doctr.utils#

This module regroups non-core features that are complementary to the rest of the package.

Visualization#

Easy-to-use functions to make sense of your model’s predictions.

visualize_page(

page,

image,

words_only=True,

display_artefacts=True,

scale=10,

interactive=True,

add_labels=True,

**kwargs,

)[source]#

Visualize a full page with predicted blocks, lines and words

>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from doctr.utils.visualization import visualize_page
>>> from doctr.models import ocr_db_crnn
>>> model = ocr_db_crnn(pretrained=True)
>>> input_page = (255 * np.random.rand(600, 800, 3)).astype(np.uint8)
>>> out = model([[input_page]])
>>> visualize_page(out[0].pages[0].export(), input_page)
>>> plt.show()

Parameters:

page (dict[str, Any]) – the exported Page of a Document
image (ndarray) – np array of the page, needs to have the same shape than page[‘dimensions’]
words_only (bool) – whether only words should be displayed
display_artefacts (bool) – whether artefacts should be displayed
scale (float) – figsize of the largest windows side
interactive (bool) – whether the plot should be interactive
add_labels (bool) – for static plot, adds text labels on top of bounding box
**kwargs (Any) – keyword arguments for the polygon patch

Returns:

the matplotlib figure

Return type:

Figure

Reconstitution#

synthesize_page( page, draw_proba=False, font_family=None, smoothing_factor=0.95, min_font_size=8, max_font_size=50, )[source]#

Draw a the content of the element page (OCR response) on a blank page.

Parameters:

page (dict[str, Any]) – exported Page object to represent
draw_proba (bool) – if True, draw words in colors to represent confidence. Blue: p=1, red: p=0
font_family (str | None) – family of the font
smoothing_factor (float) – factor to smooth the font size
min_font_size (int) – minimum font size
max_font_size (int) – maximum font size

Returns:

the synthesized page

Return type:

ndarray

Task evaluation#

Implementations of task-specific metrics to easily assess your model performances.

class TextMatch[source]#

Implements text match metric (word-level accuracy) for recognition task.

The raw aggregated metric is computed as follows:

\[\forall X, Y \in \mathcal{W}^N, TextMatch(X, Y) = \frac{1}{N} \sum\limits_{i=1}^N f_{Y_i}(X_i)\]

with the indicator function \(f_{a}\) defined as:

\[\begin{split}\forall a, x \in \mathcal{W}, f_a(x) = \left\{ \begin{array}{ll} 1 & \mbox{if } x = a \\ 0 & \mbox{otherwise.} \end{array} \right.\end{split}\]

where \(\mathcal{W}\) is the set of all possible character sequences, \(N\) is a strictly positive integer.

>>> from doctr.utils import TextMatch
>>> metric = TextMatch()
>>> metric.update(['Hello', 'world'], ['hello', 'world'])
>>> metric.summary()

update(gt, pred)[source]#

Update the state of the metric with new predictions

Parameters:

gt (list[str]) – list of groung-truth character sequences
pred (list[str]) – list of predicted character sequences

summary()[source]#

Computes the aggregated metrics

Returns:: a dictionary with the exact match score for the raw data, its lower-case counterpart, its anyascii counterpart and its lower-case anyascii counterpart
Return type:: dict[str, float]

class LocalizationConfusion(iou_thresh=0.5, use_polygons=False)[source]#

Implements common confusion metrics and mean IoU for localization evaluation.

The aggregated metrics are computed as follows:

\[\begin{split}\forall Y \in \mathcal{B}^N, \forall X \in \mathcal{B}^M, \\ Recall(X, Y) = \frac{1}{N} \sum\limits_{i=1}^N g_{X}(Y_i) \\ Precision(X, Y) = \frac{1}{M} \sum\limits_{i=1}^M g_{X}(Y_i) \\ meanIoU(X, Y) = \frac{1}{M} \sum\limits_{i=1}^M \max\limits_{j \in [1, N]} IoU(X_i, Y_j)\end{split}\]

with the function \(IoU(x, y)\) being the Intersection over Union between bounding boxes \(x\) and \(y\), and the function \(g_{X}\) defined as:

\[\begin{split}\forall y \in \mathcal{B}, g_X(y) = \left\{ \begin{array}{ll} 1 & \mbox{if } y\mbox{ has been assigned to any }(X_i)_i\mbox{ with an }IoU \geq 0.5 \\ 0 & \mbox{otherwise.} \end{array} \right.\end{split}\]

where \(\mathcal{B}\) is the set of possible bounding boxes, \(N\) (number of ground truths) and \(M\) (number of predictions) are strictly positive integers.

>>> import numpy as np
>>> from doctr.utils import LocalizationConfusion
>>> metric = LocalizationConfusion(iou_thresh=0.5)
>>> metric.update(np.asarray([[0, 0, 100, 100]]), np.asarray([[0, 0, 70, 70], [110, 95, 200, 150]]))
>>> metric.summary()

Parameters:

iou_thresh (float) – minimum IoU to consider a pair of prediction and ground truth as a match
use_polygons (bool) – if set to True, predictions and targets will be expected to have rotated format

update(gts, preds)[source]#

Updates the metric

Parameters:

gts (ndarray) – a set of relative bounding boxes either of shape (N, 4) or (N, 5) if they are rotated ones
preds (ndarray) – a set of relative bounding boxes either of shape (M, 4) or (M, 5) if they are rotated ones

summary()[source]#

Computes the aggregated metrics

Returns:: a tuple with the recall, precision and meanIoU scores
Return type:: tuple[float | None, float | None, float | None]

class OCRMetric(iou_thresh=0.5, use_polygons=False)[source]#

Implements an end-to-end OCR metric.

The aggregated metrics are computed as follows:

\[\begin{split}\forall (B, L) \in \mathcal{B}^N \times \mathcal{L}^N, \forall (\hat{B}, \hat{L}) \in \mathcal{B}^M \times \mathcal{L}^M, \\ Recall(B, \hat{B}, L, \hat{L}) = \frac{1}{N} \sum\limits_{i=1}^N h_{B,L}(\hat{B}_i, \hat{L}_i) \\ Precision(B, \hat{B}, L, \hat{L}) = \frac{1}{M} \sum\limits_{i=1}^M h_{B,L}(\hat{B}_i, \hat{L}_i) \\ meanIoU(B, \hat{B}) = \frac{1}{M} \sum\limits_{i=1}^M \max\limits_{j \in [1, N]} IoU(\hat{B}_i, B_j)\end{split}\]

with the function \(IoU(x, y)\) being the Intersection over Union between bounding boxes \(x\) and \(y\), and the function \(h_{B, L}\) defined as:

\[\begin{split}\forall (b, l) \in \mathcal{B} \times \mathcal{L}, h_{B,L}(b, l) = \left\{ \begin{array}{ll} 1 & \mbox{if } b\mbox{ has been assigned to a given }B_j\mbox{ with an } \\ & IoU \geq 0.5 \mbox{ and that for this assignment, } l = L_j\\ 0 & \mbox{otherwise.} \end{array} \right.\end{split}\]

where \(\mathcal{B}\) is the set of possible bounding boxes, \(\mathcal{L}\) is the set of possible character sequences, \(N\) (number of ground truths) and \(M\) (number of predictions) are strictly positive integers.

>>> import numpy as np
>>> from doctr.utils import OCRMetric
>>> metric = OCRMetric(iou_thresh=0.5)
>>> metric.update(np.asarray([[0, 0, 100, 100]]), np.asarray([[0, 0, 70, 70], [110, 95, 200, 150]]),
>>>               ['hello'], ['hello', 'world'])
>>> metric.summary()

Parameters:

iou_thresh (float) – minimum IoU to consider a pair of prediction and ground truth as a match
use_polygons (bool) – if set to True, predictions and targets will be expected to have rotated format

update(gt_boxes, pred_boxes, gt_labels, pred_labels)[source]#

Updates the metric

Parameters:

gt_boxes (ndarray) – a set of relative bounding boxes either of shape (N, 4) or (N, 5) if they are rotated ones
pred_boxes (ndarray) – a set of relative bounding boxes either of shape (M, 4) or (M, 5) if they are rotated ones
gt_labels (list[str]) – a list of N string labels
pred_labels (list[str]) – a list of M string labels

summary()[source]#

Computes the aggregated metrics

Returns:: a tuple with the recall & precision for each string comparison and the mean IoU
Return type:: tuple[dict[str, float | None], dict[str, float | None], float | None]

class DetectionMetric(iou_thresh=0.5, use_polygons=False)[source]#

Implements an object detection metric.

The aggregated metrics are computed as follows:

\[\begin{split}\forall (B, C) \in \mathcal{B}^N \times \mathcal{C}^N, \forall (\hat{B}, \hat{C}) \in \mathcal{B}^M \times \mathcal{C}^M, \\ Recall(B, \hat{B}, C, \hat{C}) = \frac{1}{N} \sum\limits_{i=1}^N h_{B,C}(\hat{B}_i, \hat{C}_i) \\ Precision(B, \hat{B}, C, \hat{C}) = \frac{1}{M} \sum\limits_{i=1}^M h_{B,C}(\hat{B}_i, \hat{C}_i) \\ meanIoU(B, \hat{B}) = \frac{1}{M} \sum\limits_{i=1}^M \max\limits_{j \in [1, N]} IoU(\hat{B}_i, B_j)\end{split}\]

with the function \(IoU(x, y)\) being the Intersection over Union between bounding boxes \(x\) and \(y\), and the function \(h_{B, C}\) defined as:

\[\begin{split}\forall (b, c) \in \mathcal{B} \times \mathcal{C}, h_{B,C}(b, c) = \left\{ \begin{array}{ll} 1 & \mbox{if } b\mbox{ has been assigned to a given }B_j\mbox{ with an } \\ & IoU \geq 0.5 \mbox{ and that for this assignment, } c = C_j\\ 0 & \mbox{otherwise.} \end{array} \right.\end{split}\]

where \(\mathcal{B}\) is the set of possible bounding boxes, \(\mathcal{C}\) is the set of possible class indices, \(N\) (number of ground truths) and \(M\) (number of predictions) are strictly positive integers.

>>> import numpy as np
>>> from doctr.utils import DetectionMetric
>>> metric = DetectionMetric(iou_thresh=0.5)
>>> metric.update(np.asarray([[0, 0, 100, 100]]), np.asarray([[0, 0, 70, 70], [110, 95, 200, 150]]),
>>>               np.zeros(1, dtype=np.int64), np.array([0, 1], dtype=np.int64))
>>> metric.summary()

Parameters:

iou_thresh (float) – minimum IoU to consider a pair of prediction and ground truth as a match
use_polygons (bool) – if set to True, predictions and targets will be expected to have rotated format

update(gt_boxes, pred_boxes, gt_labels, pred_labels)[source]#

Updates the metric

Parameters:

gt_boxes (ndarray) – a set of relative bounding boxes either of shape (N, 4) or (N, 5) if they are rotated ones
pred_boxes (ndarray) – a set of relative bounding boxes either of shape (M, 4) or (M, 5) if they are rotated ones
gt_labels (ndarray) – an array of class indices of shape (N,)
pred_labels (ndarray) – an array of class indices of shape (M,)

summary()[source]#

Computes the aggregated metrics

Returns:: a tuple with the recall & precision for each class prediction and the mean IoU
Return type:: tuple[float | None, float | None, float | None]