# Copyright (c) Facebook, Inc. and its affiliates. import logging import numpy as np import cv2 import torch Image = np.ndarray Boxes = torch.Tensor class MatrixVisualizer: """ Base visualizer for matrix data """ def __init__( self, inplace=True, cmap=cv2.COLORMAP_PARULA, val_scale=1.0, alpha=0.7, interp_method_matrix=cv2.INTER_LINEAR, interp_method_mask=cv2.INTER_NEAREST, ): self.inplace = inplace self.cmap = cmap self.val_scale = val_scale self.alpha = alpha self.interp_method_matrix = interp_method_matrix self.interp_method_mask = interp_method_mask def visualize(self, image_bgr, mask, matrix, bbox_xywh): self._check_image(image_bgr) self._check_mask_matrix(mask, matrix) if self.inplace: image_target_bgr = image_bgr else: image_target_bgr = image_bgr image_target_bgr *= 0 x, y, w, h = [int(v) for v in bbox_xywh] if w <= 0 or h <= 0: return image_bgr mask, matrix = self._resize(mask, matrix, w, h) mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3]) matrix_scaled = matrix.astype(np.float32) * self.val_scale _EPSILON = 1e-6 if np.any(matrix_scaled > 255 + _EPSILON): logger = logging.getLogger(__name__) logger.warning( f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]" ) matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8) matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap) matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg] image_target_bgr[y : y + h, x : x + w, :] = ( image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha ) return image_target_bgr.astype(np.uint8) def _resize(self, mask, matrix, w, h): if (w != mask.shape[1]) or (h != mask.shape[0]): mask = cv2.resize(mask, (w, h), self.interp_method_mask) if (w != matrix.shape[1]) or (h != matrix.shape[0]): matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix) return mask, matrix def _check_image(self, image_rgb): assert len(image_rgb.shape) == 3 assert image_rgb.shape[2] == 3 assert image_rgb.dtype == np.uint8 def _check_mask_matrix(self, mask, matrix): assert len(matrix.shape) == 2 assert len(mask.shape) == 2 assert mask.dtype == np.uint8 class RectangleVisualizer: _COLOR_GREEN = (18, 127, 15) def __init__(self, color=_COLOR_GREEN, thickness=1): self.color = color self.thickness = thickness def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None): x, y, w, h = bbox_xywh color = color or self.color thickness = thickness or self.thickness cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness) return image_bgr class PointsVisualizer: _COLOR_GREEN = (18, 127, 15) def __init__(self, color_bgr=_COLOR_GREEN, r=5): self.color_bgr = color_bgr self.r = r def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None): for j, pt_xy in enumerate(pts_xy): x, y = pt_xy color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr r = rs[j] if rs is not None else self.r cv2.circle(image_bgr, (x, y), r, color_bgr, -1) return image_bgr class TextVisualizer: _COLOR_GRAY = (218, 227, 218) _COLOR_WHITE = (255, 255, 255) def __init__( self, font_face=cv2.FONT_HERSHEY_SIMPLEX, font_color_bgr=_COLOR_GRAY, font_scale=0.35, font_line_type=cv2.LINE_AA, font_line_thickness=1, fill_color_bgr=_COLOR_WHITE, fill_color_transparency=1.0, frame_color_bgr=_COLOR_WHITE, frame_color_transparency=1.0, frame_thickness=1, ): self.font_face = font_face self.font_color_bgr = font_color_bgr self.font_scale = font_scale self.font_line_type = font_line_type self.font_line_thickness = font_line_thickness self.fill_color_bgr = fill_color_bgr self.fill_color_transparency = fill_color_transparency self.frame_color_bgr = frame_color_bgr self.frame_color_transparency = frame_color_transparency self.frame_thickness = frame_thickness def visualize(self, image_bgr, txt, topleft_xy): txt_w, txt_h = self.get_text_size_wh(txt) topleft_xy = tuple(map(int, topleft_xy)) x, y = topleft_xy if self.frame_color_transparency < 1.0: t = self.frame_thickness image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = ( image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] * self.frame_color_transparency + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency) ).astype(float) if self.fill_color_transparency < 1.0: image_bgr[y : y + txt_h, x : x + txt_w, :] = ( image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency) ).astype(float) cv2.putText( image_bgr, txt, topleft_xy, self.font_face, self.font_scale, self.font_color_bgr, self.font_line_thickness, self.font_line_type, ) return image_bgr def get_text_size_wh(self, txt): ((txt_w, txt_h), _) = cv2.getTextSize( txt, self.font_face, self.font_scale, self.font_line_thickness ) return txt_w, txt_h class CompoundVisualizer: def __init__(self, visualizers): self.visualizers = visualizers def visualize(self, image_bgr, data): assert len(data) == len( self.visualizers ), "The number of datas {} should match the number of visualizers" " {}".format( len(data), len(self.visualizers) ) image = image_bgr for i, visualizer in enumerate(self.visualizers): image = visualizer.visualize(image, data[i]) return image def __str__(self): visualizer_str = ", ".join([str(v) for v in self.visualizers]) return "Compound Visualizer [{}]".format(visualizer_str)