Source code for detectron2.utils.visualizer

# Copyright (c) Facebook, Inc. and its affiliates.
import colorsys
import logging
import math
import numpy as np
from enum import Enum, unique
import cv2
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import pycocotools.mask as mask_util
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image

from detectron2.data import MetadataCatalog
from detectron2.structures import BitMasks, Boxes, BoxMode, Keypoints, PolygonMasks, RotatedBoxes
from detectron2.utils.file_io import PathManager

from .colormap import random_color

logger = logging.getLogger(__name__)

__all__ = ["ColorMode", "VisImage", "Visualizer"]


_SMALL_OBJECT_AREA_THRESH = 1000
_LARGE_MASK_AREA_THRESH = 120000
_OFF_WHITE = (1.0, 1.0, 240.0 / 255)
_BLACK = (0, 0, 0)
_RED = (1.0, 0, 0)

_KEYPOINT_THRESHOLD = 0.05


[docs]@unique class ColorMode(Enum): """ Enum of different color modes to use for instance visualizations. """ IMAGE = 0 """ Picks a random color for every instance and overlay segmentations with low opacity. """ SEGMENTATION = 1 """ Let instances of the same category have similar colors (from metadata.thing_colors), and overlay them with high opacity. This provides more attention on the quality of segmentation. """ IMAGE_BW = 2 """ Same as IMAGE, but convert all areas without masks to gray-scale. Only available for drawing per-instance mask predictions. """
class GenericMask: """ Attribute: polygons (list[ndarray]): list[ndarray]: polygons for this mask. Each ndarray has format [x, y, x, y, ...] mask (ndarray): a binary mask """ def __init__(self, mask_or_polygons, height, width): self._mask = self._polygons = self._has_holes = None self.height = height self.width = width m = mask_or_polygons if isinstance(m, dict): # RLEs assert "counts" in m and "size" in m if isinstance(m["counts"], list): # uncompressed RLEs h, w = m["size"] assert h == height and w == width m = mask_util.frPyObjects(m, h, w) self._mask = mask_util.decode(m)[:, :] return if isinstance(m, list): # list[ndarray] self._polygons = [np.asarray(x).reshape(-1) for x in m] return if isinstance(m, np.ndarray): # assumed to be a binary mask assert m.shape[1] != 2, m.shape assert m.shape == ( height, width, ), f"mask shape: {m.shape}, target dims: {height}, {width}" self._mask = m.astype("uint8") return raise ValueError("GenericMask cannot handle object {} of type '{}'".format(m, type(m))) @property def mask(self): if self._mask is None: self._mask = self.polygons_to_mask(self._polygons) return self._mask @property def polygons(self): if self._polygons is None: self._polygons, self._has_holes = self.mask_to_polygons(self._mask) return self._polygons @property def has_holes(self): if self._has_holes is None: if self._mask is not None: self._polygons, self._has_holes = self.mask_to_polygons(self._mask) else: self._has_holes = False # if original format is polygon, does not have holes return self._has_holes def mask_to_polygons(self, mask): # cv2.RETR_CCOMP flag retrieves all the contours and arranges them to a 2-level # hierarchy. External contours (boundary) of the object are placed in hierarchy-1. # Internal contours (holes) are placed in hierarchy-2. # cv2.CHAIN_APPROX_NONE flag gets vertices of polygons from contours. mask = np.ascontiguousarray(mask) # some versions of cv2 does not support incontiguous arr res = cv2.findContours(mask.astype("uint8"), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE) hierarchy = res[-1] if hierarchy is None: # empty mask return [], False has_holes = (hierarchy.reshape(-1, 4)[:, 3] >= 0).sum() > 0 res = res[-2] res = [x.flatten() for x in res] # These coordinates from OpenCV are integers in range [0, W-1 or H-1]. # We add 0.5 to turn them into real-value coordinate space. A better solution # would be to first +0.5 and then dilate the returned polygon by 0.5. res = [x + 0.5 for x in res if len(x) >= 6] return res, has_holes def polygons_to_mask(self, polygons): rle = mask_util.frPyObjects(polygons, self.height, self.width) rle = mask_util.merge(rle) return mask_util.decode(rle)[:, :] def area(self): return self.mask.sum() def bbox(self): p = mask_util.frPyObjects(self.polygons, self.height, self.width) p = mask_util.merge(p) bbox = mask_util.toBbox(p) bbox[2] += bbox[0] bbox[3] += bbox[1] return bbox class _PanopticPrediction: """ Unify different panoptic annotation/prediction formats """ def __init__(self, panoptic_seg, segments_info, metadata=None): if segments_info is None: assert metadata is not None # If "segments_info" is None, we assume "panoptic_img" is a # H*W int32 image storing the panoptic_id in the format of # category_id * label_divisor + instance_id. We reserve -1 for # VOID label. label_divisor = metadata.label_divisor segments_info = [] for panoptic_label in np.unique(panoptic_seg.numpy()): if panoptic_label == -1: # VOID region. continue pred_class = panoptic_label // label_divisor isthing = pred_class in metadata.thing_dataset_id_to_contiguous_id.values() segments_info.append( { "id": int(panoptic_label), "category_id": int(pred_class), "isthing": bool(isthing), } ) del metadata self._seg = panoptic_seg self._sinfo = {s["id"]: s for s in segments_info} # seg id -> seg info segment_ids, areas = torch.unique(panoptic_seg, sorted=True, return_counts=True) areas = areas.numpy() sorted_idxs = np.argsort(-areas) self._seg_ids, self._seg_areas = segment_ids[sorted_idxs], areas[sorted_idxs] self._seg_ids = self._seg_ids.tolist() for sid, area in zip(self._seg_ids, self._seg_areas): if sid in self._sinfo: self._sinfo[sid]["area"] = float(area) def non_empty_mask(self): """ Returns: (H, W) array, a mask for all pixels that have a prediction """ empty_ids = [] for id in self._seg_ids: if id not in self._sinfo: empty_ids.append(id) if len(empty_ids) == 0: return np.zeros(self._seg.shape, dtype=np.uint8) assert ( len(empty_ids) == 1 ), ">1 ids corresponds to no labels. This is currently not supported" return (self._seg != empty_ids[0]).numpy().astype(bool) def semantic_masks(self): for sid in self._seg_ids: sinfo = self._sinfo.get(sid) if sinfo is None or sinfo["isthing"]: # Some pixels (e.g. id 0 in PanopticFPN) have no instance or semantic predictions. continue yield (self._seg == sid).numpy().astype(bool), sinfo def instance_masks(self): for sid in self._seg_ids: sinfo = self._sinfo.get(sid) if sinfo is None or not sinfo["isthing"]: continue mask = (self._seg == sid).numpy().astype(bool) if mask.sum() > 0: yield mask, sinfo def _create_text_labels(classes, scores, class_names, is_crowd=None): """ Args: classes (list[int] or None): scores (list[float] or None): class_names (list[str] or None): is_crowd (list[bool] or None): Returns: list[str] or None """ labels = None if classes is not None: if class_names is not None and len(class_names) > 0: labels = [class_names[i] for i in classes] else: labels = [str(i) for i in classes] if scores is not None: if labels is None: labels = ["{:.0f}%".format(s * 100) for s in scores] else: labels = ["{} {:.0f}%".format(l, s * 100) for l, s in zip(labels, scores)] if labels is not None and is_crowd is not None: labels = [l + ("|crowd" if crowd else "") for l, crowd in zip(labels, is_crowd)] return labels
[docs]class VisImage:
[docs] def __init__(self, img, scale=1.0): """ Args: img (ndarray): an RGB image of shape (H, W, 3) in range [0, 255]. scale (float): scale the input image """ self.img = img self.scale = scale self.width, self.height = img.shape[1], img.shape[0] self._setup_figure(img)
def _setup_figure(self, img): """ Args: Same as in :meth:`__init__()`. Returns: fig (matplotlib.pyplot.figure): top level container for all the image plot elements. ax (matplotlib.pyplot.Axes): contains figure elements and sets the coordinate system. """ fig = mplfigure.Figure(frameon=False) self.dpi = fig.get_dpi() # add a small 1e-2 to avoid precision lost due to matplotlib's truncation # (https://github.com/matplotlib/matplotlib/issues/15363) fig.set_size_inches( (self.width * self.scale + 1e-2) / self.dpi, (self.height * self.scale + 1e-2) / self.dpi, ) self.canvas = FigureCanvasAgg(fig) # self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig) ax = fig.add_axes([0.0, 0.0, 1.0, 1.0]) ax.axis("off") self.fig = fig self.ax = ax self.reset_image(img)
[docs] def reset_image(self, img): """ Args: img: same as in __init__ """ img = img.astype("uint8") self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
[docs] def save(self, filepath): """ Args: filepath (str): a string that contains the absolute path, including the file name, where the visualized image will be saved. """ self.fig.savefig(filepath)
[docs] def get_image(self): """ Returns: ndarray: the visualized image of shape (H, W, 3) (RGB) in uint8 type. The shape is scaled w.r.t the input image using the given `scale` argument. """ canvas = self.canvas s, (width, height) = canvas.print_to_buffer() # buf = io.BytesIO() # works for cairo backend # canvas.print_rgba(buf) # width, height = self.width, self.height # s = buf.getvalue() buffer = np.frombuffer(s, dtype="uint8") img_rgba = buffer.reshape(height, width, 4) rgb, alpha = np.split(img_rgba, [3], axis=2) return rgb.astype("uint8")
[docs]class Visualizer: """ Visualizer that draws data about detection/segmentation on images. It contains methods like `draw_{text,box,circle,line,binary_mask,polygon}` that draw primitive objects to images, as well as high-level wrappers like `draw_{instance_predictions,sem_seg,panoptic_seg_predictions,dataset_dict}` that draw composite data in some pre-defined style. Note that the exact visualization style for the high-level wrappers are subject to change. Style such as color, opacity, label contents, visibility of labels, or even the visibility of objects themselves (e.g. when the object is too small) may change according to different heuristics, as long as the results still look visually reasonable. To obtain a consistent style, you can implement custom drawing functions with the abovementioned primitive methods instead. If you need more customized visualization styles, you can process the data yourself following their format documented in tutorials (:doc:`/tutorials/models`, :doc:`/tutorials/datasets`). This class does not intend to satisfy everyone's preference on drawing styles. This visualizer focuses on high rendering quality rather than performance. It is not designed to be used for real-time applications. """ # TODO implement a fast, rasterized version using OpenCV
[docs] def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE): """ Args: img_rgb: a numpy array of shape (H, W, C), where H and W correspond to the height and width of the image respectively. C is the number of color channels. The image is required to be in RGB format since that is a requirement of the Matplotlib library. The image is also expected to be in the range [0, 255]. metadata (Metadata): dataset metadata (e.g. class names and colors) instance_mode (ColorMode): defines one of the pre-defined style for drawing instances on an image. """ self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8) if metadata is None: metadata = MetadataCatalog.get("__nonexist__") self.metadata = metadata self.output = VisImage(self.img, scale=scale) self.cpu_device = torch.device("cpu") # too small texts are useless, therefore clamp to 9 self._default_font_size = max( np.sqrt(self.output.height * self.output.width) // 90, 10 // scale ) self._instance_mode = instance_mode self.keypoint_threshold = _KEYPOINT_THRESHOLD
[docs] def draw_instance_predictions(self, predictions): """ Draw instance-level prediction results on an image. Args: predictions (Instances): the output of an instance detection/segmentation model. Following fields will be used to draw: "pred_boxes", "pred_classes", "scores", "pred_masks" (or "pred_masks_rle"). Returns: output (VisImage): image object with visualizations. """ boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None scores = predictions.scores if predictions.has("scores") else None classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None labels = _create_text_labels(classes, scores, self.metadata.get("thing_classes", None)) keypoints = predictions.pred_keypoints if predictions.has("pred_keypoints") else None if predictions.has("pred_masks"): masks = np.asarray(predictions.pred_masks) masks = [GenericMask(x, self.output.height, self.output.width) for x in masks] else: masks = None if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): colors = [ self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes ] alpha = 0.8 else: colors = None alpha = 0.5 if self._instance_mode == ColorMode.IMAGE_BW: self.output.reset_image( self._create_grayscale_image( (predictions.pred_masks.any(dim=0) > 0).numpy() if predictions.has("pred_masks") else None ) ) alpha = 0.3 self.overlay_instances( masks=masks, boxes=boxes, labels=labels, keypoints=keypoints, assigned_colors=colors, alpha=alpha, ) return self.output
[docs] def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8): """ Draw semantic segmentation predictions/labels. Args: sem_seg (Tensor or ndarray): the segmentation of shape (H, W). Each value is the integer label of the pixel. area_threshold (int): segments with less than `area_threshold` are not drawn. alpha (float): the larger it is, the more opaque the segmentations are. Returns: output (VisImage): image object with visualizations. """ if isinstance(sem_seg, torch.Tensor): sem_seg = sem_seg.numpy() labels, areas = np.unique(sem_seg, return_counts=True) sorted_idxs = np.argsort(-areas).tolist() labels = labels[sorted_idxs] for label in filter(lambda l: l < len(self.metadata.stuff_classes), labels): try: mask_color = [x / 255 for x in self.metadata.stuff_colors[label]] except (AttributeError, IndexError): mask_color = None binary_mask = (sem_seg == label).astype(np.uint8) text = self.metadata.stuff_classes[label] self.draw_binary_mask( binary_mask, color=mask_color, edge_color=_OFF_WHITE, text=text, alpha=alpha, area_threshold=area_threshold, ) return self.output
[docs] def draw_panoptic_seg(self, panoptic_seg, segments_info, area_threshold=None, alpha=0.7): """ Draw panoptic prediction annotations or results. Args: panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment. segments_info (list[dict] or None): Describe each segment in `panoptic_seg`. If it is a ``list[dict]``, each dict contains keys "id", "category_id". If None, category id of each pixel is computed by ``pixel // metadata.label_divisor``. area_threshold (int): stuff segments with less than `area_threshold` are not drawn. Returns: output (VisImage): image object with visualizations. """ pred = _PanopticPrediction(panoptic_seg, segments_info, self.metadata) if self._instance_mode == ColorMode.IMAGE_BW: self.output.reset_image(self._create_grayscale_image(pred.non_empty_mask())) # draw mask for all semantic segments first i.e. "stuff" for mask, sinfo in pred.semantic_masks(): category_idx = sinfo["category_id"] try: mask_color = [x / 255 for x in self.metadata.stuff_colors[category_idx]] except AttributeError: mask_color = None text = self.metadata.stuff_classes[category_idx] self.draw_binary_mask( mask, color=mask_color, edge_color=_OFF_WHITE, text=text, alpha=alpha, area_threshold=area_threshold, ) # draw mask for all instances second all_instances = list(pred.instance_masks()) if len(all_instances) == 0: return self.output masks, sinfo = list(zip(*all_instances)) category_ids = [x["category_id"] for x in sinfo] try: scores = [x["score"] for x in sinfo] except KeyError: scores = None labels = _create_text_labels( category_ids, scores, self.metadata.thing_classes, [x.get("iscrowd", 0) for x in sinfo] ) try: colors = [ self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids ] except AttributeError: colors = None self.overlay_instances(masks=masks, labels=labels, assigned_colors=colors, alpha=alpha) return self.output
draw_panoptic_seg_predictions = draw_panoptic_seg # backward compatibility
[docs] def draw_dataset_dict(self, dic): """ Draw annotations/segmentations in Detectron2 Dataset format. Args: dic (dict): annotation/segmentation data of one image, in Detectron2 Dataset format. Returns: output (VisImage): image object with visualizations. """ annos = dic.get("annotations", None) if annos: if "segmentation" in annos[0]: masks = [x["segmentation"] for x in annos] else: masks = None if "keypoints" in annos[0]: keypts = [x["keypoints"] for x in annos] keypts = np.array(keypts).reshape(len(annos), -1, 3) else: keypts = None boxes = [ BoxMode.convert(x["bbox"], x["bbox_mode"], BoxMode.XYXY_ABS) if len(x["bbox"]) == 4 else x["bbox"] for x in annos ] colors = None category_ids = [x["category_id"] for x in annos] if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"): colors = [ self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in category_ids ] names = self.metadata.get("thing_classes", None) labels = _create_text_labels( category_ids, scores=None, class_names=names, is_crowd=[x.get("iscrowd", 0) for x in annos], ) self.overlay_instances( labels=labels, boxes=boxes, masks=masks, keypoints=keypts, assigned_colors=colors ) sem_seg = dic.get("sem_seg", None) if sem_seg is None and "sem_seg_file_name" in dic: with PathManager.open(dic["sem_seg_file_name"], "rb") as f: sem_seg = Image.open(f) sem_seg = np.asarray(sem_seg, dtype="uint8") if sem_seg is not None: self.draw_sem_seg(sem_seg, area_threshold=0, alpha=0.5) pan_seg = dic.get("pan_seg", None) if pan_seg is None and "pan_seg_file_name" in dic: with PathManager.open(dic["pan_seg_file_name"], "rb") as f: pan_seg = Image.open(f) pan_seg = np.asarray(pan_seg) from panopticapi.utils import rgb2id pan_seg = rgb2id(pan_seg) if pan_seg is not None: segments_info = dic["segments_info"] pan_seg = torch.tensor(pan_seg) self.draw_panoptic_seg(pan_seg, segments_info, area_threshold=0, alpha=0.5) return self.output
[docs] def overlay_instances( self, *, boxes=None, labels=None, masks=None, keypoints=None, assigned_colors=None, alpha=0.5, ): """ Args: boxes (Boxes, RotatedBoxes or ndarray): either a :class:`Boxes`, or an Nx4 numpy array of XYXY_ABS format for the N objects in a single image, or a :class:`RotatedBoxes`, or an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format for the N objects in a single image, labels (list[str]): the text to be displayed for each instance. masks (masks-like object): Supported types are: * :class:`detectron2.structures.PolygonMasks`, :class:`detectron2.structures.BitMasks`. * list[list[ndarray]]: contains the segmentation masks for all objects in one image. The first level of the list corresponds to individual instances. The second level to all the polygon that compose the instance, and the third level to the polygon coordinates. The third level should have the format of [x0, y0, x1, y1, ..., xn, yn] (n >= 3). * list[ndarray]: each ndarray is a binary mask of shape (H, W). * list[dict]: each dict is a COCO-style RLE. keypoints (Keypoint or array like): an array-like object of shape (N, K, 3), where the N is the number of instances and K is the number of keypoints. The last dimension corresponds to (x, y, visibility or score). assigned_colors (list[matplotlib.colors]): a list of colors, where each color corresponds to each mask or box in the image. Refer to 'matplotlib.colors' for full list of formats that the colors are accepted in. Returns: output (VisImage): image object with visualizations. """ num_instances = 0 if boxes is not None: boxes = self._convert_boxes(boxes) num_instances = len(boxes) if masks is not None: masks = self._convert_masks(masks) if num_instances: assert len(masks) == num_instances else: num_instances = len(masks) if keypoints is not None: if num_instances: assert len(keypoints) == num_instances else: num_instances = len(keypoints) keypoints = self._convert_keypoints(keypoints) if labels is not None: assert len(labels) == num_instances if assigned_colors is None: assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] if num_instances == 0: return self.output if boxes is not None and boxes.shape[1] == 5: return self.overlay_rotated_instances( boxes=boxes, labels=labels, assigned_colors=assigned_colors ) # Display in largest to smallest order to reduce occlusion. areas = None if boxes is not None: areas = np.prod(boxes[:, 2:] - boxes[:, :2], axis=1) elif masks is not None: areas = np.asarray([x.area() for x in masks]) if areas is not None: sorted_idxs = np.argsort(-areas).tolist() # Re-order overlapped instances in descending order. boxes = boxes[sorted_idxs] if boxes is not None else None labels = [labels[k] for k in sorted_idxs] if labels is not None else None masks = [masks[idx] for idx in sorted_idxs] if masks is not None else None assigned_colors = [assigned_colors[idx] for idx in sorted_idxs] keypoints = keypoints[sorted_idxs] if keypoints is not None else None for i in range(num_instances): color = assigned_colors[i] if boxes is not None: self.draw_box(boxes[i], edge_color=color) if masks is not None: for segment in masks[i].polygons: self.draw_polygon(segment.reshape(-1, 2), color, alpha=alpha) if labels is not None: # first get a box if boxes is not None: x0, y0, x1, y1 = boxes[i] text_pos = (x0, y0) # if drawing boxes, put text on the box corner. horiz_align = "left" elif masks is not None: # skip small mask without polygon if len(masks[i].polygons) == 0: continue x0, y0, x1, y1 = masks[i].bbox() # draw text in the center (defined by median) when box is not drawn # median is less sensitive to outliers. text_pos = np.median(masks[i].mask.nonzero(), axis=1)[::-1] horiz_align = "center" else: continue # drawing the box confidence for keypoints isn't very useful. # for small objects, draw text at the side to avoid occlusion instance_area = (y1 - y0) * (x1 - x0) if ( instance_area < _SMALL_OBJECT_AREA_THRESH * self.output.scale or y1 - y0 < 40 * self.output.scale ): if y1 >= self.output.height - 5: text_pos = (x1, y0) else: text_pos = (x0, y1) height_ratio = (y1 - y0) / np.sqrt(self.output.height * self.output.width) lighter_color = self._change_color_brightness(color, brightness_factor=0.7) font_size = ( np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size ) self.draw_text( labels[i], text_pos, color=lighter_color, horizontal_alignment=horiz_align, font_size=font_size, ) # draw keypoints if keypoints is not None: for keypoints_per_instance in keypoints: self.draw_and_connect_keypoints(keypoints_per_instance) return self.output
[docs] def overlay_rotated_instances(self, boxes=None, labels=None, assigned_colors=None): """ Args: boxes (ndarray): an Nx5 numpy array of (x_center, y_center, width, height, angle_degrees) format for the N objects in a single image. labels (list[str]): the text to be displayed for each instance. assigned_colors (list[matplotlib.colors]): a list of colors, where each color corresponds to each mask or box in the image. Refer to 'matplotlib.colors' for full list of formats that the colors are accepted in. Returns: output (VisImage): image object with visualizations. """ num_instances = len(boxes) if assigned_colors is None: assigned_colors = [random_color(rgb=True, maximum=1) for _ in range(num_instances)] if num_instances == 0: return self.output # Display in largest to smallest order to reduce occlusion. if boxes is not None: areas = boxes[:, 2] * boxes[:, 3] sorted_idxs = np.argsort(-areas).tolist() # Re-order overlapped instances in descending order. boxes = boxes[sorted_idxs] labels = [labels[k] for k in sorted_idxs] if labels is not None else None colors = [assigned_colors[idx] for idx in sorted_idxs] for i in range(num_instances): self.draw_rotated_box_with_label( boxes[i], edge_color=colors[i], label=labels[i] if labels is not None else None ) return self.output
[docs] def draw_and_connect_keypoints(self, keypoints): """ Draws keypoints of an instance and follows the rules for keypoint connections to draw lines between appropriate keypoints. This follows color heuristics for line color. Args: keypoints (Tensor): a tensor of shape (K, 3), where K is the number of keypoints and the last dimension corresponds to (x, y, probability). Returns: output (VisImage): image object with visualizations. """ visible = {} keypoint_names = self.metadata.get("keypoint_names") for idx, keypoint in enumerate(keypoints): # draw keypoint x, y, prob = keypoint if prob > self.keypoint_threshold: self.draw_circle((x, y), color=_RED) if keypoint_names: keypoint_name = keypoint_names[idx] visible[keypoint_name] = (x, y) if self.metadata.get("keypoint_connection_rules"): for kp0, kp1, color in self.metadata.keypoint_connection_rules: if kp0 in visible and kp1 in visible: x0, y0 = visible[kp0] x1, y1 = visible[kp1] color = tuple(x / 255.0 for x in color) self.draw_line([x0, x1], [y0, y1], color=color) # draw lines from nose to mid-shoulder and mid-shoulder to mid-hip # Note that this strategy is specific to person keypoints. # For other keypoints, it should just do nothing try: ls_x, ls_y = visible["left_shoulder"] rs_x, rs_y = visible["right_shoulder"] mid_shoulder_x, mid_shoulder_y = (ls_x + rs_x) / 2, (ls_y + rs_y) / 2 except KeyError: pass else: # draw line from nose to mid-shoulder nose_x, nose_y = visible.get("nose", (None, None)) if nose_x is not None: self.draw_line([nose_x, mid_shoulder_x], [nose_y, mid_shoulder_y], color=_RED) try: # draw line from mid-shoulder to mid-hip lh_x, lh_y = visible["left_hip"] rh_x, rh_y = visible["right_hip"] except KeyError: pass else: mid_hip_x, mid_hip_y = (lh_x + rh_x) / 2, (lh_y + rh_y) / 2 self.draw_line([mid_hip_x, mid_shoulder_x], [mid_hip_y, mid_shoulder_y], color=_RED) return self.output
""" Primitive drawing functions: """
[docs] def draw_text( self, text, position, *, font_size=None, color="g", horizontal_alignment="center", rotation=0, ): """ Args: text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image. font_size (int, optional): font of the text. If not provided, a font size proportional to the image width is calculated and used. color: color of the text. Refer to `matplotlib.colors` for full list of formats that are accepted. horizontal_alignment (str): see `matplotlib.text.Text` rotation: rotation angle in degrees CCW Returns: output (VisImage): image object with text drawn. """ if not font_size: font_size = self._default_font_size # since the text background is dark, we don't want the text to be dark color = np.maximum(list(mplc.to_rgb(color)), 0.2) color[np.argmax(color)] = max(0.8, np.max(color)) x, y = position self.output.ax.text( x, y, text, size=font_size * self.output.scale, family="sans-serif", bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"}, verticalalignment="top", horizontalalignment=horizontal_alignment, color=color, zorder=10, rotation=rotation, ) return self.output
[docs] def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"): """ Args: box_coord (tuple): a tuple containing x0, y0, x1, y1 coordinates, where x0 and y0 are the coordinates of the image's top left corner. x1 and y1 are the coordinates of the image's bottom right corner. alpha (float): blending efficient. Smaller values lead to more transparent masks. edge_color: color of the outline of the box. Refer to `matplotlib.colors` for full list of formats that are accepted. line_style (string): the string to use to create the outline of the boxes. Returns: output (VisImage): image object with box drawn. """ x0, y0, x1, y1 = box_coord width = x1 - x0 height = y1 - y0 linewidth = max(self._default_font_size / 4, 1) self.output.ax.add_patch( mpl.patches.Rectangle( (x0, y0), width, height, fill=False, edgecolor=edge_color, linewidth=linewidth * self.output.scale, alpha=alpha, linestyle=line_style, ) ) return self.output
[docs] def draw_rotated_box_with_label( self, rotated_box, alpha=0.5, edge_color="g", line_style="-", label=None ): """ Draw a rotated box with label on its top-left corner. Args: rotated_box (tuple): a tuple containing (cnt_x, cnt_y, w, h, angle), where cnt_x and cnt_y are the center coordinates of the box. w and h are the width and height of the box. angle represents how many degrees the box is rotated CCW with regard to the 0-degree box. alpha (float): blending efficient. Smaller values lead to more transparent masks. edge_color: color of the outline of the box. Refer to `matplotlib.colors` for full list of formats that are accepted. line_style (string): the string to use to create the outline of the boxes. label (string): label for rotated box. It will not be rendered when set to None. Returns: output (VisImage): image object with box drawn. """ cnt_x, cnt_y, w, h, angle = rotated_box area = w * h # use thinner lines when the box is small linewidth = self._default_font_size / ( 6 if area < _SMALL_OBJECT_AREA_THRESH * self.output.scale else 3 ) theta = angle * math.pi / 180.0 c = math.cos(theta) s = math.sin(theta) rect = [(-w / 2, h / 2), (-w / 2, -h / 2), (w / 2, -h / 2), (w / 2, h / 2)] # x: left->right ; y: top->down rotated_rect = [(s * yy + c * xx + cnt_x, c * yy - s * xx + cnt_y) for (xx, yy) in rect] for k in range(4): j = (k + 1) % 4 self.draw_line( [rotated_rect[k][0], rotated_rect[j][0]], [rotated_rect[k][1], rotated_rect[j][1]], color=edge_color, linestyle="--" if k == 1 else line_style, linewidth=linewidth, ) if label is not None: text_pos = rotated_rect[1] # topleft corner height_ratio = h / np.sqrt(self.output.height * self.output.width) label_color = self._change_color_brightness(edge_color, brightness_factor=0.7) font_size = ( np.clip((height_ratio - 0.02) / 0.08 + 1, 1.2, 2) * 0.5 * self._default_font_size ) self.draw_text(label, text_pos, color=label_color, font_size=font_size, rotation=angle) return self.output
[docs] def draw_circle(self, circle_coord, color, radius=3): """ Args: circle_coord (list(int) or tuple(int)): contains the x and y coordinates of the center of the circle. color: color of the polygon. Refer to `matplotlib.colors` for a full list of formats that are accepted. radius (int): radius of the circle. Returns: output (VisImage): image object with box drawn. """ x, y = circle_coord self.output.ax.add_patch( mpl.patches.Circle(circle_coord, radius=radius, fill=True, color=color) ) return self.output
[docs] def draw_line(self, x_data, y_data, color, linestyle="-", linewidth=None): """ Args: x_data (list[int]): a list containing x values of all the points being drawn. Length of list should match the length of y_data. y_data (list[int]): a list containing y values of all the points being drawn. Length of list should match the length of x_data. color: color of the line. Refer to `matplotlib.colors` for a full list of formats that are accepted. linestyle: style of the line. Refer to `matplotlib.lines.Line2D` for a full list of formats that are accepted. linewidth (float or None): width of the line. When it's None, a default value will be computed and used. Returns: output (VisImage): image object with line drawn. """ if linewidth is None: linewidth = self._default_font_size / 3 linewidth = max(linewidth, 1) self.output.ax.add_line( mpl.lines.Line2D( x_data, y_data, linewidth=linewidth * self.output.scale, color=color, linestyle=linestyle, ) ) return self.output
[docs] def draw_binary_mask( self, binary_mask, color=None, *, edge_color=None, text=None, alpha=0.5, area_threshold=10 ): """ Args: binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and W is the image width. Each value in the array is either a 0 or 1 value of uint8 type. color: color of the mask. Refer to `matplotlib.colors` for a full list of formats that are accepted. If None, will pick a random color. edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a full list of formats that are accepted. text (str): if None, will be drawn on the object alpha (float): blending efficient. Smaller values lead to more transparent masks. area_threshold (float): a connected component smaller than this area will not be shown. Returns: output (VisImage): image object with mask drawn. """ if color is None: color = random_color(rgb=True, maximum=1) color = mplc.to_rgb(color) has_valid_segment = False binary_mask = binary_mask.astype("uint8") # opencv needs uint8 mask = GenericMask(binary_mask, self.output.height, self.output.width) shape2d = (binary_mask.shape[0], binary_mask.shape[1]) if not mask.has_holes: # draw polygons for regular masks for segment in mask.polygons: area = mask_util.area(mask_util.frPyObjects([segment], shape2d[0], shape2d[1])) if area < (area_threshold or 0): continue has_valid_segment = True segment = segment.reshape(-1, 2) self.draw_polygon(segment, color=color, edge_color=edge_color, alpha=alpha) else: # TODO: Use Path/PathPatch to draw vector graphics: # https://stackoverflow.com/questions/8919719/how-to-plot-a-complex-polygon rgba = np.zeros(shape2d + (4,), dtype="float32") rgba[:, :, :3] = color rgba[:, :, 3] = (mask.mask == 1).astype("float32") * alpha has_valid_segment = True self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) if text is not None and has_valid_segment: lighter_color = self._change_color_brightness(color, brightness_factor=0.7) self._draw_text_in_mask(binary_mask, text, lighter_color) return self.output
[docs] def draw_soft_mask(self, soft_mask, color=None, *, text=None, alpha=0.5): """ Args: soft_mask (ndarray): float array of shape (H, W), each value in [0, 1]. color: color of the mask. Refer to `matplotlib.colors` for a full list of formats that are accepted. If None, will pick a random color. text (str): if None, will be drawn on the object alpha (float): blending efficient. Smaller values lead to more transparent masks. Returns: output (VisImage): image object with mask drawn. """ if color is None: color = random_color(rgb=True, maximum=1) color = mplc.to_rgb(color) shape2d = (soft_mask.shape[0], soft_mask.shape[1]) rgba = np.zeros(shape2d + (4,), dtype="float32") rgba[:, :, :3] = color rgba[:, :, 3] = soft_mask * alpha self.output.ax.imshow(rgba, extent=(0, self.output.width, self.output.height, 0)) if text is not None: lighter_color = self._change_color_brightness(color, brightness_factor=0.7) binary_mask = (soft_mask > 0.5).astype("uint8") self._draw_text_in_mask(binary_mask, text, lighter_color) return self.output
[docs] def draw_polygon(self, segment, color, edge_color=None, alpha=0.5): """ Args: segment: numpy array of shape Nx2, containing all the points in the polygon. color: color of the polygon. Refer to `matplotlib.colors` for a full list of formats that are accepted. edge_color: color of the polygon edges. Refer to `matplotlib.colors` for a full list of formats that are accepted. If not provided, a darker shade of the polygon color will be used instead. alpha (float): blending efficient. Smaller values lead to more transparent masks. Returns: output (VisImage): image object with polygon drawn. """ if edge_color is None: # make edge color darker than the polygon color if alpha > 0.8: edge_color = self._change_color_brightness(color, brightness_factor=-0.7) else: edge_color = color edge_color = mplc.to_rgb(edge_color) + (1,) polygon = mpl.patches.Polygon( segment, fill=True, facecolor=mplc.to_rgb(color) + (alpha,), edgecolor=edge_color, linewidth=max(self._default_font_size // 15 * self.output.scale, 1), ) self.output.ax.add_patch(polygon) return self.output
""" Internal methods: """ def _jitter(self, color): """ Randomly modifies given color to produce a slightly different color than the color given. Args: color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color picked. The values in the list are in the [0.0, 1.0] range. Returns: jittered_color (tuple[double]): a tuple of 3 elements, containing the RGB values of the color after being jittered. The values in the list are in the [0.0, 1.0] range. """ color = mplc.to_rgb(color) vec = np.random.rand(3) # better to do it in another color space vec = vec / np.linalg.norm(vec) * 0.5 res = np.clip(vec + color, 0, 1) return tuple(res) def _create_grayscale_image(self, mask=None): """ Create a grayscale version of the original image. The colors in masked area, if given, will be kept. """ img_bw = self.img.astype("f4").mean(axis=2) img_bw = np.stack([img_bw] * 3, axis=2) if mask is not None: img_bw[mask] = self.img[mask] return img_bw def _change_color_brightness(self, color, brightness_factor): """ Depending on the brightness_factor, gives a lighter or darker color i.e. a color with less or more saturation than the original color. Args: color: color of the polygon. Refer to `matplotlib.colors` for a full list of formats that are accepted. brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of 0 will correspond to no change, a factor in [-1.0, 0) range will result in a darker color and a factor in (0, 1.0] range will result in a lighter color. Returns: modified_color (tuple[double]): a tuple containing the RGB values of the modified color. Each value in the tuple is in the [0.0, 1.0] range. """ assert brightness_factor >= -1.0 and brightness_factor <= 1.0 color = mplc.to_rgb(color) polygon_color = colorsys.rgb_to_hls(*mplc.to_rgb(color)) modified_lightness = polygon_color[1] + (brightness_factor * polygon_color[1]) modified_lightness = 0.0 if modified_lightness < 0.0 else modified_lightness modified_lightness = 1.0 if modified_lightness > 1.0 else modified_lightness modified_color = colorsys.hls_to_rgb(polygon_color[0], modified_lightness, polygon_color[2]) return tuple(np.clip(modified_color, 0.0, 1.0)) def _convert_boxes(self, boxes): """ Convert different format of boxes to an NxB array, where B = 4 or 5 is the box dimension. """ if isinstance(boxes, Boxes) or isinstance(boxes, RotatedBoxes): return boxes.tensor.detach().numpy() else: return np.asarray(boxes) def _convert_masks(self, masks_or_polygons): """ Convert different format of masks or polygons to a tuple of masks and polygons. Returns: list[GenericMask]: """ m = masks_or_polygons if isinstance(m, PolygonMasks): m = m.polygons if isinstance(m, BitMasks): m = m.tensor.numpy() if isinstance(m, torch.Tensor): m = m.numpy() ret = [] for x in m: if isinstance(x, GenericMask): ret.append(x) else: ret.append(GenericMask(x, self.output.height, self.output.width)) return ret def _draw_text_in_mask(self, binary_mask, text, color): """ Find proper places to draw text given a binary mask. """ # TODO sometimes drawn on wrong objects. the heuristics here can improve. _num_cc, cc_labels, stats, centroids = cv2.connectedComponentsWithStats(binary_mask, 8) if stats[1:, -1].size == 0: return largest_component_id = np.argmax(stats[1:, -1]) + 1 # draw text on the largest component, as well as other very large components. for cid in range(1, _num_cc): if cid == largest_component_id or stats[cid, -1] > _LARGE_MASK_AREA_THRESH: # median is more stable than centroid # center = centroids[largest_component_id] center = np.median((cc_labels == cid).nonzero(), axis=1)[::-1] self.draw_text(text, center, color=color) def _convert_keypoints(self, keypoints): if isinstance(keypoints, Keypoints): keypoints = keypoints.tensor keypoints = np.asarray(keypoints) return keypoints
[docs] def get_output(self): """ Returns: output (VisImage): the image output containing the visualizations added to the image. """ return self.output