def GUI():
global frame, goon, win
for key, frame in autoStream():
cv.imshow('cam', frame)
if win is not None:
cv.imshow('inception', win)
win = None
goon = False
z1 = np.zeros([s, w2])
z2 = np.zeros([s, s - w - w2])
y = np.hstack([z1, x, z2])
if h2 > 0:
z1 = np.zeros([h2, s])
z2 = np.zeros([s - h - h2, s])
y = np.vstack([z1, x, z2])
y = cv.resize(y, (20, 20)) / 255
mx, my = center(y)
H = np.array([[1., 0, 4 - (mx - 9.5)], [0, 1, 4 - (my - 9.5)]])
return cv.warpAffine(y, H, (28, 28))
black = True
for key, frame in autoStream():
cosas = extractThings(frame)
nor = [adaptsize(c) for (x, y), c in cosas]
loc = [c[0] for c in cosas]
t0 = time.time()
if nor:
clas, prob = classify(nor)
else:
clas, prob = [], []
t1 = time.time()
for (x, y), label, pr in zip(loc, clas, prob):
import numpy as np
from umucv.stream import autoStream
from umucv.util import putText
import time
corners_params = dict(maxCorners=500,
qualityLevel=0.1,
minDistance=10,
blockSize=7)
lk_params = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10,
0.03))
for n, (key, frame) in enumerate(autoStream()):
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# resetear el punto inicial del tracking
if n == 0 or key == ord('c'):
corners = cv.goodFeaturesToTrack(gray, **corners_params).reshape(-1, 2)
nextPts = corners
prevgray = gray
t0 = time.time()
# encontramos la posición siguiente a partir de la anterior
nextPts, status, err = cv.calcOpticalFlowPyrLK(prevgray, gray, nextPts,
None, **lk_params)
prevgray = gray
def rots(c):
return [np.roll(c, k, 0) for k in range(len(c))]
def bestPose(K, view, model):
poses = [Pose(K, v.astype(float), model) for v in rots(view)]
sp = sorted(poses, key=lambda p: p.rms)
return sp[0]
marker = np.array([[0, 0, 0], [0, 1, 0], [0.5, 1, 0], [0.5, 0.5, 0],
[1, 0.5, 0], [1, 0, 0]])
stream = autoStream()
HEIGHT, WIDTH = next(stream)[1].shape[:2]
size = WIDTH, HEIGHT
print(size)
f = 1.7
K = kgen(size, f) # fov aprox 60 degree
print(K)
## Create a GL View widget to display data
app = QtGui.QApplication([])
win = gl.GLViewWidget()
win.show()
win.setWindowTitle('pose')
win.setCameraPosition(distance=20)
#!/usr/bin/env python
import cv2 as cv
import time
from umucv.util import putText
from umucv.stream import autoStream
hog = cv.HOGDescriptor()
hog.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetector())
MULTISCALE = True
for key, image in autoStream():
if key == ord('m'):
MULTISCALE = not MULTISCALE
t0 = time.time()
if MULTISCALE:
(rects, weights) = hog.detectMultiScale(image,
winStride=(4, 4),
padding=(8, 8),
scale=1.1)
else:
(rects, weights) = hog.detect(image, winStride=(4, 4), padding=(8, 8))
t1 = time.time()
if len(rects) > 0:
for rect, p in zip(rects, weights.flatten()):
if MULTISCALE:
x, y, w, h = rect
# invertimos la imagen para evitar el rectángulo exterior blanco
mod = extractContours(255 - rgb2gray(readrgb('shapes/trebol.png')))[0].reshape(
-1, 2)
imod = invar(mod)
model = np.zeros((200, 200), np.uint8)
cv.drawContours(model, [mod], -1, 255, 1)
cv.imshow('model', model)
def razonable(c):
return cv.arcLength(c, closed=True) >= 50
for (key, frame) in autoStream():
g = rgb2gray(frame)
# sería bueno eliminar contornos que tocan el borde de la imagen, etc.
contours = extractContours(g)
ok = [c for c in contours if razonable(c)]
found = [c for c in ok if norm(invar(c) - imod) < 0.15]
#print(len(contours), len(ok), len(found))
#cv.drawContours(frame, contours, -1, (255,128,128), 1)
#cv.drawContours(frame, ok, -1, (0,0,255), 1)
cv.drawContours(frame, found, -1, (0, 255, 0), cv.FILLED)
cv.imshow('shapes', frame)
#!/usr/bin/env python
# adaptado de
# http://dlib.net/train_object_detector.py.html
import sys
import dlib
import cv2 as cv
from umucv.stream import autoStream
detector = dlib.simple_object_detector(sys.argv[1])
# We can look at the HOG filter we learned.
win_det = dlib.image_window()
win_det.set_image(detector)
for key, img in autoStream():
dets = detector(img)
for k, d in enumerate(dets):
cv.rectangle(img, (d.left(), d.top()), (d.right(), d.bottom()),
(128, 128, 255), 3)
cv.imshow("object detection", img)
def __init__(self, xs, levels=16):
self.redu = 256 // levels
self.H = fil.gaussian_filter(hist(xs, self.redu), 1)
print(self.H.shape)
def __call__(self, img):
r, g, b = np.floor_divide(x, self.redu).transpose(2, 0, 1)
return self.H[r, g, b]
cv.namedWindow("input")
roi = ROI('input')
model = None
for key, x in autoStream():
if model is not None:
l = model(x)
maxl = l.max()
cv.imshow("likelihood",
np.clip((1 * l / maxl * 255), 0, 255).astype(np.uint8))
term_crit = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1)
ret, track_window = cv.CamShift(l, track_window, term_crit)
cv.ellipse(x, ret, (0, 128, 255), 2)
#pts = cv.boxPoints(ret).astype(int)
#cv.polylines(x,[pts],True, (0,128,255),2)
def fun():
global frame, goon, key
for key, frame in autoStream():
cv.imshow('cam', frame)
goon = False
## Create a GL View widget to display data
app = QtGui.QApplication([])
#w = gl.GLViewWidget()
w = KeyPressWindow()
w.sigKeyPress.connect(keyPressed)
w.show()
w.setWindowTitle('gray level surface')
w.setCameraPosition(distance=50)
## Add a grid to the view
g = gl.GLGridItem()
w.addItem(g)
source = autoStream()
def getframe():
key, frame = next(source)
return cv.cvtColor(frame, cv.COLOR_BGR2GRAY).astype(float) / 255
p = gl.GLSurfacePlotItem(z=getframe(),
shader='heightColor',
computeNormals=False,
smooth=False)
p.scale(20 / 640, 20 / 640, 10)
p.translate(-8, -10, 0)
w.addItem(p)
