config_tracker.klt.templateRadius = 5
# TODO use SURF tracker for this example as it will be more stable
video_mosaic = pb.FactoryVideoMosaic(np.uint8).mosaic(config_tracker)
video_mosaic.configure(1000, 500, scale=0.5)
image_input = video_mosaic.get_image_type().create_boof_image(0, 0)
boof_color = None
while True:
# Capture sequence frame-by-frame
ret, frame = cap.read()
# Convert it into a boofcv image
boof_color = pb.ndarray_to_boof(frame, boof_color)
# Convert it into the image type required by the tracker
pb.convert_boof_image(boof_color, image_input)
# Track the point objects
time0 = time.time() * 1000.0
if not video_mosaic.process(image_input):
print("mosaic failed!")
video_mosaic.reset()
continue
time1 = time.time() * 1000.0
# Get the mosaic image and display the results
boof_mosaic = video_mosaic.get_stitched_image()
ndarray_mosaic = pb.boof_to_ndarray(boof_mosaic)
qrResult = ''
pb.init_memmap()
detector = pb.FactoryFiducial(np.uint8).qrcode()
print('Demo will work')
cnt = 0
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
# frame = cv2.flip(frame,-1)
# # QR Code Detector
# if not findQR:
frame_mono = cv2.cvtColor(np.uint8(frame), cv2.COLOR_BGR2GRAY)
boof_img = pb.ndarray_to_boof(frame_mono)
# boof_img = pb.load_single_band( 'path to image', np.uint8)
start = time.time_ns()
detector.detect(boof_img)
if len(detector.detections) > 0:
end = time.time_ns()
print("running time is ", str(end - start))
print("find QR Code ")
findQR = True
qrResult = detector.detections[0].message
# print("QR Code is: {}".format(qrResult))
mask = detector.detections[0].mask_pattern
# print("mask", mask)
import numpy as np
import cv2
import pyboof as pb
# Enable use of memory mapped files for MUCH faster conversion of images between java and python
pb.init_memmap(5)
image_path = '../data/example/outdoors01.jpg'
# Can load an image using OpenCV then convert it into BoofCV
ndarray_img = cv2.imread(image_path, 0)
boof_cv = pb.ndarray_to_boof(ndarray_img)
# Can also use BoofCV to load the image directly
boof_gray = pb.load_single_band(image_path, np.uint8)
boof_color = pb.load_planar(image_path, np.uint8)
# Let's display all 3 of them in Java
# display the results in a single window as a list
image_list = [(boof_cv, "OpenCV"), (boof_gray, "Gray Scale"),
(boof_color, "Color")]
pb.swing.show_list(image_list, title="Images")
input("Press any key to exit")
import numpy as np
import cv2
import pyboof as pb
# Enable use of memory mapped files for MUCH faster conversion of images between java and python
pb.init_memmap(5)
image_path = '../data/example/outdoors01.jpg'
# Can load an image using OpenCV then convert it into BoofCV
ndarray_img = cv2.imread(image_path,0)
boof_cv = pb.ndarray_to_boof(ndarray_img)
# Can also use BoofCV to load the image directly
boof_gray = pb.load_single_band(image_path,np.uint8)
boof_color = pb.load_planar(image_path,np.uint8)
# Let's display all 3 of them in Java
# display the results in a single window as a list
image_list = [(boof_cv,"OpenCV"),
(boof_gray,"Gray Scale"),
(boof_color,"Color")]
pb.swing.show_list(image_list,title="Images")
raw_input("Press any key to exit")
# tracker = pb.FactoryTrackerObjectQuad(np.uint8).tld(pb.ConfigTld(False))
# initialize data structures
ret, frame = cap.read()
if not ret:
print "Failed to read frame"
exit(-1)
image_input = tracker.getImageType().create_boof_image(frame.shape[1], frame.shape[0])
while True:
# Capture frame-by-frame
ret, frame = cap.read()
time0 = int(round(time.time() * 1000))
# Convert it into a boofcv image
boof_color = pb.ndarray_to_boof(frame)
time1 = int(round(time.time() * 1000))
# Convert it into the image type required by the tracker
pb.convert_boof_image(boof_color,image_input)
time2 = int(round(time.time() * 1000))
print("time py to boof: "+str(time1-time0)+" boof to boof "+str(time2-time1))
if state == 1:
cv2.rectangle(frame, refPt[0], refPt[1], (0, 255, 0), 2)
elif state == 2:
pts_to_quad()
if not tracker.initialize(image_input,quad):
print "Initialization failed!"
state = 0
else:
