def pr_stats(run, image_dir, label_db, connected_components_threshold):
# TODO: a bunch of this can go back into one off init in a class
_train_opts, model = m.restore_model(run)
label_db = LabelDB(label_db_file=label_db)
set_comparison = u.SetComparison()
# use 4 images for debug
debug_imgs = []
for idx, filename in enumerate(sorted(os.listdir(image_dir))):
# load next image
# TODO: this block used in various places, refactor
img = np.array(Image.open(image_dir + "/" +
filename)) # uint8 0->255 (H, W)
img = img.astype(np.float32)
img = (img / 127.5) - 1.0 # -1.0 -> 1.0 # see data.py
# run through model
prediction = expit(model.predict(np.expand_dims(img, 0))[0])
if len(debug_imgs) < 4:
debug_imgs.append(u.side_by_side(rgb=img, bitmap=prediction))
# calc [(x,y), ...] centroids
predicted_centroids = u.centroids_of_connected_components(
prediction, rescale=2.0, threshold=connected_components_threshold)
# compare to true labels
true_centroids = label_db.get_labels(filename)
true_centroids = [(y, x) for (x, y) in true_centroids] # sigh...
tp, fn, fp = set_comparison.compare_sets(true_centroids,
predicted_centroids)
precision, recall, f1 = set_comparison.precision_recall_f1()
return {
"debug_imgs": debug_imgs,
"precision": precision,
"recall": recall,
"f1": f1
}
help='relative scale of label bitmap compared to input image')
parser.add_argument('--distort', action='store_true')
parser.add_argument('--rotate', action='store_true')
parser.add_argument('--width', type=int, default=None, help='input image width. required if --patch-width-height not set.')
parser.add_argument('--height', type=int, default=None, help='input image height. required if --patch-width-height not set.')
opts = parser.parse_args()
print(opts)
from PIL import Image, ImageDraw
sess = tf.Session()
imgs, xyss = img_xys_iterator(image_dir=opts.image_dir,
label_dir=opts.label_dir,
batch_size=opts.batch_size,
patch_width_height=opts.patch_width_height,
distort_rgb=opts.distort,
flip_left_right=True,
random_rotation=opts.rotate,
repeat=True,
width=opts.width,
height=opts.height,
label_rescale=opts.label_rescale)
for b in range(3):
img_batch, xys_batch = sess.run([imgs, xyss])
for i, (img, xys) in enumerate(zip(img_batch, xys_batch)):
fname = "test_%03d_%03d.png" % (b, i)
print("batch", b, "element", i, "fname", fname)
u.side_by_side(rgb=img, bitmap=xys).save(fname)
img = (img / 127.5) - 1.0 # -1.0 -> 1.0 # see data.py
try:
# run single image through model
prediction = sess.run(model.output, feed_dict={model.imgs: [img]})[0]
# calc [(x,y), ...] centroids
centroids = u.centroids_of_connected_components(prediction,
rescale=2.0,
threshold=opts.connected_components_threshold)
print("\t".join(map(str, [idx, filename, len(centroids)])))
# export some debug image (if requested)
if opts.export_pngs != '':
if opts.export_pngs == 'predictions':
debug_img = u.side_by_side(rgb=img, bitmap=prediction)
elif opts.export_pngs == 'centroids':
debug_img = u.red_dots(rgb=img, centroids=centroids)
else:
raise Exception("unknown --export-pngs option")
debug_img.save("%s/%s.png" % (export_dir, filename))
# set new labels (if requested)
if db:
db.set_labels(filename, centroids, flip=True)
except tf.errors.OutOfRangeError:
# end of iterator
break
parser.add_argument('--image-dir', type=str, default='images/201802_sample/training',
help='location of RGB input images')
parser.add_argument('--label-dir', type=str, default='labels/201802_sample',
help='location of corresponding L label files')
parser.add_argument('--batch-size', type=int, default=4)
parser.add_argument('--patch-fraction', type=int, default=1,
help="what fraction of image to use as patch. 1 => no patch")
parser.add_argument('--distort', action='store_true')
opts = parser.parse_args()
print(opts)
from PIL import Image, ImageDraw
sess = tf.Session()
imgs, xyss = img_xys_iterator(image_dir=opts.image_dir,
label_dir=opts.label_dir,
batch_size=opts.batch_size,
patch_fraction=opts.patch_fraction,
distort_rgb=opts.distort,
flip_left_right=True,
random_rotation=True,
repeat=True)
for b in range(3):
print(">batch", b)
img_batch, xys_batch = sess.run([imgs, xyss])
for i, (img, xys) in enumerate(zip(img_batch, xys_batch)):
print(">element", i)
u.side_by_side(rgb=img, bitmap=xys).save("test_%03d_%03d.png" % (b, i))
