def eval_segment_best_possible():
ds = PascalSegmentation()
print("loading")
data = load_pascal('train')
print("getting edges")
data = add_edges(data)
print("computing segments")
segments = [get_km_segments(x, ds.get_image(image_name), sps,
n_segments=25) for x, image_name, sps in
zip(data.X, data.file_names, data.superpixels)]
print("combining superpixels")
segments = [seg[sp] for seg, sp in zip(segments, data.superpixels)]
predictions = [gt_in_sp(ds, f, seg)[seg]
for seg, f in zip(segments, data.file_names)]
Y_true = [ds.get_ground_truth(f) for f in data.file_names]
hamming, jaccard = eval_on_pixels(ds, Y_true, predictions,
print_results=True)
tracer()
def eval_pixel_prediction():
data = load_pascal_pixelwise('val')
predictions = [np.argmax(x, axis=-1) for x in data.X]
hamming, jaccard = eval_on_pixels(data.Y, predictions, print_results=True)
tracer()
def eval_pixel_prediction():
dataset = NYUSegmentation()
data = load_nyu_pixelwise('val')
predictions = [np.argmax(x, axis=-1) for x in data.X]
hamming, jaccard = eval_on_pixels(dataset, data.Y, predictions, print_results=True)
def eval_pixel_prediction():
dataset = NYUSegmentation()
data = load_nyu_pixelwise("val")
predictions = [np.argmax(x, axis=-1) for x in data.X]
hamming, jaccard = eval_on_pixels(dataset, data.Y, predictions, print_results=True)
