diff --git a/LICENSE b/COPYING
similarity index 100%
rename from LICENSE
rename to COPYING
diff --git a/MANIFEST.in b/MANIFEST.in
index 4a6bfd23ab0b5beb5c0da50537912ceef7183e02..946a661e098c89b0b5b505c5b22594f474fd8b9a 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,2 +1,2 @@
-include README.rst buildout.cfg LICENSE version.txt requirements.txt
+include README.rst buildout.cfg COPYING version.txt requirements.txt
recursive-include doc *.rst *.png *.ico *.txt
diff --git a/bob/ip/binseg/configs/datasets/imagefolderinference.py b/bob/ip/binseg/configs/datasets/imagefolderinference.py
index ba760e876811ef5bc80259a7008e6e66e23958f4..634566b9c0f8c899474494651044f138e610f724 100644
--- a/bob/ip/binseg/configs/datasets/imagefolderinference.py
+++ b/bob/ip/binseg/configs/datasets/imagefolderinference.py
@@ -7,7 +7,8 @@ from bob.ip.binseg.data.imagefolderinference import ImageFolderInference
#### Config ####
# add your transforms below
-transforms = Compose([
+transforms = Compose([
+ ToRGB(),
CenterCrop((544,544))
,ToTensor()
])
diff --git a/bob/ip/binseg/data/imagefolderinference.py b/bob/ip/binseg/data/imagefolderinference.py
index 79e57e245ffbc3bd369f29523f36f1d09cabc833..c4218755f099e5455e474b67fcaa004f99f46ca4 100644
--- a/bob/ip/binseg/data/imagefolderinference.py
+++ b/bob/ip/binseg/data/imagefolderinference.py
@@ -8,24 +8,45 @@ import torch
import torchvision.transforms.functional as VF
import bob.io.base
-def get_file_lists(data_path):
+def get_file_lists(data_path, glob):
+ """
+ Recursively retrieves file lists from a given path, matching a given glob
+
+ This function will use :py:meth:`pathlib.Path.rglob`, together with the
+ provided glob pattern to search for anything the desired filename.
+ """
+
data_path = Path(data_path)
- image_file_names = np.array(sorted(list(data_path.glob('*'))))
+ image_file_names = np.array(sorted(list(data_path.rglob(glob))))
return image_file_names
class ImageFolderInference(Dataset):
"""
Generic ImageFolder containing images for inference
-
+
+ Notice that this implementation, contrary to its sister
+ :py:class:`.ImageFolder`, does not *automatically*
+ convert the input image to RGB, before passing it to the transforms, so it
+ is possible to accomodate a wider range of input types (e.g. 16-bit PNG
+ images).
+
Parameters
----------
path : str
full path to root of dataset
-
+
+ glob : str
+ glob that can be used to filter-down files to be loaded on the provided
+ path
+
+ transform : list
+ List of transformations to apply to every input sample
+
"""
- def __init__(self, path, transform = None):
+ def __init__(self, path, glob='*', transform = None):
self.transform = transform
- self.img_file_list = get_file_lists(path)
+ self.path = path
+ self.img_file_list = get_file_lists(path, glob)
def __len__(self):
"""
@@ -35,27 +56,27 @@ class ImageFolderInference(Dataset):
size of the dataset
"""
return len(self.img_file_list)
-
+
def __getitem__(self,index):
"""
Parameters
----------
index : int
-
+
Returns
-------
list
dataitem [img_name, img]
"""
img_path = self.img_file_list[index]
- img_name = img_path.name
- img = Image.open(img_path).convert(mode='RGB')
-
+ img_name = img_path.relative_to(self.path).as_posix()
+ img = Image.open(img_path)
+
sample = [img]
-
+
if self.transform :
sample = self.transform(*sample)
-
+
sample.insert(0,img_name)
-
+
return sample
diff --git a/bob/ip/binseg/data/transforms.py b/bob/ip/binseg/data/transforms.py
index 1336ff135c89f4e33db6add57910f3894cbe5dcb..b97fe7959ba95e571462d6f6d9d2ddade9fad226 100644
--- a/bob/ip/binseg/data/transforms.py
+++ b/bob/ip/binseg/data/transforms.py
@@ -11,6 +11,7 @@ import math
from math import floor
import warnings
import collections
+import bob.core
_pil_interpolation_to_str = {
Image.NEAREST: 'PIL.Image.NEAREST',
@@ -22,7 +23,7 @@ _pil_interpolation_to_str = {
}
Iterable = collections.abc.Iterable
-# Compose
+# Compose
class Compose:
"""Composes several transforms.
@@ -62,7 +63,7 @@ class CenterCrop:
"""
def __init__(self, size):
self.size = size
-
+
def __call__(self, *args):
return [VF.center_crop(img, self.size) for img in args]
@@ -70,24 +71,24 @@ class CenterCrop:
class Crop:
"""
Crop at the given coordinates.
-
+
Attributes
----------
- i : int
+ i : int
upper pixel coordinate.
- j : int
+ j : int
left pixel coordinate.
- h : int
+ h : int
height of the cropped image.
- w : int
+ w : int
width of the cropped image.
"""
def __init__(self, i, j, h, w):
self.i = i
self.j = j
- self.h = h
- self.w = w
-
+ self.h = h
+ self.w = w
+
def __call__(self, *args):
return [img.crop((self.j, self.i, self.j + self.w, self.i + self.h)) for img in args]
@@ -97,34 +98,61 @@ class Pad:
Attributes
----------
- padding : int or tuple
- padding on each border. If a single int is provided this is used to pad all borders.
+ padding : int or tuple
+ padding on each border. If a single int is provided this is used to pad all borders.
If tuple of length 2 is provided this is the padding on left/right and top/bottom respectively.
If a tuple of length 4 is provided this is the padding for the left, top, right and bottom borders respectively.
-
+
fill : int
- pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
- This value is only used when the padding_mode is constant
+ pixel fill value for constant fill. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively.
+ This value is only used when the padding_mode is constant
"""
def __init__(self, padding, fill=0):
self.padding = padding
self.fill = fill
-
+
def __call__(self, *args):
return [VF.pad(img, self.padding, self.fill, padding_mode='constant') for img in args]
-
+
+class AutoLevel16to8:
+ """Converts a 16-bit image to 8-bit representation using "auto-level"
+
+ This transform assumes that the input images are gray-scaled.
+
+ To auto-level, we calculate the maximum and the minimum of the image, and
+ consider such a range should be mapped to the [0,255] range of the
+ destination image.
+ """
+ def _process_one(self, img):
+ return Image.fromarray(bob.core.convert(img, 'uint8', (0,255),
+ img.getextrema()))
+
+ def __call__(self, *args):
+ return [self._process_one(img) for img in args]
+
+class ToRGB:
+ """Converts from any input format to RGB, using an ADAPTIVE conversion.
+
+ This transform takes the input image and converts it to RGB using
+ py:method:`Image.Image.convert`, with `mode='RGB'` and using all other
+ defaults. This may be aggressive if applied to 16-bit images without
+ further considerations.
+ """
+ def __call__(self, *args):
+ return [img.convert(mode="RGB") for img in args]
+
class ToTensor:
"""Converts :py:class:`PIL.Image.Image` to :py:class:`torch.Tensor` """
def __call__(self, *args):
return [VF.to_tensor(img) for img in args]
-
+
# Augmentations
class RandomHFlip:
"""
Flips horizontally
-
+
Attributes
----------
prob : float
@@ -132,50 +160,50 @@ class RandomHFlip:
"""
def __init__(self, prob = 0.5):
self.prob = prob
-
+
def __call__(self, *args):
if random.random() < self.prob:
return [VF.hflip(img) for img in args]
-
+
else:
return args
-
-
+
+
class RandomVFlip:
"""
Flips vertically
-
+
Attributes
----------
- prob : float
+ prob : float
probability at which imgage is flipped. Defaults to ``0.5``
"""
def __init__(self, prob = 0.5):
self.prob = prob
-
+
def __call__(self, *args):
if random.random() < self.prob:
return [VF.vflip(img) for img in args]
-
+
else:
return args
-
+
class RandomRotation:
"""
Rotates by degree
-
+
Attributes
----------
degree_range : tuple
range of degrees in which image and ground truth are rotated. Defaults to ``(-15, +15)``
- prob : float
+ prob : float
probability at which imgage is rotated. Defaults to ``0.5``
"""
def __init__(self, degree_range = (-15, +15), prob = 0.5):
self.prob = prob
self.degree_range = degree_range
-
+
def __call__(self, *args):
if random.random() < self.prob:
degree = random.randint(*self.degree_range)
@@ -184,21 +212,21 @@ class RandomRotation:
return args
class ColorJitter(object):
- """
+ """
Randomly change the brightness, contrast, saturation and hue
-
+
Attributes
----------
- brightness : float
+ brightness : float
how much to jitter brightness. brightness_factor
is chosen uniformly from ``[max(0, 1 - brightness), 1 + brightness]``.
contrast : float
how much to jitter contrast. contrast_factor
is chosen uniformly from ``[max(0, 1 - contrast), 1 + contrast]``.
- saturation : float
+ saturation : float
how much to jitter saturation. saturation_factor
is chosen uniformly from ``[max(0, 1 - saturation), 1 + saturation]``.
- hue : float
+ hue : float
how much to jitter hue. hue_factor is chosen uniformly from
``[-hue, hue]``. Should be >=0 and <= 0.5
prob : float
@@ -247,21 +275,21 @@ class ColorJitter(object):
class RandomResizedCrop:
"""Crop to random size and aspect ratio.
- A crop of random size of the original size and a random aspect ratio of
- the original aspect ratio is made. This crop is finally resized to
+ A crop of random size of the original size and a random aspect ratio of
+ the original aspect ratio is made. This crop is finally resized to
given size. This is popularly used to train the Inception networks.
-
+
Attributes
----------
- size : int
+ size : int
expected output size of each edge
- scale : tuple
+ scale : tuple
range of size of the origin size cropped. Defaults to ``(0.08, 1.0)``
ratio : tuple
range of aspect ratio of the origin aspect ratio cropped. Defaults to ``(3. / 4., 4. / 3.)``
interpolation :
Defaults to ``PIL.Image.BILINEAR``
- prob : float
+ prob : float
probability at which the operation is applied. Defaults to ``0.5``
"""
@@ -332,7 +360,7 @@ class RandomResizedCrop:
class Resize:
"""Resize to given size.
-
+
Attributes
----------
size : tuple or int
diff --git a/bob/ip/binseg/engine/inferencer.py b/bob/ip/binseg/engine/inferencer.py
index c2dd6443152cef96fba9dfdbe5a3907f948e8ebf..c1ed5fafa2aa20b46a749fcdca3f29a46a003455 100644
--- a/bob/ip/binseg/engine/inferencer.py
+++ b/bob/ip/binseg/engine/inferencer.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-import os
+import os
import logging
import time
import datetime
@@ -30,7 +30,7 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
ground_truths : :py:class:`torch.Tensor`
tensor with binary ground-truth
names : list
- list of file names
+ list of file names
output_folder : str
output path
logger : :py:class:`logging.Logger`
@@ -38,7 +38,7 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
Returns
-------
- list
+ list
list containing batch metrics: ``[name, threshold, precision, recall, specificity, accuracy, jaccard, f1_score]``
"""
step_size = 0.01
@@ -50,25 +50,25 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
file_name = "{}.csv".format(names[j])
logger.info("saving {}".format(file_name))
-
+
with open (os.path.join(output_folder,file_name), "w+") as outfile:
outfile.write("threshold, precision, recall, specificity, accuracy, jaccard, f1_score\n")
- for threshold in np.arange(0.0,1.0,step_size):
+ for threshold in np.arange(0.0,1.0,step_size):
# threshold
binary_pred = torch.gt(predictions[j], threshold).byte()
# equals and not-equals
equals = torch.eq(binary_pred, gts) # tensor
notequals = torch.ne(binary_pred, gts) # tensor
-
- # true positives
+
+ # true positives
tp_tensor = (gts * binary_pred ) # tensor
tp_count = torch.sum(tp_tensor).item() # scalar
- # false positives
- fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
+ # false positives
+ fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
fp_count = torch.sum(fp_tensor).item()
# true negatives
@@ -80,14 +80,14 @@ def batch_metrics(predictions, ground_truths, names, output_folder, logger):
fn_count = torch.sum(fn_tensor).item()
# calc metrics
- metrics = base_metrics(tp_count, fp_count, tn_count, fn_count)
-
- # write to disk
+ metrics = base_metrics(tp_count, fp_count, tn_count, fn_count)
+
+ # write to disk
outfile.write("{:.2f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f},{:.5f} \n".format(threshold, *metrics))
-
+
batch_metrics.append([names[j],threshold, *metrics ])
-
+
return batch_metrics
@@ -100,19 +100,21 @@ def save_probability_images(predictions, names, output_folder, logger):
predictions : :py:class:`torch.Tensor`
tensor with pixel-wise probabilities
names : list
- list of file names
+ list of file names
output_folder : str
output path
logger : :py:class:`logging.Logger`
python logger
"""
- images_subfolder = os.path.join(output_folder,'images')
- if not os.path.exists(images_subfolder): os.makedirs(images_subfolder)
+ images_subfolder = os.path.join(output_folder,'images')
for j in range(predictions.size()[0]):
img = VF.to_pil_image(predictions.cpu().data[j])
filename = '{}.png'.format(names[j].split(".")[0])
- logger.info("saving {}".format(filename))
- img.save(os.path.join(images_subfolder, filename))
+ fullpath = os.path.join(images_subfolder, filename)
+ logger.info("saving {}".format(fullpath))
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir): os.makedirs(fulldir)
+ img.save(fullpath)
def save_hdf(predictions, names, output_folder, logger):
"""
@@ -123,19 +125,22 @@ def save_hdf(predictions, names, output_folder, logger):
predictions : :py:class:`torch.Tensor`
tensor with pixel-wise probabilities
names : list
- list of file names
+ list of file names
output_folder : str
output path
logger : :py:class:`logging.Logger`
python logger
"""
- hdf5_subfolder = os.path.join(output_folder,'hdf5')
+ hdf5_subfolder = os.path.join(output_folder,'hdf5')
if not os.path.exists(hdf5_subfolder): os.makedirs(hdf5_subfolder)
for j in range(predictions.size()[0]):
img = predictions.cpu().data[j].squeeze(0).numpy()
filename = '{}.hdf5'.format(names[j].split(".")[0])
+ fullpath = os.path.join(hdf5_subfolder, filename)
logger.info("saving {}".format(filename))
- bob.io.base.save(img, os.path.join(hdf5_subfolder, filename))
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir): os.makedirs(fulldir)
+ bob.io.base.save(img, fullpath)
def do_inference(
model,
@@ -146,7 +151,7 @@ def do_inference(
"""
Run inference and calculate metrics
-
+
Parameters
---------
model : :py:class:`torch.nn.Module`
@@ -159,18 +164,18 @@ def do_inference(
logger = logging.getLogger("bob.ip.binseg.engine.inference")
logger.info("Start evaluation")
logger.info("Output folder: {}, Device: {}".format(output_folder, device))
- results_subfolder = os.path.join(output_folder,'results')
+ results_subfolder = os.path.join(output_folder,'results')
os.makedirs(results_subfolder,exist_ok=True)
-
+
model.eval().to(device)
- # Sigmoid for probabilities
- sigmoid = torch.nn.Sigmoid()
+ # Sigmoid for probabilities
+ sigmoid = torch.nn.Sigmoid()
# Setup timers
start_total_time = time.time()
times = []
- # Collect overall metrics
+ # Collect overall metrics
metrics = []
for samples in tqdm(data_loader):
@@ -181,50 +186,50 @@ def do_inference(
start_time = time.perf_counter()
outputs = model(images)
-
- # necessary check for hed architecture that uses several outputs
+
+ # necessary check for hed architecture that uses several outputs
# for loss calculation instead of just the last concatfuse block
if isinstance(outputs,list):
outputs = outputs[-1]
-
+
probabilities = sigmoid(outputs)
-
+
batch_time = time.perf_counter() - start_time
times.append(batch_time)
logger.info("Batch time: {:.5f} s".format(batch_time))
-
+
b_metrics = batch_metrics(probabilities, ground_truths, names,results_subfolder, logger)
metrics.extend(b_metrics)
-
+
# Create probability images
save_probability_images(probabilities, names, output_folder, logger)
# save hdf5
save_hdf(probabilities, names, output_folder, logger)
- # DataFrame
+ # DataFrame
df_metrics = pd.DataFrame(metrics,columns= \
["name",
"threshold",
- "precision",
- "recall",
- "specificity",
- "accuracy",
- "jaccard",
+ "precision",
+ "recall",
+ "specificity",
+ "accuracy",
+ "jaccard",
"f1_score"])
# Report and Averages
metrics_file = "Metrics.csv".format(model.name)
metrics_path = os.path.join(results_subfolder, metrics_file)
logger.info("Saving average over all input images: {}".format(metrics_file))
-
+
avg_metrics = df_metrics.groupby('threshold').mean()
std_metrics = df_metrics.groupby('threshold').std()
- # Uncomment below for F1-score calculation based on average precision and metrics instead of
+ # Uncomment below for F1-score calculation based on average precision and metrics instead of
# F1-scores of individual images. This method is in line with Maninis et. al. (2016)
#avg_metrics["f1_score"] = (2* avg_metrics["precision"]*avg_metrics["recall"])/ \
# (avg_metrics["precision"]+avg_metrics["recall"])
-
+
avg_metrics["std_pr"] = std_metrics["precision"]
avg_metrics["pr_upper"] = avg_metrics['precision'] + avg_metrics["std_pr"]
avg_metrics["pr_lower"] = avg_metrics['precision'] - avg_metrics["std_pr"]
@@ -232,13 +237,13 @@ def do_inference(
avg_metrics["re_upper"] = avg_metrics['recall'] + avg_metrics["std_re"]
avg_metrics["re_lower"] = avg_metrics['recall'] - avg_metrics["std_re"]
avg_metrics["std_f1"] = std_metrics["f1_score"]
-
+
avg_metrics.to_csv(metrics_path)
maxf1 = avg_metrics['f1_score'].max()
optimal_f1_threshold = avg_metrics['f1_score'].idxmax()
-
+
logger.info("Highest F1-score of {:.5f}, achieved at threshold {}".format(maxf1, optimal_f1_threshold))
-
+
# Plotting
np_avg_metrics = avg_metrics.to_numpy().T
fig_name = "precision_recall.pdf"
@@ -246,7 +251,7 @@ def do_inference(
fig = precision_recall_f1iso_confintval([np_avg_metrics[0]],[np_avg_metrics[1]],[np_avg_metrics[7]],[np_avg_metrics[8]],[np_avg_metrics[10]],[np_avg_metrics[11]], [model.name,None], title=output_folder)
fig_filename = os.path.join(results_subfolder, fig_name)
fig.savefig(fig_filename)
-
+
# Report times
total_inference_time = str(datetime.timedelta(seconds=int(sum(times))))
average_batch_inference_time = np.mean(times)
@@ -256,7 +261,7 @@ def do_inference(
times_file = "Times.txt"
logger.info("saving {}".format(times_file))
-
+
with open (os.path.join(results_subfolder,times_file), "w+") as outfile:
date = datetime.datetime.now()
outfile.write("Date: {} \n".format(date.strftime("%Y-%m-%d %H:%M:%S")))
@@ -264,7 +269,7 @@ def do_inference(
outfile.write("Average batch inference time: {} \n".format(average_batch_inference_time))
outfile.write("Total inference time: {} \n".format(total_inference_time))
- # Save model summary
+ # Save model summary
summary_file = 'ModelSummary.txt'
logger.info("saving {}".format(summary_file))
diff --git a/bob/ip/binseg/engine/predicter.py b/bob/ip/binseg/engine/predicter.py
index b6e8ad06da54b43a538cf4fc7805cc63e6966cee..ebd09ac5e84d4f9a81a20f72c3919f42071fb73d 100644
--- a/bob/ip/binseg/engine/predicter.py
+++ b/bob/ip/binseg/engine/predicter.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
-import os
+import os
import logging
import time
import datetime
@@ -24,7 +24,7 @@ def do_predict(
"""
Run inference and calculate metrics
-
+
Parameters
---------
model : :py:class:`torch.nn.Module`
@@ -37,12 +37,12 @@ def do_predict(
logger = logging.getLogger("bob.ip.binseg.engine.inference")
logger.info("Start evaluation")
logger.info("Output folder: {}, Device: {}".format(output_folder, device))
- results_subfolder = os.path.join(output_folder,'results')
+ results_subfolder = os.path.join(output_folder,'results')
os.makedirs(results_subfolder,exist_ok=True)
-
+
model.eval().to(device)
- # Sigmoid for probabilities
- sigmoid = torch.nn.Sigmoid()
+ # Sigmoid for probabilities
+ sigmoid = torch.nn.Sigmoid()
# Setup timers
start_total_time = time.time()
@@ -55,24 +55,24 @@ def do_predict(
start_time = time.perf_counter()
outputs = model(images)
-
- # necessary check for hed architecture that uses several outputs
+
+ # necessary check for hed architecture that uses several outputs
# for loss calculation instead of just the last concatfuse block
if isinstance(outputs,list):
outputs = outputs[-1]
-
+
probabilities = sigmoid(outputs)
-
+
batch_time = time.perf_counter() - start_time
times.append(batch_time)
logger.info("Batch time: {:.5f} s".format(batch_time))
-
+
# Create probability images
save_probability_images(probabilities, names, output_folder, logger)
# Save hdf5
save_hdf(probabilities, names, output_folder, logger)
-
+
# Report times
total_inference_time = str(datetime.timedelta(seconds=int(sum(times))))
average_batch_inference_time = np.mean(times)
@@ -82,7 +82,7 @@ def do_predict(
times_file = "Times.txt"
logger.info("saving {}".format(times_file))
-
+
with open (os.path.join(results_subfolder,times_file), "w+") as outfile:
date = datetime.datetime.now()
outfile.write("Date: {} \n".format(date.strftime("%Y-%m-%d %H:%M:%S")))
diff --git a/bob/ip/binseg/utils/plot.py b/bob/ip/binseg/utils/plot.py
index ceb268d06ee29c42c750e58fc37a1cfd438af2fb..bd9c2131d41e9b39fcf40b4484501655e45a1a73 100644
--- a/bob/ip/binseg/utils/plot.py
+++ b/bob/ip/binseg/utils/plot.py
@@ -3,7 +3,7 @@
import numpy as np
import os
-import csv
+import csv
import pandas as pd
import PIL
from PIL import Image,ImageFont, ImageDraw
@@ -13,62 +13,62 @@ import torch
def precision_recall_f1iso(precision, recall, names, title=None):
"""
Author: Andre Anjos (andre.anjos@idiap.ch).
-
- Creates a precision-recall plot of the given data.
+
+ Creates a precision-recall plot of the given data.
The plot will be annotated with F1-score iso-lines (in which the F1-score
- maintains the same value)
-
+ maintains the same value)
+
Parameters
- ----------
+ ----------
precision : :py:class:`numpy.ndarray` or :py:class:`list`
A list of 1D np arrays containing the Y coordinates of the plot, or
the precision, or a 2D np array in which the rows correspond to each
- of the system's precision coordinates.
+ of the system's precision coordinates.
recall : :py:class:`numpy.ndarray` or :py:class:`list`
A list of 1D np arrays containing the X coordinates of the plot, or
the recall, or a 2D np array in which the rows correspond to each
- of the system's recall coordinates.
+ of the system's recall coordinates.
names : :py:class:`list`
An iterable over the names of each of the systems along the rows of
- ``precision`` and ``recall``
+ ``precision`` and ``recall``
title : :py:class:`str`, optional
- A title for the plot. If not set, omits the title
+ A title for the plot. If not set, omits the title
Returns
- -------
+ -------
matplotlib.figure.Figure
- A matplotlib figure you can save or display
- """
+ A matplotlib figure you can save or display
+ """
import matplotlib
matplotlib.use('agg')
- import matplotlib.pyplot as plt
+ import matplotlib.pyplot as plt
from itertools import cycle
- fig, ax1 = plt.subplots(1)
+ fig, ax1 = plt.subplots(1)
lines = ["-","--","-.",":"]
linecycler = cycle(lines)
- for p, r, n in zip(precision, recall, names):
+ for p, r, n in zip(precision, recall, names):
# Plots only from the point where recall reaches its maximum, otherwise, we
# don't see a curve...
i = r.argmax()
pi = p[i:]
- ri = r[i:]
+ ri = r[i:]
valid = (pi+ri) > 0
- f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
+ f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
# optimal point along the curve
argmax = f1.argmax()
opi = pi[argmax]
ori = ri[argmax]
# Plot Recall/Precision as threshold changes
- ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
+ ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
ax1.plot(ori,opi, marker='o', linestyle=None, markersize=3, color='black')
- ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
+ ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
if len(names) > 1:
- plt.legend(loc='lower left', framealpha=0.5)
+ plt.legend(loc='lower left', framealpha=0.5)
ax1.set_xlabel('Recall')
ax1.set_ylabel('Precision')
ax1.set_xlim([0.0, 1.0])
- ax1.set_ylim([0.0, 1.0])
- if title is not None: ax1.set_title(title)
+ ax1.set_ylim([0.0, 1.0])
+ if title is not None: ax1.set_title(title)
# Annotates plot with F1-score iso-lines
ax2 = ax1.twinx()
f_scores = np.linspace(0.1, 0.9, num=9)
@@ -79,70 +79,70 @@ def precision_recall_f1iso(precision, recall, names, title=None):
y = f_score * x / (2 * x - f_score)
l, = plt.plot(x[y >= 0], y[y >= 0], color='green', alpha=0.1)
tick_locs.append(y[-1])
- tick_labels.append('%.1f' % f_score)
+ tick_labels.append('%.1f' % f_score)
ax2.tick_params(axis='y', which='both', pad=0, right=False, left=False)
ax2.set_ylabel('iso-F', color='green', alpha=0.3)
ax2.set_ylim([0.0, 1.0])
- ax2.yaxis.set_label_coords(1.015, 0.97)
- ax2.set_yticks(tick_locs) #notice these are invisible
+ ax2.yaxis.set_label_coords(1.015, 0.97)
+ ax2.set_yticks(tick_locs) #notice these are invisible
for k in ax2.set_yticklabels(tick_labels):
k.set_color('green')
k.set_alpha(0.3)
- k.set_size(8)
+ k.set_size(8)
# we should see some of axes 1 axes
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_position(('data', -0.015))
- ax1.spines['bottom'].set_position(('data', -0.015))
+ ax1.spines['bottom'].set_position(('data', -0.015))
# we shouldn't see any of axes 2 axes
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['left'].set_visible(False)
- ax2.spines['bottom'].set_visible(False)
- plt.tight_layout()
- return fig
+ ax2.spines['bottom'].set_visible(False)
+ plt.tight_layout()
+ return fig
def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_upper, re_lower, names, title=None):
"""
Author: Andre Anjos (andre.anjos@idiap.ch).
-
- Creates a precision-recall plot of the given data.
+
+ Creates a precision-recall plot of the given data.
The plot will be annotated with F1-score iso-lines (in which the F1-score
- maintains the same value)
-
+ maintains the same value)
+
Parameters
- ----------
+ ----------
precision : :py:class:`numpy.ndarray` or :py:class:`list`
A list of 1D np arrays containing the Y coordinates of the plot, or
the precision, or a 2D np array in which the rows correspond to each
- of the system's precision coordinates.
+ of the system's precision coordinates.
recall : :py:class:`numpy.ndarray` or :py:class:`list`
A list of 1D np arrays containing the X coordinates of the plot, or
the recall, or a 2D np array in which the rows correspond to each
- of the system's recall coordinates.
+ of the system's recall coordinates.
names : :py:class:`list`
An iterable over the names of each of the systems along the rows of
- ``precision`` and ``recall``
+ ``precision`` and ``recall``
title : :py:class:`str`, optional
- A title for the plot. If not set, omits the title
+ A title for the plot. If not set, omits the title
Returns
- -------
+ -------
matplotlib.figure.Figure
- A matplotlib figure you can save or display
- """
+ A matplotlib figure you can save or display
+ """
import matplotlib
matplotlib.use('agg')
- import matplotlib.pyplot as plt
+ import matplotlib.pyplot as plt
from itertools import cycle
- fig, ax1 = plt.subplots(1)
+ fig, ax1 = plt.subplots(1)
lines = ["-","--","-.",":"]
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
'#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
'#bcbd22', '#17becf']
colorcycler = cycle(colors)
linecycler = cycle(lines)
- for p, r, pu, pl, ru, rl, n in zip(precision, recall, pr_upper, pr_lower, re_upper, re_lower, names):
+ for p, r, pu, pl, ru, rl, n in zip(precision, recall, pr_upper, pr_lower, re_upper, re_lower, names):
# Plots only from the point where recall reaches its maximum, otherwise, we
# don't see a curve...
i = r.argmax()
@@ -151,24 +151,24 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
pui = pu[i:]
pli = pl[i:]
rui = ru[i:]
- rli = rl[i:]
+ rli = rl[i:]
valid = (pi+ri) > 0
- f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
+ f1 = 2 * (pi[valid]*ri[valid]) / (pi[valid]+ri[valid])
# optimal point along the curve
argmax = f1.argmax()
opi = pi[argmax]
ori = ri[argmax]
# Plot Recall/Precision as threshold changes
- ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
+ ax1.plot(ri[pi>0], pi[pi>0], next(linecycler), label='[F={:.4f}] {}'.format(f1.max(), n),)
ax1.plot(ori,opi, marker='o', linestyle=None, markersize=3, color='black')
# Plot confidence
# Upper bound
- #ax1.plot(r95ui[p95ui>0], p95ui[p95ui>0])
+ #ax1.plot(r95ui[p95ui>0], p95ui[p95ui>0])
# Lower bound
#ax1.plot(r95li[p95li>0], p95li[p95li>0])
# create the limiting polygon
vert_x = np.concatenate((rui[pui>0], rli[pli>0][::-1]))
- vert_y = np.concatenate((pui[pui>0], pli[pli>0][::-1]))
+ vert_y = np.concatenate((pui[pui>0], pli[pli>0][::-1]))
# hacky workaround to plot 2nd human
if np.isclose(np.mean(rui), rui[1], rtol=1e-05):
print('found human')
@@ -177,14 +177,14 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
p = plt.Polygon(np.column_stack((vert_x, vert_y)), facecolor=next(colorcycler), alpha=.2, edgecolor='none',lw=.2)
ax1.add_artist(p)
- ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
+ ax1.grid(linestyle='--', linewidth=1, color='gray', alpha=0.2)
if len(names) > 1:
- plt.legend(loc='lower left', framealpha=0.5)
+ plt.legend(loc='lower left', framealpha=0.5)
ax1.set_xlabel('Recall')
ax1.set_ylabel('Precision')
ax1.set_xlim([0.0, 1.0])
- ax1.set_ylim([0.0, 1.0])
- if title is not None: ax1.set_title(title)
+ ax1.set_ylim([0.0, 1.0])
+ if title is not None: ax1.set_title(title)
# Annotates plot with F1-score iso-lines
ax2 = ax1.twinx()
f_scores = np.linspace(0.1, 0.9, num=9)
@@ -195,45 +195,45 @@ def precision_recall_f1iso_confintval(precision, recall, pr_upper, pr_lower, re_
y = f_score * x / (2 * x - f_score)
l, = plt.plot(x[y >= 0], y[y >= 0], color='green', alpha=0.1)
tick_locs.append(y[-1])
- tick_labels.append('%.1f' % f_score)
+ tick_labels.append('%.1f' % f_score)
ax2.tick_params(axis='y', which='both', pad=0, right=False, left=False)
ax2.set_ylabel('iso-F', color='green', alpha=0.3)
ax2.set_ylim([0.0, 1.0])
- ax2.yaxis.set_label_coords(1.015, 0.97)
- ax2.set_yticks(tick_locs) #notice these are invisible
+ ax2.yaxis.set_label_coords(1.015, 0.97)
+ ax2.set_yticks(tick_locs) #notice these are invisible
for k in ax2.set_yticklabels(tick_labels):
k.set_color('green')
k.set_alpha(0.3)
- k.set_size(8)
+ k.set_size(8)
# we should see some of axes 1 axes
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.spines['left'].set_position(('data', -0.015))
- ax1.spines['bottom'].set_position(('data', -0.015))
+ ax1.spines['bottom'].set_position(('data', -0.015))
# we shouldn't see any of axes 2 axes
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.spines['left'].set_visible(False)
- ax2.spines['bottom'].set_visible(False)
- plt.tight_layout()
- return fig
+ ax2.spines['bottom'].set_visible(False)
+ plt.tight_layout()
+ return fig
def loss_curve(df, title):
""" Creates a loss curve given a Dataframe with column names:
``['avg. loss', 'median loss','lr','max memory']``
-
+
Parameters
----------
df : :py:class:`pandas.DataFrame`
-
+
Returns
-------
matplotlib.figure.Figure
- """
+ """
import matplotlib
matplotlib.use('agg')
- import matplotlib.pyplot as plt
+ import matplotlib.pyplot as plt
ax1 = df.plot(y="median loss", grid=True)
ax1.set_title(title)
ax1.set_ylabel('median loss')
@@ -241,7 +241,7 @@ def loss_curve(df, title):
ax2 = df['lr'].plot(secondary_y=True,legend=True,grid=True,)
ax2.set_ylabel('lr')
ax1.set_xlabel('epoch')
- plt.tight_layout()
+ plt.tight_layout()
fig = ax1.get_figure()
return fig
@@ -249,12 +249,12 @@ def loss_curve(df, title):
def read_metricscsv(file):
"""
Read precision and recall from csv file
-
+
Parameters
----------
file : str
path to file
-
+
Returns
-------
:py:class:`numpy.ndarray`
@@ -283,7 +283,7 @@ def read_metricscsv(file):
def plot_overview(outputfolders,title):
"""
Plots comparison chart of all trained models
-
+
Parameters
----------
outputfolder : list
@@ -303,7 +303,7 @@ def plot_overview(outputfolders,title):
names = []
params = []
for folder in outputfolders:
- # metrics
+ # metrics
metrics_path = os.path.join(folder,'results/Metrics.csv')
pr, re, pr_upper, pr_lower, re_upper, re_lower = read_metricscsv(metrics_path)
precisions.append(pr)
@@ -335,7 +335,7 @@ def metricsviz(dataset
,overlayed=True):
""" Visualizes true positives, false positives and false negatives
Default colors TP: Gray, FP: Cyan, FN: Orange
-
+
Parameters
----------
dataset : :py:class:`torch.utils.data.Dataset`
@@ -354,27 +354,27 @@ def metricsviz(dataset
name = sample[0]
img = VF.to_pil_image(sample[1]) # PIL Image
gt = sample[2].byte() # byte tensor
-
- # read metrics
+
+ # read metrics
metrics = pd.read_csv(os.path.join(output_path,'results','Metrics.csv'))
optimal_threshold = metrics['threshold'][metrics['f1_score'].idxmax()]
-
- # read probability output
+
+ # read probability output
pred = Image.open(os.path.join(output_path,'images',name))
pred = pred.convert(mode='L')
pred = VF.to_tensor(pred)
binary_pred = torch.gt(pred, optimal_threshold).byte()
-
+
# calc metrics
# equals and not-equals
equals = torch.eq(binary_pred, gt) # tensor
- notequals = torch.ne(binary_pred, gt) # tensor
- # true positives
+ notequals = torch.ne(binary_pred, gt) # tensor
+ # true positives
tp_tensor = (gt * binary_pred ) # tensor
tp_pil = VF.to_pil_image(tp_tensor.float())
tp_pil_colored = PIL.ImageOps.colorize(tp_pil, (0,0,0), tp_color)
- # false positives
- fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
+ # false positives
+ fp_tensor = torch.eq((binary_pred + tp_tensor), 1)
fp_pil = VF.to_pil_image(fp_tensor.float())
fp_pil_colored = PIL.ImageOps.colorize(fp_pil, (0,0,0), fp_color)
# false negatives
@@ -385,7 +385,7 @@ def metricsviz(dataset
# paste together
tp_pil_colored.paste(fp_pil_colored,mask=fp_pil)
tp_pil_colored.paste(fn_pil_colored,mask=fn_pil)
-
+
if overlayed:
tp_pil_colored = PIL.Image.blend(img, tp_pil_colored, 0.4)
img_metrics = pd.read_csv(os.path.join(output_path,'results',name+'.csv'))
@@ -396,15 +396,17 @@ def metricsviz(dataset
fnt = ImageFont.truetype('FreeMono.ttf', fnt_size)
draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
- # save to disk
+ # save to disk
overlayed_path = os.path.join(output_path,'tpfnfpviz')
- if not os.path.exists(overlayed_path): os.makedirs(overlayed_path)
- tp_pil_colored.save(os.path.join(overlayed_path,name))
+ fullpath = os.path.join(overlayed_path, name)
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir): os.makedirs(fulldir)
+ tp_pil_colored.save(fullpath)
def overlay(dataset, output_path):
"""Overlays prediction probabilities vessel tree with original test image.
-
+
Parameters
----------
dataset : :py:class:`torch.utils.data.Dataset`
@@ -416,8 +418,8 @@ def overlay(dataset, output_path):
# get sample
name = sample[0]
img = VF.to_pil_image(sample[1]) # PIL Image
-
- # read probability output
+
+ # read probability output
pred = Image.open(os.path.join(output_path,'images',name)).convert(mode='L')
# color and overlay
pred_green = PIL.ImageOps.colorize(pred, (0,0,0), (0,255,0))
@@ -430,14 +432,16 @@ def overlay(dataset, output_path):
#draw.text((0, 0),"F1: {:.4f}".format(f1),(255,255,255),font=fnt)
# save to disk
overlayed_path = os.path.join(output_path,'overlayed')
- if not os.path.exists(overlayed_path): os.makedirs(overlayed_path)
- overlayed.save(os.path.join(overlayed_path,name))
+ fullpath = os.path.join(overlayed_path, name)
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir): os.makedirs(fulldir)
+ overlayed.save(fullpath)
def savetransformedtest(dataset, output_path):
- """Save the test images as they are fed into the neural network.
+ """Save the test images as they are fed into the neural network.
Makes it easier to create overlay animations (e.g. slide)
-
+
Parameters
----------
dataset : :py:class:`torch.utils.data.Dataset`
@@ -449,8 +453,10 @@ def savetransformedtest(dataset, output_path):
# get sample
name = sample[0]
img = VF.to_pil_image(sample[1]) # PIL Image
-
+
# save to disk
testimg_path = os.path.join(output_path,'transformedtestimages')
- if not os.path.exists(testimg_path): os.makedirs(testimg_path)
- img.save(os.path.join(testimg_path,name))
+ fullpath = os.path.join(testimg_path, name)
+ fulldir = os.path.dirname(fullpath)
+ if not os.path.exists(fulldir): os.makedirs(fulldir)
+ img.save(fullpath)
diff --git a/conda/meta.yaml b/conda/meta.yaml
index 0a33ea32cb27975a95d66c3ac2d36b2a0aa13a03..1a7c95acb6da05634579c283c8de6248c82a7677 100644
--- a/conda/meta.yaml
+++ b/conda/meta.yaml
@@ -39,6 +39,7 @@ requirements:
- matplotlib
- tqdm
- tabulate
+ - bob.core
test:
imports:
@@ -75,4 +76,3 @@ about:
home: https://www.idiap.ch/software/bob/
license: GNU General Public License v3 (GPLv3)
license_family: GPL
- license_file: ../LICENSE
diff --git a/doc/api.rst b/doc/api.rst
index a6608a637c5a55b0e73a3d15c54364c4b8f37ffa..1eade48273e9a1117484ac0d14a7df6b510b5e3d 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -17,9 +17,11 @@ PyTorch ImageFolder Dataset
===========================
.. automodule:: bob.ip.binseg.data.imagefolder
+.. automodule:: bob.ip.binseg.data.imagefolderinference
+
Transforms
==========
-.. note::
+.. note::
All transforms work with :py:class:`PIL.Image.Image` objects. We make heavy use of the
`torchvision package`_
diff --git a/requirements.txt b/requirements.txt
index 719383e1b5ee6b907c96f6edf6e7da9de184b5a9..82b7843aa0e051bad5f22858752465620aca6772 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,3 +7,4 @@ pandas
matplotlib
tqdm
tabulate
+bob.core