from fastai.gen_doc.nbdoc import *
from fastai.vision import *
from fastai.vision.interpret import *

show_doc(SegmentationInterpretation)

show_doc(SegmentationInterpretation.top_losses)

show_doc(SegmentationInterpretation._interp_show)

show_doc(SegmentationInterpretation.show_xyz)

show_doc(SegmentationInterpretation._generate_confusion)

show_doc(SegmentationInterpretation._plot_intersect_cm)

camvid = untar_data(URLs.CAMVID_TINY)
path_lbl = camvid/'labels'
path_img = camvid/'images'

codes = np.loadtxt(camvid/'codes.txt', dtype=str)
get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'

data = (SegmentationItemList.from_folder(path_img)
        .split_by_rand_pct()
        .label_from_func(get_y_fn, classes=codes)
        .transform(get_transforms(), tfm_y=True, size=128)
        .databunch(bs=16, path=camvid)
        .normalize(imagenet_stats))

data.show_batch(rows=2, figsize=(7,5))

learn = unet_learner(data, models.resnet18)
learn.fit_one_cycle(3,1e-2)
learn.save('mini_train')

jekyll_warn("Following results will not make much sense with this underperforming model but functionality will be explained with ease")

interp = SegmentationInterpretation.from_learner(learn)

top_losses, top_idxs = interp.top_losses(sizes=(128,128))

top_losses, top_idxs

plt.hist(to_np(top_losses), bins=20);plt.title("Loss Distribution");

learn.data.classes

mean_cm, single_img_cm = interp._generate_confusion()

mean_cm.shape, single_img_cm.shape

df = interp._plot_intersect_cm(mean_cm, "Mean of Ratio of Intersection given True Label")

i = top_idxs[0]
df = interp._plot_intersect_cm(single_img_cm[i], f"Ratio of Intersection given True Label, Image:{i}")

interp.show_xyz(i, sz=15)

jekyll_warn("""With matplotlib colormaps the max number of unique qualitative colors is 20. 
So if len(classes) > 20 then close class indexes may be plotted with the same color.
Let's fix this together :)""")

interp.c2i

show_doc(ObjectDetectionInterpretation)

jekyll_warn("ObjectDetectionInterpretation is not implemented yet. Feel free to implement it :)")