def main():
# Parse arguments
parser = argparse.ArgumentParser(description='Classify images in a directory as good or bad.')
parser.add_argument('images-path', nargs='?', default='.', help='Path to directory of input images.')
args = vars(parser.parse_args())
# Select directory
global if_path
if_path = args['images-path']
# Parse in features information
global feats
cwd = os.getcwd()
os.chdir('../suitability/')
feats = Feats(if_path)
print('- Loaded features for %d files' % len(feats))
# Initialise classifier
global classifier
classifier = Classifier()
os.chdir(cwd)
# Get list of files
global files
valid_suffices = ('png', 'jpg', 'jpeg')
files = [f for f in os.listdir(if_path) if \
os.path.isfile('%s/%s' % (if_path, f)) and \
f.lower().endswith(valid_suffices)]
if len(files) == 0:
print('No images to read in: %s' % if_path)
cleanup()
# Classify each image and keep suitable ones
global suitability_scores
suitable = []
suitability_scores = []
for fname in files:
score = classifier.getScore(feats[fname])
if score > 0:
suitable.append(fname)
suitability_scores.append(score)
print('- Classified images to retain list of suitable images only')
print('- %d/%d suitable images left' % (len(suitable), len(files)))
files = suitable
# Create list of last shown times
global last_times
global selected
last_times = [time.time()]*len(files)
selected = [False]*len(files)
# Main loop
global num
num = 0
find_next_image()
show_next_image()
signal.signal(signal.SIGINT, cleanup)
while True:
# Wait for key for 50ms
k = cv.waitKey(50)
# If no more images left to show, exit
n_left = len([x for x in selected if not x])
if n_left == 0:
break
# Ignore if no key captured
if k is -1:
continue
# If Esc, break loop
elif k == 27:
break
# If any other button, show previous image
else:
next_image()
print '%d images left to show' % n_left
cleanup()
def get_images(n):
# Parse in features information
global file_to_feat
cwd = os.getcwd()
os.chdir('../suitability/')
feats = Feats(if_path)
print('- Loaded features for %d files' % len(feats))
# Initialise classifier
global classifier
classifier = Classifier()
os.chdir(cwd)
# Get list of files
global files
valid_suffices = ('png', 'jpg', 'jpeg')
files = [f for f in os.listdir(if_path) if \
os.path.isfile('%s/%s' % (if_path, f)) and \
f.lower().endswith(valid_suffices)]
if len(files) == 0:
print('No images to read in: %s' % if_path)
cleanup()
random.shuffle(files)
# Classify each image and keep suitable ones
suitable = [False]*len(files)
scores = [0]*len(files)
for i, fpath in enumerate(files):
if classifier.predictFeats(feats[fpath]) == 1:
suitable[i] = True
scores[i] = classifier.getScore(feats[fpath])
# Show 10 top suitable images
N = 7
_, topfs = zip(*sorted(zip(scores, files), reverse=True)[:N])
iH = 150
iW = 200
H = iH
W = iW*N
out = np.ndarray((H, W, 3), dtype=np.uint8)
out.fill(255)
for i, fpath in enumerate(topfs):
I = imread_rotated('%s/%s' % (if_path, fpath))
h, w, _ = I.shape
scale = min(float(iW) / w, float(iH) / h)
I = cv.resize(I, None, fx=scale, fy=scale)
h, w, _ = I.shape
x0 = iW*i + iW/2 - w/2
y0 = iH/2 - h/2
out[y0:y0+h, x0:x0+w,] = I
cv.imwrite('top_suitable.jpg', out)
# Get up to n entries
#pairs = sorted(zip(suitable, files))
#good = [(files[i], suitable[i]) for i in range(len(files)) if suitable[i]]
#bad = [(files[i], suitable[i]) for i in range(len(files)) if not suitable[i]]
#random.shuffle(good)
#random.shuffle(bad)
#n_good = int(n * 0.35)
#n_bad = n - n_good
#pairs = bad[:n_bad] + good[:n_good]
#random.shuffle(pairs)
#files, suitable = zip(*pairs)
files = files[:n]
suitable = suitable[:n]
files = ['%s/%s' % (if_path, fname) for fname in files]
# Load images
imgs = [None]*len(files)
for i, fpath in enumerate(files):
imgs[i] = imread_rotated(fpath)
return files, imgs, suitable
