from fastai2.vision.all import *

# all_slow

path = untar_data(URLs.PETS)

path.ls()

files = get_image_files(path/"images")
len(files)

files[0],files[6]

def label_func(f): return f[0].isupper()

dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))

dls.show_batch()

learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(1)

learn.predict(files[0])

learn.show_results()

files[0].name

pat = r'^(.*)_\d+.jpg'

dls = ImageDataLoaders.from_name_re(path, files, pat, item_tfms=Resize(224))

dls.show_batch()

dls = ImageDataLoaders.from_name_re(path, files, pat, item_tfms=Resize(460),
                                    batch_tfms=aug_transforms(size=224))

dls.show_batch()

learn = cnn_learner(dls, resnet34, metrics=error_rate)

learn.lr_find()

learn.fine_tune(4, 3e-3)

learn.show_results()

interp = Interpretation.from_learner(learn)

interp.plot_top_losses(9, figsize=(15,10))

pets = DataBlock(blocks=(ImageBlock, CategoryBlock), 
                 get_items=get_image_files, 
                 splitter=RandomSplitter(),
                 get_y=using_attr(RegexLabeller(r'(.+)_\d+.jpg$'), 'name'),
                 item_tfms=Resize(460),
                 batch_tfms=aug_transforms(size=224))

dls = pets.dataloaders(untar_data(URLs.PETS)/"images")

dls.show_batch(max_n=9)

path = untar_data(URLs.PASCAL_2007)
path.ls()

df = pd.read_csv(path/'train.csv')
df.head()

dls = ImageDataLoaders.from_df(df, path, folder='train', valid_col='is_valid', label_delim=' ',
                               item_tfms=Resize(460), batch_tfms=aug_transforms(size=224))

dls.show_batch()

learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.5))

learn.lr_find()

learn.fine_tune(4, 3e-2)

learn.show_results()

learn.predict(path/'train/000005.jpg')

interp = Interpretation.from_learner(learn)
interp.plot_top_losses(9)

df.head()

pascal = DataBlock(blocks=(ImageBlock, MultiCategoryBlock),
                   splitter=ColSplitter('is_valid'),
                   get_x=ColReader('fname', pref=str(path/'train') + os.path.sep),
                   get_y=ColReader('labels', label_delim=' '),
                   item_tfms = Resize(460),
                   batch_tfms=aug_transforms(size=224))

dls = pascal.dataloaders(df)

dls.show_batch(max_n=9)

path = untar_data(URLs.CAMVID_TINY)
path.ls()

codes = np.loadtxt(path/'codes.txt', dtype=str)
codes

fnames = get_image_files(path/"images")
fnames[0]

(path/"labels").ls()[0]

def label_func(fn): return path/"labels"/f"{fn.stem}_P{fn.suffix}"

dls = SegmentationDataLoaders.from_label_func(
    path, bs=8, fnames = fnames, label_func = label_func, codes = codes
)

dls.show_batch(max_n=6)

learn = unet_learner(dls, resnet34)
learn.fine_tune(8)

learn.show_results(max_n=6, figsize=(7,8))

camvid = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),
                   get_items = get_image_files,
                   get_y = label_func,
                   splitter=RandomSplitter(),
                   batch_tfms=aug_transforms(size=(120,160)))

dls = camvid.dataloaders(path/"images", path=path, bs=8)

dls.show_batch(max_n=6)

path = untar_data(URLs.BIWI_HEAD_POSE)

path.ls()

(path/'01').ls()

img_files = get_image_files(path)
def img2pose(x): return Path(f'{str(x)[:-7]}pose.txt')
img2pose(img_files[0])

im = PILImage.create(img_files[0])
im.shape

im.to_thumb(160)

cal = np.genfromtxt(path/'01'/'rgb.cal', skip_footer=6)
def get_ctr(f):
    ctr = np.genfromtxt(img2pose(f), skip_header=3)
    c1 = ctr[0] * cal[0][0]/ctr[2] + cal[0][2]
    c2 = ctr[1] * cal[1][1]/ctr[2] + cal[1][2]
    return tensor([c1,c2])

get_ctr(img_files[0])

biwi = DataBlock(
    blocks=(ImageBlock, PointBlock),
    get_items=get_image_files,
    get_y=get_ctr,
    splitter=FuncSplitter(lambda o: o.parent.name=='13'),
    batch_tfms=[*aug_transforms(size=(240,320)), 
                Normalize.from_stats(*imagenet_stats)]
)

dls = biwi.dataloaders(path)
dls.show_batch(max_n=9, figsize=(8,6))

learn = cnn_learner(dls, resnet18, y_range=(-1,1))

learn.lr_find()

learn.fine_tune(4, 5e-3)

math.sqrt(0.0001)

learn.show_results()