def fetch_yesterday():
"""下载昨日的数据"""
jobs = [gevent.spawn(fetch, year, month, day, n)
for n in range(24)]
gevent.joinall(jobs)
if not util.detect(data_dir, year, month, day):
util.sendmail('Download Error', '\n'.join(os.listdir(data_dir)) + '\nFound In Target Dir.')
def high_level_tracker(image, should_stop, lock, targets, cascade):
# 1 thread, high level tracking
last_taunt_time = 0
next_taunt_wait = 3*(1+random.random())
while True:
with lock:
if ord(should_stop.value):
break
high_image = image.copy()
# t = clock()
rects = detect(high_image, cascade)
# with lock:
# print "high just took: ", clock() - t
if len(rects) > 0:
with lock:
targets[:] = rects
if (time.time() - last_taunt_time) > next_taunt_wait:
speak()
last_taunt_time = time.time()
next_taunt_wait = 3*(1+random.random())
def fetch_yesterday():
"""下载昨日的数据"""
jobs = [gevent.spawn(fetch, year, month, day, n) for n in range(24)]
gevent.joinall(jobs)
if not util.detect(data_dir, year, month, day):
util.sendmail(
'Download Error',
'\n'.join(os.listdir(data_dir)) + '\nFound In Target Dir.')
def high_level_tracker(image, should_stop, lock, targets, cascade):
# 1 thread, high level tracking
while True:
with lock:
if ord(should_stop.value):
break
high_image = image.copy()
# t = clock()
rects = detect(high_image, cascade)
# with lock:
# print "high just took: ", clock() - t
if len(rects) > 0:
with lock:
targets[:] = rects
def high_level_tracker(image, should_stop, lock, targets, cascade):
# 1 thread, high level tracking
while True:
with lock:
if ord(should_stop.value):
break
high_image = image.copy()
# t = clock()
rects = detect(high_image, cascade)
# with lock:
# print "high just took: ", clock() - t
if len(rects) > 0:
with lock:
targets[:] = rects
def main():
input_dir = args.input_dir
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
for f_path in os.listdir(input_dir):
f = f_path.split('.')
suffix = f[-1].lower()
prefix = os.path.split(f[-2])[-1]
filename = prefix + '.' + suffix
img = cv2.imread(os.path.join(input_dir, f_path))
centers, landmarks = detect(img)
if landmarks is None:
with open(os.path.join(output_dir, 'fails.log'), 'a') as f:
f.write(filename)
print(filename, 'failed')
continue
print(filename)
if suffix in ['jpg', 'png']:
for ft_list in ft_lists:
dirname = '-'.join(ft_list)
ft_dir = os.path.join(output_dir, dirname)
# img = auto_mask_single_img(f_path, ft_list, disturb_ellipse=0)
mask = get_mask(img,
ft_list,
centers,
landmarks,
disturb_ellipse=2,
randrange=(10, 50))
if not os.path.exists(ft_dir):
os.makedirs(ft_dir)
imsave(os.path.join(ft_dir, filename), mask)
def high_level_tracker(image, should_stop, lock, targets, cascade):
# 1 thread, high level tracking
last_taunt_time = 0
next_taunt_wait = 3 * (1 + random.random())
while True:
with lock:
if ord(should_stop.value):
break
high_image = image.copy()
# t = clock()
rects = detect(high_image, cascade)
# with lock:
# print "high just took: ", clock() - t
if len(rects) > 0:
with lock:
targets[:] = rects
if (time.time() - last_taunt_time) > next_taunt_wait:
speak()
last_taunt_time = time.time()
next_taunt_wait = 3 * (1 + random.random())
targets.append((rect, 0, np.zeros(2), 0))
possible_targets[:] = []
vis = img.copy()
next_targets = []
for rect, misses, velocity, karlness in targets:
x1, y1, x2, y2 = rect
with the_lock:
roi = low_image[max(0, y1-BUFFER):min(width, y2+BUFFER), max(0, x1-BUFFER):min(height, x2+BUFFER)]
# draw_rects(vis, [[max(0, x1-BUFFER), max(0, y1-BUFFER), min(height, x2+BUFFER), min(width, y2+BUFFER)]], (255,0,0))
subt = clock()
s, c = size_and_center(rect)
subtargets = detect(roi.copy(), cascade_nested, size=(max(30, s[0]-REFIND_BUFFER), max(30, s[1]-REFIND_BUFFER)))
# with the_lock:
# print "subtarget detect: ", clock() - subt
if len(subtargets) == 1:
sx1, sy1, sx2, sy2 = subtargets[0]
vis_roi = vis[max(0, y1-BUFFER):min(width, y2+BUFFER), max(0, x1-BUFFER):min(height, x2+BUFFER)]
if recognizing:
detection_image = roi[sy1:sy2, sx1:sx2]
predictions = recognizer.predict(cv.fromarray(detection_image))
for i, (label, confidence) in enumerate(predictions):
color = recog.LABEL_COLORS[label]
who = recog.LABELS[label]
# cv2.putText(vis_roi,
# "%s (%s)" % (who, confidence),
# (sx1 + 2, sy1 + 20 + 20 * i),
next_targets = []
for rect, misses, velocity, karlness in targets:
x1, y1, x2, y2 = rect
with the_lock:
roi = low_image[max(0, y1 -
BUFFER):min(width, y2 + BUFFER),
max(0, x1 -
BUFFER):min(height, x2 + BUFFER)]
# draw_rects(vis, [[max(0, x1-BUFFER), max(0, y1-BUFFER), min(height, x2+BUFFER), min(width, y2+BUFFER)]], (255,0,0))
subt = clock()
s, c = size_and_center(rect)
subtargets = detect(roi.copy(),
cascade_nested,
size=(max(30, s[0] - REFIND_BUFFER),
max(30, s[1] - REFIND_BUFFER)))
# with the_lock:
# print "subtarget detect: ", clock() - subt
if len(subtargets) == 1:
sx1, sy1, sx2, sy2 = subtargets[0]
vis_roi = vis[max(0, y1 - BUFFER):min(width, y2 + BUFFER),
max(0, x1 - BUFFER):min(height, x2 + BUFFER)]
if recognizing:
detection_image = roi[sy1:sy2, sx1:sx2]
predictions = recognizer.predict(
cv.fromarray(detection_image))
for i, (label, confidence) in enumerate(predictions):
color = recog.LABEL_COLORS[label]
who = recog.LABELS[label]
def test15():
print util.detect('/Users/liushuai/Downloads/data/2013/11/3', 2013, 11, 3)
def test15():
print util.detect('/Users/liushuai/Downloads/data/2013/11/3', 2013, 11, 3)
