def goGetEm(): minRemovalScore = 0.0001 timeOut = 10 cleanThresh = 5 binNumber = 100 distanceWeight = 0.5 sizeWeight = 0.0 timeWeight = 0.5 weights = (distanceWeight, timeWeight, sizeWeight) writingToFiles = True distDev = 200 timeDev = 0.34 sizeDev = 0.25 devs = (distDev, timeDev, sizeDev) framesback = 2 variables = [minRemovalScore, timeOut, cleanThresh, binNumber, weights, writingToFiles, devs, framesback] vid = Video(0, variables) # vidFile = "outvid.avi" # csvFile = "variable.csv" # if writingToFiles: # vid.openVidWrite(vidFile) # vid.openCSVWrite(csvFile) while (True): vid.readFrame() vid.findFaces() vid.display() # vid.writeToVideo() # exit on escape key key = cv2.waitKey(1) if key == 27: break vid.endWindow()
def main(video=0, dowsample=True):
print('QUAD Vision, Python:', version, ' OpenCV:', cv2.__version__)
vid = Video(video,
'ORB Detection testing',
delay=1,
fast=False,
skip=20,
downsample=downsample)
detector = Detector()
if ros_mode:
import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
from quad.msg import Target_coordinates
pub_detection = rospy.Publisher('detection',
Target_coordinates,
queue_size=5)
pub_frame = rospy.Publisher('annotated_frame',
msg.image,
queue_size=15)
rospy.init_node('detection', anonymous=True)
rospy.init_node('annotated_frame', anonymous=True)
# Detection profiles
detector.load_profile(profiles.idea_orange)
# detector.load_profile(profiles.quadbox_black)
# detector.load_profile(profiles.quadbox_white)
# detector.load_profile(profiles.idea_red)
# detector.load_profile(profiles.sim_drone)
vid.skip(20)
t = Timer('Detection')
while vid.next():
frame = vid.img
t.start() # start timer
img, confidence, x, y = detector.detect(frame)
print('Detection:', confidence, x, y)
t.end()
vid.display(img)
if ros_mode:
detection = Target_coordinates()
detection.confidence = confidence
detection.x = x
detection.y = y
try:
pub_detection.publish(detection)
except CvBridgeError as e:
print(e)
# ros_img = CvBridge.cv2_to_imgmsg(img, encoding="passthrough")
# pub_frame.publish(ros_img)
vid.close()
t.output()
def main():
vid = Video(video, 'Haar Cascade detection')
vid.overlay = True
cascade_file = 'data/idea_cascade.xml'
detector = CascadeDetector(cascade_file)
t = Timer('Cascade Detection')
while vid.next():
t.start()
img = vid.img
message = []
img = preprocess(img)
img, confidence = detector.detect(img)
t.end()
message.append('%0.2f' % confidence)
vid.display(img, message)
t.output()
def goGetEm():
minRemovalScore = 0.0001
timeOut = 10
cleanThresh = 5
binNumber = 100
distanceWeight = 1
timeWeight = 1
sizeWeight = 1
#weights = (distanceWeight, timeWeight, sizeWeight)
#variables = [minRemovalScore, timeOut, cleanThresh, binNumber, weights]
variables = (0.25, 10, 5, 100, (1,1,1), False, (200,0.34,0.25), 2)
#vid = Video("Slightmovement.MP4", [0,1,0.1,0.1,1000,0])
vid = Video(0, variables)
while (True):
vid.readFrame()
vid.findFaces()
vid.display()
# exit on escape key
key = cv2.waitKey(20)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
vid.endWindow()
