[Visualization utils] Add visualization utils for plotting images(plain, with bounding boxes and segmentation masks)

keras/__init__.py

@@ -50,6 +50,7 @@
 from keras.api import tree
 from keras.api import utils
 from keras.api import version
+from keras.api import visualization
 
 # END DO NOT EDIT.
 

keras/api/__init__.py

@@ -31,6 +31,7 @@
 from keras.api import saving
 from keras.api import tree
 from keras.api import utils
+from keras.api import visualization
 from keras.src.backend import Variable
 from keras.src.backend import device
 from keras.src.backend import name_scope

keras/api/_tf_keras/keras/__init__.py

@@ -24,6 +24,7 @@
 from keras.api import regularizers
 from keras.api import tree
 from keras.api import utils
+from keras.api import visualization
 from keras.api._tf_keras.keras import backend
 from keras.api._tf_keras.keras import layers
 from keras.api._tf_keras.keras import losses

keras/api/_tf_keras/keras/visualization/__init__.py

@@ -0,0 +1,17 @@
+"""DO NOT EDIT.
+
+This file was autogenerated. Do not edit it by hand,
+since your modifications would be overwritten.
+"""
+
+from keras.src.visualization.draw_bounding_boxes import draw_bounding_boxes
+from keras.src.visualization.draw_segmentation_masks import (
+    draw_segmentation_masks,
+)
+from keras.src.visualization.plot_bounding_box_gallery import (
+    plot_bounding_box_gallery,
+)
+from keras.src.visualization.plot_image_gallery import plot_image_gallery
+from keras.src.visualization.plot_segmentation_mask_gallery import (
+    plot_segmentation_mask_gallery,
+)

keras/api/visualization/__init__.py

@@ -0,0 +1,17 @@
+"""DO NOT EDIT.
+
+This file was autogenerated. Do not edit it by hand,
+since your modifications would be overwritten.
+"""
+
+from keras.src.visualization.draw_bounding_boxes import draw_bounding_boxes
+from keras.src.visualization.draw_segmentation_masks import (
+    draw_segmentation_masks,
+)
+from keras.src.visualization.plot_bounding_box_gallery import (
+    plot_bounding_box_gallery,
+)
+from keras.src.visualization.plot_image_gallery import plot_image_gallery
+from keras.src.visualization.plot_segmentation_mask_gallery import (
+    plot_segmentation_mask_gallery,
+)

keras/src/__init__.py

@@ -10,6 +10,7 @@
 from keras.src import optimizers
 from keras.src import regularizers
 from keras.src import utils
+from keras.src import visualization
 from keras.src.backend import KerasTensor
 from keras.src.layers import Input
 from keras.src.layers import Layer

keras/src/visualization/__init__.py

@@ -0,0 +1,2 @@
+from keras.src.visualization import draw_bounding_boxes
+from keras.src.visualization import plot_image_gallery

keras/src/visualization/draw_bounding_boxes.py

@@ -0,0 +1,177 @@
+import numpy as np
+
+from keras.src import backend
+from keras.src import ops
+from keras.src.api_export import keras_export
+from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (  # noqa: E501
+    convert_format,
+)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None
+
+
+@keras_export("keras.visualization.draw_bounding_boxes")
+def draw_bounding_boxes(
+    images,
+    bounding_boxes,
+    bounding_box_format,
+    class_mapping=None,
+    color=(128, 128, 128),
+    line_thickness=2,
+    text_thickness=1,
+    font_scale=1.0,
+    data_format=None,
+):
+    """Draws bounding boxes on images.
+
+    This function draws bounding boxes on a batch of images.  It supports
+    different bounding box formats and can optionally display class labels
+    and confidences.
+
+    Args:
+        images: A batch of images as a 4D tensor or NumPy array. Shape should be
+            `(batch_size, height, width, channels)`.
+        bounding_boxes: A dictionary containing bounding box data.  Should have
+            the following keys:
+            - `boxes`: A tensor or array of shape `(batch_size, num_boxes, 4)`
+               containing the bounding box coordinates in the specified format.
+            - `labels`: A tensor or array of shape `(batch_size, num_boxes)`
+              containing the class labels for each bounding box.
+            - `confidences` (Optional): A tensor or array of shape
+               `(batch_size, num_boxes)` containing the confidence scores for
+               each bounding box.
+        bounding_box_format: A string specifying the format of the bounding
+            boxes. Refer [keras-io](TODO)
+        class_mapping: A dictionary mapping class IDs (integers) to class labels
+            (strings).  Used to display class labels next to the bounding boxes.
+            Defaults to None (no labels displayed).
+        color: A tuple or list representing the RGB color of the bounding boxes.
+            For example, `(255, 0, 0)` for red. Defaults to `(128, 128, 128)`.
+        line_thickness: An integer specifying the thickness of the bounding box
+            lines. Defaults to `2`.
+        text_thickness: An integer specifying the thickness of the text labels.
+            Defaults to `1`.
+        font_scale: A float specifying the scale of the font used for text
+            labels. Defaults to `1.0`.
+        data_format: A string, either `"channels_last"` or `"channels_first"`,
+            specifying the order of dimensions in the input images. Defaults to
+            the `image_data_format` value found in your Keras config file at
+            `~/.keras/keras.json`. If you never set it, then it will be
+            "channels_last".
+
+    Returns:
+        A NumPy array of the annotated images with the bounding boxes drawn.
+        The array will have the same shape as the input `images`.
+
+    Raises:
+        ValueError: If `images` is not a 4D tensor/array, if `bounding_boxes` is
+            not a dictionary, or if `bounding_boxes` does not contain `"boxes"`
+            and `"labels"` keys.
+        TypeError: If `bounding_boxes` is not a dictionary.
+        ImportError: If `cv2` (OpenCV) is not installed.
+    """
+
+    if cv2 is None:
+        raise ImportError(
+            "The `draw_bounding_boxes` function requires the `cv2` package "
+            " (OpenCV). Please install it with `pip install opencv-python`."
+        )
+
+    class_mapping = class_mapping or {}
+    text_thickness = (
+        text_thickness or line_thickness
+    )  # Default text_thickness if not provided.
+    data_format = data_format or backend.image_data_format()
+    images_shape = ops.shape(images)
+    if len(images_shape) != 4:
+        raise ValueError(
+            "`images` must be batched 4D tensor. "
+            f"Received: images.shape={images_shape}"
+        )
+    if not isinstance(bounding_boxes, dict):
+        raise TypeError(
+            "`bounding_boxes` should be a dict. "
+            f"Received: bounding_boxes={bounding_boxes} of type "
+            f"{type(bounding_boxes)}"
+        )
+    if "boxes" not in bounding_boxes or "labels" not in bounding_boxes:
+        raise ValueError(
+            "`bounding_boxes` should be a dict containing 'boxes' and "
+            f"'labels' keys. Received: bounding_boxes={bounding_boxes}"
+        )
+    if data_format == "channels_last":
+        h_axis = -3
+        w_axis = -2
+    else:
+        h_axis = -2
+        w_axis = -1
+    height = images_shape[h_axis]
+    width = images_shape[w_axis]
+    bounding_boxes = bounding_boxes.copy()
+    bounding_boxes = convert_format(
+        bounding_boxes, bounding_box_format, "xyxy", height, width
+    )
+
+    # To numpy array
+    images = ops.convert_to_numpy(images).astype("uint8")
+    boxes = ops.convert_to_numpy(bounding_boxes["boxes"])
+    labels = ops.convert_to_numpy(bounding_boxes["labels"])
+    if "confidences" in bounding_boxes:
+        confidences = ops.convert_to_numpy(bounding_boxes["confidences"])
+    else:
+        confidences = None
+
+    result = []
+    batch_size = images.shape[0]
+    for i in range(batch_size):
+        _image = images[i]
+        _box = boxes[i]
+        _class = labels[i]
+        for box_i in range(_box.shape[0]):
+            x1, y1, x2, y2 = _box[box_i].astype("int32")
+            c = _class[box_i].astype("int32")
+            if c == -1:
+                continue
+            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+            c = int(c)
+            # Draw bounding box
+            cv2.rectangle(_image, (x1, y1), (x2, y2), color, line_thickness)
+
+            if c in class_mapping:
+                label = class_mapping[c]
+                if confidences is not None:
+                    conf = confidences[i][box_i]
+                    label = f"{label} | {conf:.2f}"
+
+                font_x1, font_y1 = _find_text_location(
+                    x1, y1, font_scale, text_thickness
+                )
+                cv2.putText(
+                    img=_image,
+                    text=label,
+                    org=(font_x1, font_y1),
+                    fontFace=cv2.FONT_HERSHEY_SIMPLEX,
+                    fontScale=font_scale,
+                    color=color,
+                    thickness=text_thickness,
+                )
+        result.append(_image)
+    return np.stack(result, axis=0)
+
+
+def _find_text_location(x, y, font_scale, thickness):
+    font_height = int(font_scale * 12)
+    target_y = y - 8
+    if target_y - (2 * font_height) > 0:
+        return x, y - 8
+
+    line_offset = thickness
+    static_offset = 3
+
+    return (
+        x + static_offset,
+        y + (2 * font_height) + line_offset + static_offset,
+    )

keras/src/visualization/draw_segmentation_masks.py

@@ -0,0 +1,109 @@
+import numpy as np
+
+from keras.src import backend
+from keras.src import ops
+from keras.src.api_export import keras_export
+
+
+@keras_export("keras.visualization.draw_segmentation_masks")
+def draw_segmentation_masks(
+    images,
+    segmentation_masks,
+    num_classes=None,
+    color_mapping=None,
+    alpha=0.8,
+    blend=True,
+    ignore_index=-1,
+    data_format=None,
+):
+    """Draws segmentation masks on images.
+
+    The function overlays segmentation masks on the input images.
+    The masks are blended with the images using the specified alpha value.
+
+    Args:
+        images: A batch of images as a 4D tensor or NumPy array. Shape
+            should be (batch_size, height, width, channels).
+        segmentation_masks: A batch of segmentation masks as a 3D or 4D tensor
+            or NumPy array.  Shape should be (batch_size, height, width) or
+            (batch_size, height, width, 1). The values represent class indices
+            starting from 1 up to `num_classes`. Class 0 is reserved for
+            the background and will be ignored if `ignore_index` is not 0.
+        num_classes: The number of segmentation classes. If `None`, it is
+            inferred from the maximum value in `segmentation_masks`.
+        color_mapping: A dictionary mapping class indices to RGB colors.
+            If `None`, a default color palette is generated. The keys should be
+            integers starting from 1 up to `num_classes`.
+        alpha: The opacity of the segmentation masks. Must be in the range
+            `[0, 1]`.
+        blend: Whether to blend the masks with the input image using the
+            `alpha` value. If `False`, the masks are drawn directly on the
+            images without blending. Defaults to `True`.
+        ignore_index: The class index to ignore. Mask pixels with this value
+            will not be drawn.  Defaults to -1.
+        data_format: Image data format, either `"channels_last"` or
+            `"channels_first"`. Defaults to the `image_data_format` value found
+            in your Keras config file at `~/.keras/keras.json`. If you never
+            set it, then it will be `"channels_last"`.
+
+    Returns:
+        A NumPy array of the images with the segmentation masks overlaid.
+
+    Raises:
+        ValueError: If the input `images` is not a 4D tensor or NumPy array.
+        TypeError: If the input `segmentation_masks` is not an integer type.
+    """
+    data_format = data_format or backend.image_data_format()
+    images_shape = ops.shape(images)
+    if len(images_shape) != 4:
+        raise ValueError(
+            "`images` must be batched 4D tensor. "
+            f"Received: images.shape={images_shape}"
+        )
+    if data_format == "channels_first":
+        images = ops.transpose(images, (0, 2, 3, 1))
+        segmentation_masks = ops.transpose(segmentation_masks, (0, 2, 3, 1))
+    images = ops.convert_to_tensor(images, dtype="float32")
+    segmentation_masks = ops.convert_to_tensor(segmentation_masks)
+
+    if not backend.is_int_dtype(segmentation_masks.dtype):
+        dtype = backend.standardize_dtype(segmentation_masks.dtype)
+        raise TypeError(
+            "`segmentation_masks` must be in integer dtype. "
+            f"Received: segmentation_masks.dtype={dtype}"
+        )
+
+    # Infer num_classes
+    if num_classes is None:
+        num_classes = int(ops.convert_to_numpy(ops.max(segmentation_masks)))
+    if color_mapping is None:
+        colors = _generate_color_palette(num_classes)
+    else:
+        colors = [color_mapping[i] for i in range(num_classes)]
+    valid_masks = ops.not_equal(segmentation_masks, ignore_index)
+    valid_masks = ops.squeeze(valid_masks, axis=-1)
+    segmentation_masks = ops.one_hot(segmentation_masks, num_classes)
+    segmentation_masks = segmentation_masks[..., 0, :]
+    segmentation_masks = ops.convert_to_numpy(segmentation_masks)
+
+    # Replace class with color
+    masks = segmentation_masks
+    masks = np.transpose(masks, axes=(3, 0, 1, 2)).astype("bool")
+    images_to_draw = ops.convert_to_numpy(images).copy()
+    for mask, color in zip(masks, colors):
+        color = np.array(color, dtype=images_to_draw.dtype)
+        images_to_draw[mask, ...] = color[None, :]
+    images_to_draw = ops.convert_to_tensor(images_to_draw)
+    outputs = ops.cast(images_to_draw, dtype="float32")
+
+    if blend:
+        outputs = images * (1 - alpha) + outputs * alpha
+        outputs = ops.where(valid_masks[..., None], outputs, images)
+        outputs = ops.cast(outputs, dtype="uint8")
+        outputs = ops.convert_to_numpy(outputs)
+    return outputs
+
+
+def _generate_color_palette(num_classes: int):
+    palette = np.array([2**25 - 1, 2**15 - 1, 2**21 - 1])
+    return [((i * palette) % 255).tolist() for i in range(num_classes)]