def main(input_size, model_path):
demo = Predictor(model_path, input_size)
while True:
glob_pattern = os.path.join("/app/data/lsun_room/images/", '*.jpg')
img_fn = random.choice(glob(glob_pattern))
img = np.array(Image.open(img_fn).resize(input_size))
output, label_map = demo.process(img)
output_layout = output.copy()
output_layout_edges = output.copy()
for label in np.unique(label_map):
label_map = discard_smallest_blobs(label_map,
label,
discard_value=-1,
plot_diff=False)
pts = np.flip(np.array(np.where(label_map == label)).T, axis=1)
hull = ConvexHull(pts, qhull_options='Qt')
#rot_angle, area, width, height, center_point, corner_points = minBoundingRect(pts[hull.vertices])
corner_points = pts[hull.vertices]
#corner_points = minimum_bounding_rectangle(pts)
corner_points = corner_points.astype(np.int32)
polygon_pts = corner_points
#polygon_pts_rdp = rdp(corner_points, epsilon=10)
cv2.fillPoly(output_layout, pts=[polygon_pts], color=COLORS[label])
cv2.polylines(output_layout_edges, [polygon_pts], True,
COLORS[label])
f, axarr = plt.subplots(1, 4)
axarr[0].imshow(img, interpolation='bicubic')
axarr[1].imshow(output.astype('float32'), interpolation='bicubic')
axarr[2].imshow(output_layout.astype('uint8'), interpolation='bicubic')
axarr[3].imshow(output_layout_edges.astype('uint8'),
interpolation='bicubic')
plt.title(img_fn)
plt.show()
alpha = 0.9
output = cv2.addWeighted(output, alpha, output_layout, 1 - alpha, 0)
scipy.misc.imsave('output/super_res_output.jpg', output)
def main(input_size, model_path):
demo = Predictor(model_path, input_size)
img = Image.open('/app/hubstairs.jpg').resize(input_size)
output, label_map = demo.process(img)
lines, output = gen_linear_layout_map(label_map, output)
minmax = lambda x, y: (min(320, max(0, x)), min(320, max(0, y)))
for pt1, pt2 in lines:
cv2.circle(output, minmax(*pt1), 10, [255,0,0], thickness=1, lineType=8, shift=0)
cv2.circle(output, minmax(*pt2), 10, [255,0,0], thickness=1, lineType=8, shift=0)
for pt3, pt4 in lines:
if pt1 != pt3 and pt2 != pt4:
x, y = get_intersect(pt1, pt2, pt3, pt4)
cv2.circle(output, (int(x), int(y)), 5, [255,0,0], thickness=1, lineType=8, shift=0)
plt.imshow(output, cmap = 'gray', interpolation = 'bicubic')
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.show()
scipy.misc.imsave('output/super_res_output.jpg', output)