def draw_text(self, image, text, size, color, position):
'''
Zeichnet einen Text auf das Bild.
Parameter
---------
text : String
Anzuzeigender Text
size : Integer
Groesse des Textes
color : Tupel
Farbe des Textes >> (255,0,0)
position : Tupel
Position des Textes >> (x,y)
'''
if imutils.is_cv2():
cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.CV_AA)
elif imutils.is_cv3():
cv2.putText(image, text, position, cv2.FONT_HERSHEY_COMPLEX, size, color, 2, cv2.LINE_AA)
python类is_cv2()的实例源码
def extract_color_histogram(image, bins=(8, 8, 8)):
# extract a 3D color histogram from the HSV color space using
# the supplied number of `bins` per channel
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hist = cv2.calcHist([hsv], [0, 1, 2], None, bins, [0, 180, 0, 256, 0, 256])
# handle normalizing the histogram if we are using OpenCV 2.4.X
if imutils.is_cv2():
hist = cv2.normalize(hist)
# otherwise, perform "in place" normalization in OpenCV 3 (I
# personally hate the way this is done
else:
cv2.normalize(hist, hist)
# return the flattened histogram as the feature vector
return hist.flatten()
def run(self):
bytes=''
while not self.thread_cancelled:
try:
bytes+=self.stream.raw.read(1024)
a = bytes.find('\xff\xd8')
b = bytes.find('\xff\xd9')
if a!=-1 and b!=-1:
jpg = bytes[a:b+2]
bytes= bytes[b+2:]
img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR)
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
#lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8)
#upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8)
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153]))
# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
#### blurred = cv2.GaussianBlur(mask, (5, 5), 0)
blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT)
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cv2.filterSpeckles(mask, 0, 100, 25)
## cv2.filterSpeckles(mask, 0, 50, 25)
## cv2.filterSpeckles(mask, 0, 100, 100)
for c in cnts:
M = cv2.moments(c)
if int(M["m00"]) != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
(cX, cY) = (0, 0)
print(cX, cY)
cv2.drawContours(res, [c], -1, (0, 255, 0), 2)
cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1)
# table.putNumber("center X", cX)
cv2.imshow('img',img)
cv2.imshow('mask',mask)
cv2.imshow('Final',res)
cv2.imshow('cam',img)
#sd.putNumber('Center X', cX) ##send the x value of the center
#sd.putNumber('Center Y', cY) ##send the y value of the center
## print(sd.getNumber('Center Y'), sd.getNumber('Center X'))
if cv2.waitKey(1) ==27:
exit(0)
except ThreadError:
self.thread_cancelled = True
def run(self):
bytes=''
while not self.thread_cancelled: ####see lines 18, 80, 88 ....
try:
bytes+=self.stream.raw.read(1024) ##limit max bytes read in 1 itteration? need to read more on this
a = bytes.find('\xff\xd8')##find start of stream of data
b = bytes.find('\xff\xd9')##find our end of data stream
if a!=-1 and b!=-1: ##so as long as we have a stream of data....do the following
jpg = bytes[a:b+2] ##converts to image or a specific variable...
bytes= bytes[b+2:]
img = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),cv2.IMREAD_COLOR) ##decode the data
# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) ##converting color format for easier proccessing/ math
# define range of blue color in HSV
#lower_blue = np.array([self.L_RED, self.L_GREEN, self.L_BLUE], np.uint8)
#upper_blue = np.array([self.U_RED, self.U_GREEN, self.L_BLUE], np.uint8)
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, np.array([53,187,37]), np.array([97,244,153])) ##get colors in the range of these HSV values
# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
blurred = cv2.boxFilter(mask, 0, (7, 7), mask, (-1, -1), False, cv2.BORDER_DEFAULT) ##the next few line create outlines and
thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1] ##remove any noise
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) #find countors
cnts = cnts[0] if imutils.is_cv2() else cnts[1]
cv2.filterSpeckles(mask, 0, 100, 25) ##remove speckles aka random dots and white noise
for c in cnts:
M = cv2.moments(c)
if int(M["m00"]) != 0: ##Checks for division by zero
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
else:
(cX, cY) = (0, 0)
cv2.drawContours(res, [c], -1, (0, 255, 0), 2) ##draw box/highlighting
cv2.circle(res, (cX, cY), 7, (255, 255, 255), 1) ##draw box/highlighting
##Try-Catch for appending cX to table
try:
self.table.putNumber('centerX', cX) ##Adds cX to the networktables
except KeyError:
print("centerX failed.")
cv2.imshow('img',img) ##display original image
cv2.imshow('mask',mask) ##display masked image
cv2.imshow('Final',res) ##show final image
cv2.imshow('cam',img) ##see line 71/comments
if cv2.waitKey(1) ==27: ##now we close if esc key is pressed
exit(0)
except ThreadError:
self.thread_cancelled = True
