def __init__(self, video_src, paused=False):
self.cap = video.create_capture(video_src)
_, self.frame = self.cap.read()
cv.imshow('frame', self.frame)
self.rect_sel = RectSelector('frame', self.onrect)
self.trackers = []
self.paused = paused
def __init__(self, src):
self.cap = video.create_capture(src, presets['book'])
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv.namedWindow('plane')
self.rect_sel = common.RectSelector('plane', self.on_rect)
def __init__(self, video_src):
self.cam = video.create_capture(video_src, presets['cube'])
_ret, self.frame = self.cam.read()
cv.namedWindow('camshift')
cv.setMouseCallback('camshift', self.onmouse)
self.selection = None
self.drag_start = None
self.show_backproj = False
self.track_window = None
args, video_src = getopt.getopt(sys.argv[1:], '',
['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
cascade_fn = args.get(
'--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade',
"../../data/haarcascades/haarcascade_eye.xml")
cascade = cv.CascadeClassifier(cascade_fn)
nested = cv.CascadeClassifier(nested_fn)
cam = create_capture(video_src,
fallback='synth:bg=../data/lena.jpg:noise=0.05')
while True:
ret, img = cam.read()
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
gray = cv.equalizeHist(gray)
t = clock()
rects = detect(gray, cascade)
vis = img.copy()
draw_rects(vis, rects, (0, 255, 0))
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect(roi.copy(), nested)
hsv_map = cv.cvtColor(hsv_map, cv.COLOR_HSV2BGR)
cv.imshow('hsv_map', hsv_map)
cv.namedWindow('hist', 0)
hist_scale = 10
def set_scale(val):
global hist_scale
hist_scale = val
cv.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
try:
fn = sys.argv[1]
except:
fn = 0
cam = video.create_capture(fn, fallback='synth:bg=../data/baboon.jpg:class=chess:noise=0.05')
while True:
flag, frame = cam.read()
cv.imshow('camera', frame)
small = cv.pyrDown(frame)
hsv = cv.cvtColor(small, cv.COLOR_BGR2HSV)
dark = hsv[...,2] < 32
hsv[dark] = 0
h = cv.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256])
h = np.clip(h*0.005*hist_scale, 0, 1)
vis = hsv_map*h[:,:,np.newaxis] / 255.0
cv.imshow('hist', vis)
def __init__(self, video_src):
self.cam = self.cam = video.create_capture(video_src, presets['book'])
self.p0 = None
self.use_ransac = True
img = levels[-1]
for lev_img in levels[-2::-1]:
img = cv.pyrUp(img, dstsize=getsize(lev_img))
img += lev_img
return np.uint8(np.clip(img, 0, 255))
if __name__ == '__main__':
import sys
print(__doc__)
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
leveln = 6
cv.namedWindow('level control')
for i in xrange(leveln):
cv.createTrackbar('%d' % i, 'level control', 5, 50, nothing)
while True:
ret, frame = cap.read()
pyr = build_lappyr(frame, leveln)
for i in xrange(leveln):
v = int(cv.getTrackbarPos('%d' % i, 'level control') / 5)
pyr[i] *= v
res = merge_lappyr(pyr)
-----
ESC - exit
'''
import cv2 as cv
from face import video
import sys
if __name__ == '__main__':
try:
video_src = sys.argv[1]
except:
video_src = 0
cam = video.create_capture(video_src)
mser = cv.MSER_create()
while True:
ret, img = cam.read()
if ret == 0:
break
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
vis = img.copy()
regions, _ = mser.detectRegions(gray)
hulls = [cv.convexHull(p.reshape(-1, 1, 2)) for p in regions]
cv.polylines(vis, hulls, 1, (0, 255, 0))
cv.imshow('img', vis)
if cv.waitKey(5) == 27:
def main():
try:
src = sys.argv[1]
except:
src = 0
cap = video.create_capture(src)
classifier_fn = 'digits_svm.dat'
if not os.path.exists(classifier_fn):
print('"%s" not found, run digits.py first' % classifier_fn)
return
if True:
model = cv.ml.SVM_load(classifier_fn)
else:
model = cv.ml.SVM_create()
model.load_(classifier_fn
) #Known bug: https://github.com/opencv/opencv/issues/4969
while True:
_ret, frame = cap.read()
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
bin = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C,
cv.THRESH_BINARY_INV, 31, 10)
bin = cv.medianBlur(bin, 3)
_, contours, heirs = cv.findContours(bin.copy(), cv.RETR_CCOMP,
cv.CHAIN_APPROX_SIMPLE)
try:
heirs = heirs[0]
except:
heirs = []
for cnt, heir in zip(contours, heirs):
_, _, _, outer_i = heir
if outer_i >= 0:
continue
x, y, w, h = cv.boundingRect(cnt)
if not (16 <= h <= 64 and w <= 1.2 * h):
continue
pad = max(h - w, 0)
x, w = x - (pad // 2), w + pad
cv.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0))
bin_roi = bin[y:, x:][:h, :w]
m = bin_roi != 0
if not 0.1 < m.mean() < 0.4:
continue
'''
gray_roi = gray[y:,x:][:h,:w]
v_in, v_out = gray_roi[m], gray_roi[~m]
if v_out.std() > 10.0:
continue
s = "%f, %f" % (abs(v_in.mean() - v_out.mean()), v_out.std())
cv.putText(frame, s, (x, y), cv.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)
'''
s = 1.5 * float(h) / SZ
m = cv.moments(bin_roi)
c1 = np.float32([m['m10'], m['m01']]) / m['m00']
c0 = np.float32([SZ / 2, SZ / 2])
t = c1 - s * c0
A = np.zeros((2, 3), np.float32)
A[:, :2] = np.eye(2) * s
A[:, 2] = t
bin_norm = cv.warpAffine(bin_roi,
A, (SZ, SZ),
flags=cv.WARP_INVERSE_MAP
| cv.INTER_LINEAR)
bin_norm = deskew(bin_norm)
if x + w + SZ < frame.shape[1] and y + SZ < frame.shape[0]:
frame[y:, x + w:][:SZ, :SZ] = bin_norm[..., np.newaxis]
sample = preprocess_hog([bin_norm])
digit = model.predict(sample)[0]
cv.putText(frame,
'%d' % digit, (x, y),
cv.FONT_HERSHEY_PLAIN,
1.0, (200, 0, 0),
thickness=1)
cv.imshow('frame', frame)
cv.imshow('bin', bin)
ch = cv.waitKey(1)
if ch == 27:
break
def __init__(self, video_src):
self.track_len = 10
self.detect_interval = 5
self.tracks = []
self.cam = video.create_capture(video_src)
self.frame_idx = 0
