.dotcount += 1
self.dotcount = 1
# condition checking
if self.showsq == 0 and self.showdot == 0:
cv2.imshow("WebCam", freeframe)
cv2.destroyWindow("Mapping")
cv2.destroyWindow("Detection")
else:
cv2.destroyWindow("WebCam")
if self.showdot == 1:
cv2.imshow("Mapping", image) # face maping
self.captureframeDot = image
# enabling the required elements
self.pbMouth.setEnabled(True)
self.pbEyeBrows.setEnabled(True)
self.pbEyes.setEnabled(True)
self.pbNose.setEnabled(True)
self.pbJaw.setEnabled(True)
self.sqRed.setEnabled(True)
self.sqGreen.setEnabled(True)
self.sqBlue.setEnabled(True)
else:
# disabling the non required elements
cv2.destroyWindow("Mapping")
self.pbMouth.setEnabled(False)
self.pbEyeBrows.setEnabled(False)
self.pbEyes.setEnabled(False)
self.pbNose.setEnabled(False)
self.pbJaw.setEnabled(False)
if self.showsq == 0:
self.sqRed.setEnabled(False)
self.sqGreen.setEnabled(False)
self.sqBlue.setEnabled(False)
if self.showsq == 1:
cv2.imshow("Detection", frame) # face detection
self.captureframeSq = frame
# enabling the required elements
self.sqRed.setEnabled(True)
self.sqGreen.setEnabled(True)
self.sqBlue.setEnabled(True)
else:
cv2.destroyWindow("Detection")
if self.showdot == 0:
self.sqRed.setEnabled(False)
self.sqGreen.setEnabled(False)
self.sqBlue.setEnabled(False)
if self.showsq == 0 and self.showdot == 0:
# disabling the non required elements
self.pbMouth.setEnabled(False)
self.pbEyeBrows.setEnabled(False)
self.pbEyes.setEnabled(False)
self.pbNose.setEnabled(False)
self.pbJaw.setEnabled(False)
self.sqRed.setEnabled(False)
self.sqGreen.setEnabled(False)
self.sqBlue.setEnabled(False)
# confirming for existence of window
if cv2.getWindowProperty("Mapping", 0) == -1.0:
self.showdot = 0
if cv2.getWindowProperty("Detection", 0) == -1.0:
self.showsq = 0
cv2.waitKey(1)
# exit condition
if self.counter == 0:
# print('Q pressed')
break
# releasing the window
cap.release()
cv2.destroyAllWindows()
self.counter = 1
import cv2
faceCascade = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml")
img = cv2.imread('./lena.png')
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(imgGray, 1.1, 4)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow("Result", img)
cv2.waitKey(0)
cv2.destroyWindow("Result")
def show_cv(img, name='image'):
cv2.namedWindow(name, cv2.WINDOW_NORMAL)
cv2.resizeWindow(name, 600, 400)
cv2.imshow(name, img)
cv2.waitKey()
cv2.destroyWindow(name)
roi_color=frame[y:y+h,x:x+w]
id_,conf=recognizer.predict(roi_gray)
print(conf)
if conf>=67:
#print(id_)
print(labels[id_])
img=cv2.imread('C:/Users/dania/Desktop/python/Face Recognition/Smile.png',1)
img2=cv2.imread('C:/Users/dania/Desktop/python/Face Recognition/Poker.png',1)
cv2.imshow('smile',img)
font=cv2.FONT_HERSHEY_SIMPLEX
name=labels[id_]
color=(255,255,255)
stroke=2
cv2.putText(frame,name,(x,y),font,1,color,stroke,cv2.LINE_AA)
cv2.destroyWindow('Poker')
else:
cv2.destroyWindow('smile')
cv2.imshow('Poker',img2)
img_item="my-image1.png"
cv2.imwrite(img_item,roi_color)
color=(255,0,0) ##BGR
stroke=2
width=x+w
height=y+h
#print(str(width)+" "+str(height))
cv2.rectangle(frame,(x,y),(width,height),color,stroke)
cv2.imshow('frame',frame)
for file in backgrounds_files:
current_file = file
img = cv2.imread('Backgrounds/'+ file)
img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
backgroud_images.append([img, file])
# img = cv2.resize(img, None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)
# img = np.zeros((512,512,3), np.uint8)
if file not in overlay_loc or force_mark:
cv2.namedWindow(file)
cv2.setMouseCallback(file,get_point)
while(1):
cv2.imshow(file ,cv2.cvtColor(img,cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
cv2.destroyWindow(file)
break
f = open("overlay_locations.p","wb")
pickle.dump(overlay_loc,f)
f.close()
gif = imageio.mimread("delaware.gif")
duration = Image.open("delaware.gif").info['duration']
# print(len(gif)/Image.open("delaware.gif").info['duration'])
nums = len(gif)
# new_img = img
def show(img):
cv2.imshow('opencv_sift_function', img)
cv2.waitKey(0)
cv2.destroyWindow('opencv_sift_function')
while True:
ret, image = video.read()
locations = face_recognition.face_locations(image, model=MODEL) # detect all faces on one image
encodings = face_recognition.face_encodings(image, locations)
for face_encoding, face_location in zip(encodings, locations):
results = face_recognition.compare_faces(known_faces,face_encoding, TOLERANCE)
match = None
if True in results:
match = known_names[results.index(True)]
print(f"Match found: {match}")
top_left = (face_location[3], face_location[0])
bottom_right = (face_location[1], face_location[2])
color = [0,255,0]
cv2.rectangle(image, top_left, bottom_right, color, FRAME_THICKNESS)
top_left = (face_location[3], face_location[2])
bottom_right = (face_location[1], face_location[2]+22)
cv2.rectangle(image, top_left, bottom_right, color, cv2.FILLED)
cv2.putText(image, match, (face_location[3]+10, face_location[2]+15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (200,200,200),FONT_THICKNESS)
cv2.imshow("FaceRecognition", image)
if cv2.waitKey(1) & 0xFF == ord("q"):
cv2.destroyWindow("FaceRecognition")
# cv2.waitKey(0)
# cv2.destroyWindow(filename)
key = cv2.waitKey(20)
if key == 27: # exit on ESC
break
# depthimagefile+=1;
# scaler = 255
# depthArray = depthArray*scaler
# filename = 'C:/ADEV/WinPython-64bit-2.7.10.3/python-2.7.10.amd64/Scripts/ADEV/depthImages/' + str(depthimagefile) + '.tiff'
# cv2.imwrite(filename, depthArray)
#
#
#
#
#
cv2.destroyWindow("Left")
cv2.destroyWindow("Right")
cv2.destroyWindow('DepthImage')
# for(pixel in depthArray):
# pixel =
# A = ((xvalue/depth.getCols())*depthMaxHorzAngle)-90
# #if distance values aren't along the hypotenuse add next line
# #x = pixel*cos()
# #otherwise
# x=pixel
#
# # for left webcam
# if(A>0):
# C=A+90
# else:
def close(self):
self.cam.release()
cv2.destroyWindow(self.window_name)
red_lower = np.array([136, 87, 111])
red_upper = np.array([180, 255, 255])
prev_y = 0
while True:
rate, frame = capture.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, red_lower, red_upper)
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
area = cv2.contourArea(c)
if area > 300:
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if y < prev_y:
pyautogui.press('space')
prev_y = y
cv2.imshow('Mask', mask)
cv2.imshow("Frame", frame)
if cv2.waitKey(10) == ord('c'):
break
capture.release()
cv2.destroyWindow()
def cv2ShowWait(img, title='Image'):
cv2.imshow(title, img)
cv2.waitKey(0)
cv2.destroyWindow(title)
