def update_target_rect(self, rect_misses):
if rect_misses[1] == 0:
size, center = size_and_center(rect_misses[0])
self.target = center + rect_misses[2]
self.correction = distance_correction(size[0])
else:
self.target = None
continue
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),
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))
possible_targets[:] = []
vis = img.copy()
next_targets = []
last_time = time.time()
for rect, misses, _ 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)
roi_copy = roi.copy()
subtargets = detect(roi_copy,
cascade_nested,
size=(max(30, s[0] - REFIND_BUFFER),
max(30, s[1] - REFIND_BUFFER)))
if len(subtargets) == 1:
sx1, sy1, sx2, sy2 = subtargets[0]
if not contains(next_targets, subtargets[0]):
next_targets.append(([
max(0, x1 - BUFFER) + sx1,
max(0, y1 - BUFFER) + sy1,
max(0, x1 - BUFFER) + sx2,
max(0, y1 - BUFFER) + sy2
], 0, None))
vis_roi = vis[max(0, y1 - BUFFER):min(width, y2 + BUFFER),
targets.append((rect, 0, None))
possible_targets[:] = []
vis = img.copy()
next_targets = []
last_time = time.time()
for rect, misses, _ 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)
roi_copy = roi.copy()
subtargets = detect(roi_copy, cascade_nested, size=(max(30, s[0]-REFIND_BUFFER), max(30, s[1]-REFIND_BUFFER)))
if len(subtargets) == 1:
sx1, sy1, sx2, sy2 = subtargets[0]
if not contains(next_targets, subtargets[0]):
next_targets.append(([max(0, x1-BUFFER)+sx1,max(0, y1-BUFFER)+sy1,max(0, x1-BUFFER)+sx2,max(0, y1-BUFFER)+sy2], 0, None))
vis_roi = vis[max(0, y1-BUFFER):min(width, y2+BUFFER), max(0, x1-BUFFER):min(height, x2+BUFFER)]
draw_rects(vis_roi, subtargets, (0, 255, 0))
cv.SaveImage("%s/roi/%s.pgm" % (output_dir, time.time()), cv.fromarray(roi_copy, allowND=True))
cv.SaveImage("%s/face/%s.pgm" % (output_dir, time.time()), cv.fromarray(roi[sx1:sx2, sy1:sy2].copy(), allowND=True))
else:
# draw_rects(vis, [rect], (0,0,255))
if misses < MAX_MISSES:
next_targets.append((rect, misses+1, None))
