def find_interesting_object(classifier, wc_paths):
for i, path in enumerate(wc_paths):
print i
cam = Webcam(online=False, path=path)
for i in range(10):
cam.update()
for patch in cam.patches(squareSize=227):
if patch is not None:
rankings = classifier.rankings(patch)
for label, idx, prob in rankings:
if label == 'human' and prob > 0.015:
print rankings
cv2.imshow('patch', patch)
cv2.waitKey(0)
from detector.Webcam import Webcam
from classify.classifier import ReferenceClassifier
from classify.classifier import CamClassifier
parser = argparse.ArgumentParser()
parser.add_argument("frames_dir",
help = "Directory containing webcam image folders")
parser.add_argument("num_webcams",
help = "Number of webcams to sample", type = int)
args = parser.parse_args()
frames_dir = args.frames_dir
num_webcams = args.num_webcams
wc_paths = [os.path.join(frames_dir, fn) for fn in os.listdir(frames_dir)]
wc_paths = random.sample(wc_paths, num_webcams)
classifier = CamClassifier()
classifier.set_batch_size(1)
while True:
for path in wc_paths:
cam = Webcam(online=False, path=path)
for i in range(5):
cam.update()
overlay = cam.filtered_overlay(classifier, squareSize=227)
if overlay is not None:
cv2.imshow('overlay', overlay)
cv2.waitKey(0)
label, idx, prob = ranking
if label != 'noise' and prob > 0.5:
if prob > K_HUMAN_THRESH:
print rankings
cv2.imshow('patch', patch)
cv2.waitKey(0)
return
def find_human(classifier, cams):
for i, cam in enumerate(cams):
print i
find_human_in_cam(classifier, cam)
for i in range(5):
# map(lambda c: c.update(), cams)
webcam.update()
def find_interesting_object(classifier, wc_paths):
for i, path in enumerate(wc_paths):
print i
cam = Webcam(online=False, path=path)
for i in range(10):
cam.update()
for patch in cam.patches(squareSize=227):
if patch is not None:
rankings = classifier.rankings(patch)
for label, idx, prob in rankings:
if label == 'human' and prob > 0.015:
print rankings
cv2.imshow('patch', patch)
cv2.waitKey(0)
