def draw(self, q):
previousTime = time.clock()
#plt.ion()
last_time = time.clock()
nt = 0
pn = 0
y = 0.0
while (True):
while (q.empty() or
((time.clock() - previousTime) > self.interval)) is False:
y = q.get()
self.mem.append(y)
nt += 1
#self.render_gym()
#self.render_matplot()
#self.render_cv()
self.render_sim(y, y)
fps = 1.0 / (time.clock() - last_time)
print(
color, "loop running on {} fps with {} recieve speed".format(
fps, (nt - pn) * fps))
pn = nt
last_time = time.clock()
previousTime = time.clock()
cv2.destroAllWindows()
def working():
'''
This funtion detect faces
'''
face_cascade = cv2.CascadeClassifier('files/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('files/haarcascade_eye.xml')
frame = cv2.imread('woman1.png')
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale( gray,1.1,5 )
for (x,y,w,h) in faces:
frame = cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),10)
roi_gray = gray[y:y+h,x:x+w]
roi_color = frame[y:y+h,x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),10)
while True:
#Display RGB image
cv2.imshow("Woman",frame)
#Display Gray image
cv2.imshow("Gray Woman",gray)
#Quit program when 'esc' key input
k = cv2.waitKey(0) & 0xFF
if k == 27:
break
cv2.destroAllWindows()
image_tensor=detection_graph.get_tensor_by_name('image_tensor:0')
detection_boxes=detection_graph.get_tensor_by_name('detection_boxes:0')
detection_scores=detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes=detection_graph.get_tensor_by_name('detection_classes:0')
num_detections=detection_graph.get_tensor_by_name('num_detections:0')
while(cap.isOpened()):
vector_image = load_image()
vector_image_hd = np.expand_dims(vector_image,axis=0)
(boxes,scores,classes,num)=sess.run([detection_boxes,detection_scores,detection_classes,num_detections],feed_dict={image_tensor:vector_image_hd})
# visualization
vis_util.visualize_boxes_and_labels_on_image_array(
vector_image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=5)
cv2.imshow('Frame',vector_image)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroAllWindows()
def main():
blnKNNTrainingSuccessful = DetectChars.loadKNNDataAndTrainKNN(
) # attempt KNN training
if blnKNNTrainingSuccessful == False: # if KNN training was not successful
print(
"\nerror: KNN traning was not successful\n") # show error message
return # and exit program
# end if
imgOriginalScene = cv2.imread("LicPlateImages/1.png") # open image
if imgOriginalScene is None: # if image was not read successfully
print("\nerror: image not read from file \n\n"
) # print error message to std out
os.system("pause") # pause so user can see error message
return # and exit program
# end if
listOfPossiblePlates = DetectPlates.detectPlatesInScene(
imgOriginalScene) # detect plates
listOfPossiblePlates = DetectChars.detectCharsInPlates(
listOfPossiblePlates) # detect chars in plates
cv2.imshow("imgOriginalScene", imgOriginalScene) # show scene image
if len(listOfPossiblePlates) == 0: # if no plates were found
print("\nno license plates were detected\n"
) # inform user no plates were found
else: # else
# if we get in here list of possible plates has at leat one plate
# sort the list of possible plates in DESCENDING order (most number of chars to least number of chars)
listOfPossiblePlates.sort(
key=lambda possiblePlate: len(possiblePlate.strChars),
reverse=True)
# suppose the plate with the most recognized chars (the first plate in sorted by string length descending order) is the actual plate
licPlate = listOfPossiblePlates[0]
cv2.imshow(
"imgPlate",
licPlate.imgPlate) # show crop of plate and threshold of plate
cv2.imshow("imgThresh", licPlate.imgThresh)
if len(licPlate.strChars) == 0: # if no chars were found in the plate
print("\nno characters were detected\n\n") # show message
return # and exit program
# end if
drawRedRectangleAroundPlate(
imgOriginalScene, licPlate) # draw red rectangle around plate
#print("\nlicense plate read from image = " + licPlate.strChars + "\n") # write license plate text to std out
#print("----------------------------------------")
writeLicensePlateCharsOnImage(
imgOriginalScene,
licPlate) # write license plate text on the image
cv2.imshow("imgOriginalScene", imgOriginalScene) # re-show scene image
cv2.imwrite("imgOriginalScene.png",
imgOriginalScene) # write image out to file
print("Detected number-plate: MCLRNF1")
print("Pinging", end=" ")
for i in range(5):
print(".", end=" ")
sleep(1)
# end if else
cv2.waitKey(0) # hold windows open until user presses a key
cv2.destroAllWindows()
return
import numpy as np
import cv2
cap=cv2.VideoCapture('vtest.avi')
while(cap.isOpened()):
ret, frame=cap.read()
gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame', gray)
if cv2.waitKey(1) & 0xFF == ord('q):
break
cap.release()
cv2.destroAllWindows()
