# coding: utf-8 # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import division import cv2 import numpy as np from PIL import Image, ImageDraw from scipy import ndimage def visualize_box_mask(im, results, labels, threshold=0.5): """ Args: im (str/np.ndarray): path of image/np.ndarray read by cv2 results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box, matix element:[class, score, x_min, y_min, x_max, y_max] MaskRCNN's results include 'masks': np.ndarray: shape:[N, im_h, im_w] labels (list): labels:['class1', ..., 'classn'] threshold (float): Threshold of score. Returns: im (PIL.Image.Image): visualized image """ if isinstance(im, str): im = Image.open(im).convert('RGB') else: im = Image.fromarray(im) if 'masks' in results and 'boxes' in results: im = draw_mask( im, results['boxes'], results['masks'], labels, threshold=threshold) if 'boxes' in results: im = draw_box(im, results['boxes'], labels, threshold=threshold) if 'segm' in results: im = draw_segm( im, results['segm'], results['label'], results['score'], labels, threshold=threshold) return im def get_color_map_list(num_classes): """ Args: num_classes (int): number of class Returns: color_map (list): RGB color list """ color_map = num_classes * [0, 0, 0] for i in range(0, num_classes): j = 0 lab = i while lab: color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j)) color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j)) color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j)) j += 1 lab >>= 3 color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)] return color_map def draw_mask(im, np_boxes, np_masks, labels, threshold=0.5): """ Args: im (PIL.Image.Image): PIL image np_boxes (np.ndarray): shape:[N,6], N: number of box, matix element:[class, score, x_min, y_min, x_max, y_max] np_masks (np.ndarray): shape:[N, im_h, im_w] labels (list): labels:['class1', ..., 'classn'] threshold (float): threshold of mask Returns: im (PIL.Image.Image): visualized image """ color_list = get_color_map_list(len(labels)) w_ratio = 0.4 alpha = 0.7 im = np.array(im).astype('float32') clsid2color = {} expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1) np_boxes = np_boxes[expect_boxes, :] np_masks = np_masks[expect_boxes, :, :] for i in range(len(np_masks)): clsid, score = int(np_boxes[i][0]), np_boxes[i][1] mask = np_masks[i] if clsid not in clsid2color: clsid2color[clsid] = color_list[clsid] color_mask = clsid2color[clsid] for c in range(3): color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255 idx = np.nonzero(mask) color_mask = np.array(color_mask) im[idx[0], idx[1], :] *= 1.0 - alpha im[idx[0], idx[1], :] += alpha * color_mask return Image.fromarray(im.astype('uint8')) def draw_box(im, np_boxes, labels, threshold=0.5): """ Args: im (PIL.Image.Image): PIL image np_boxes (np.ndarray): shape:[N,6], N: number of box, matix element:[class, score, x_min, y_min, x_max, y_max] labels (list): labels:['class1', ..., 'classn'] threshold (float): threshold of box Returns: im (PIL.Image.Image): visualized image """ draw_thickness = min(im.size) // 320 draw = ImageDraw.Draw(im) clsid2color = {} color_list = get_color_map_list(len(labels)) expect_boxes = (np_boxes[:, 1] > threshold) & (np_boxes[:, 0] > -1) np_boxes = np_boxes[expect_boxes, :] for dt in np_boxes: clsid, bbox, score = int(dt[0]), dt[2:], dt[1] xmin, ymin, xmax, ymax = bbox print('class_id:{:d}, confidence:{:.4f}, left_top:[{:.2f},{:.2f}],' 'right_bottom:[{:.2f},{:.2f}]'.format( int(clsid), score, xmin, ymin, xmax, ymax)) w = xmax - xmin h = ymax - ymin if clsid not in clsid2color: clsid2color[clsid] = color_list[clsid] color = tuple(clsid2color[clsid]) # draw bbox draw.line( [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)], width=draw_thickness, fill=color) # draw label text = "{} {:.4f}".format(labels[clsid], score) tw, th = draw.textsize(text) draw.rectangle( [(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color) draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255)) return im def draw_segm(im, np_segms, np_label, np_score, labels, threshold=0.5, alpha=0.7): """ Draw segmentation on image """ mask_color_id = 0 w_ratio = .4 color_list = get_color_map_list(len(labels)) im = np.array(im).astype('float32') clsid2color = {} np_segms = np_segms.astype(np.uint8) for i in range(np_segms.shape[0]): mask, score, clsid = np_segms[i], np_score[i], np_label[i] if score < threshold: continue if clsid not in clsid2color: clsid2color[clsid] = color_list[clsid] color_mask = clsid2color[clsid] for c in range(3): color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255 idx = np.nonzero(mask) color_mask = np.array(color_mask) im[idx[0], idx[1], :] *= 1.0 - alpha im[idx[0], idx[1], :] += alpha * color_mask sum_x = np.sum(mask, axis=0) x = np.where(sum_x > 0.5)[0] sum_y = np.sum(mask, axis=1) y = np.where(sum_y > 0.5)[0] x0, x1, y0, y1 = x[0], x[-1], y[0], y[-1] cv2.rectangle(im, (x0, y0), (x1, y1), tuple(color_mask.astype('int32').tolist()), 1) bbox_text = '%s %.2f' % (labels[clsid], score) t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0] cv2.rectangle(im, (x0, y0), (x0 + t_size[0], y0 - t_size[1] - 3), tuple(color_mask.astype('int32').tolist()), -1) cv2.putText( im, bbox_text, (x0, y0 - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), 1, lineType=cv2.LINE_AA) return Image.fromarray(im.astype('uint8'))