forked from PulseFocusPlatform/PulseFocusPlatform
292 lines
10 KiB
Python
292 lines
10 KiB
Python
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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from PIL import Image, ImageDraw
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import cv2
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import math
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from .colormap import colormap
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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__all__ = ['visualize_results']
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def visualize_results(image,
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bbox_res,
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mask_res,
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segm_res,
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keypoint_res,
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im_id,
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catid2name,
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threshold=0.5):
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"""
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Visualize bbox and mask results
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"""
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if bbox_res is not None:
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image = draw_bbox(image, im_id, catid2name, bbox_res, threshold)
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if mask_res is not None:
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image = draw_mask(image, im_id, mask_res, threshold)
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if segm_res is not None:
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image = draw_segm(image, im_id, catid2name, segm_res, threshold)
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if keypoint_res is not None:
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image = draw_pose(image, keypoint_res, threshold)
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return image
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def draw_mask(image, im_id, segms, threshold, alpha=0.7):
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"""
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Draw mask on image
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"""
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mask_color_id = 0
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w_ratio = .4
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color_list = colormap(rgb=True)
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img_array = np.array(image).astype('float32')
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for dt in np.array(segms):
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if im_id != dt['image_id']:
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continue
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segm, score = dt['segmentation'], dt['score']
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if score < threshold:
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continue
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import pycocotools.mask as mask_util
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mask = mask_util.decode(segm) * 255
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color_mask = color_list[mask_color_id % len(color_list), 0:3]
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mask_color_id += 1
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for c in range(3):
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color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
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idx = np.nonzero(mask)
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img_array[idx[0], idx[1], :] *= 1.0 - alpha
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img_array[idx[0], idx[1], :] += alpha * color_mask
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return Image.fromarray(img_array.astype('uint8'))
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def draw_bbox(image, im_id, catid2name, bboxes, threshold):
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"""
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Draw bbox on image
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"""
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draw = ImageDraw.Draw(image)
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catid2color = {}
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color_list = colormap(rgb=True)[:40]
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for dt in np.array(bboxes):
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if im_id != dt['image_id']:
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continue
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catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
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if score < threshold:
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continue
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if catid not in catid2color:
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idx = np.random.randint(len(color_list))
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catid2color[catid] = color_list[idx]
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color = tuple(catid2color[catid])
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# draw bbox
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if len(bbox) == 4:
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# draw bbox
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xmin, ymin, w, h = bbox
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xmax = xmin + w
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ymax = ymin + h
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draw.line(
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[(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin),
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(xmin, ymin)],
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width=2,
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fill=color)
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elif len(bbox) == 8:
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x1, y1, x2, y2, x3, y3, x4, y4 = bbox
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draw.line(
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[(x1, y1), (x2, y2), (x3, y3), (x4, y4), (x1, y1)],
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width=2,
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fill=color)
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xmin = min(x1, x2, x3, x4)
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ymin = min(y1, y2, y3, y4)
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else:
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logger.error('the shape of bbox must be [M, 4] or [M, 8]!')
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# draw label
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text = "{} {:.2f}".format(catid2name[catid], score)
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tw, th = draw.textsize(text)
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draw.rectangle(
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[(xmin + 1, ymin - th), (xmin + tw + 1, ymin)], fill=color)
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draw.text((xmin + 1, ymin - th), text, fill=(255, 255, 255))
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return image
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def save_result(save_path, results, catid2name, threshold):
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"""
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save result as txt
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"""
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img_id = int(results["im_id"])
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with open(save_path, 'w') as f:
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if "bbox_res" in results:
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for dt in results["bbox_res"]:
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catid, bbox, score = dt['category_id'], dt['bbox'], dt['score']
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if score < threshold:
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continue
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# each bbox result as a line
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# for rbox: classname score x1 y1 x2 y2 x3 y3 x4 y4
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# for bbox: classname score x1 y1 w h
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bbox_pred = '{} {} '.format(catid2name[catid],
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score) + ' '.join(
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[str(e) for e in bbox])
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f.write(bbox_pred + '\n')
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elif "keypoint_res" in results:
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for dt in results["keypoint_res"]:
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kpts = dt['keypoints']
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scores = dt['score']
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keypoint_pred = [img_id, scores, kpts]
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print(keypoint_pred, file=f)
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else:
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print("No valid results found, skip txt save")
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def draw_segm(image,
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im_id,
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catid2name,
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segms,
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threshold,
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alpha=0.7,
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draw_box=True):
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"""
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Draw segmentation on image
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"""
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mask_color_id = 0
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w_ratio = .4
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color_list = colormap(rgb=True)
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img_array = np.array(image).astype('float32')
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for dt in np.array(segms):
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if im_id != dt['image_id']:
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continue
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segm, score, catid = dt['segmentation'], dt['score'], dt['category_id']
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if score < threshold:
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continue
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import pycocotools.mask as mask_util
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mask = mask_util.decode(segm) * 255
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color_mask = color_list[mask_color_id % len(color_list), 0:3]
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mask_color_id += 1
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for c in range(3):
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color_mask[c] = color_mask[c] * (1 - w_ratio) + w_ratio * 255
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idx = np.nonzero(mask)
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img_array[idx[0], idx[1], :] *= 1.0 - alpha
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img_array[idx[0], idx[1], :] += alpha * color_mask
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if not draw_box:
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center_y, center_x = ndimage.measurements.center_of_mass(mask)
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label_text = "{}".format(catid2name[catid])
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vis_pos = (max(int(center_x) - 10, 0), int(center_y))
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cv2.putText(img_array, label_text, vis_pos,
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cv2.FONT_HERSHEY_COMPLEX, 0.3, (255, 255, 255))
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else:
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mask = mask_util.decode(segm) * 255
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sum_x = np.sum(mask, axis=0)
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x = np.where(sum_x > 0.5)[0]
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sum_y = np.sum(mask, axis=1)
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y = np.where(sum_y > 0.5)[0]
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x0, x1, y0, y1 = x[0], x[-1], y[0], y[-1]
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cv2.rectangle(img_array, (x0, y0), (x1, y1),
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tuple(color_mask.astype('int32').tolist()), 1)
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bbox_text = '%s %.2f' % (catid2name[catid], score)
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t_size = cv2.getTextSize(bbox_text, 0, 0.3, thickness=1)[0]
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cv2.rectangle(img_array, (x0, y0), (x0 + t_size[0],
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y0 - t_size[1] - 3),
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tuple(color_mask.astype('int32').tolist()), -1)
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cv2.putText(
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img_array,
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bbox_text, (x0, y0 - 2),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.3, (0, 0, 0),
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1,
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lineType=cv2.LINE_AA)
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return Image.fromarray(img_array.astype('uint8'))
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def map_coco_to_personlab(keypoints):
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permute = [0, 6, 8, 10, 5, 7, 9, 12, 14, 16, 11, 13, 15, 2, 1, 4, 3]
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return keypoints[:, permute, :]
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def draw_pose(image, results, visual_thread=0.6, save_name='pose.jpg'):
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try:
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import matplotlib.pyplot as plt
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import matplotlib
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plt.switch_backend('agg')
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except Exception as e:
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logger.error('Matplotlib not found, plaese install matplotlib.'
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'for example: `pip install matplotlib`.')
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raise e
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EDGES = [(0, 14), (0, 13), (0, 4), (0, 1), (14, 16), (13, 15), (4, 10),
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(1, 7), (10, 11), (7, 8), (11, 12), (8, 9), (4, 5), (1, 2), (5, 6),
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(2, 3)]
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NUM_EDGES = len(EDGES)
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colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
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[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
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[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
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cmap = matplotlib.cm.get_cmap('hsv')
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plt.figure()
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skeletons = np.array([item['keypoints'] for item in results]).reshape(-1,
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17, 3)
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img = np.array(image).astype('float32')
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canvas = img.copy()
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for i in range(17):
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rgba = np.array(cmap(1 - i / 17. - 1. / 34))
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rgba[0:3] *= 255
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for j in range(len(skeletons)):
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if skeletons[j][i, 2] < visual_thread:
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continue
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cv2.circle(
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canvas,
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tuple(skeletons[j][i, 0:2].astype('int32')),
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2,
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colors[i],
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thickness=-1)
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to_plot = cv2.addWeighted(img, 0.3, canvas, 0.7, 0)
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fig = matplotlib.pyplot.gcf()
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stickwidth = 2
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skeletons = map_coco_to_personlab(skeletons)
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for i in range(NUM_EDGES):
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for j in range(len(skeletons)):
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edge = EDGES[i]
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if skeletons[j][edge[0], 2] < visual_thread or skeletons[j][edge[
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1], 2] < visual_thread:
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continue
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cur_canvas = canvas.copy()
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X = [skeletons[j][edge[0], 1], skeletons[j][edge[1], 1]]
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Y = [skeletons[j][edge[0], 0], skeletons[j][edge[1], 0]]
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mX = np.mean(X)
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mY = np.mean(Y)
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length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
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angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
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polygon = cv2.ellipse2Poly((int(mY), int(mX)),
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(int(length / 2), stickwidth),
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int(angle), 0, 360, 1)
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cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
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canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
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image = Image.fromarray(canvas.astype('uint8'))
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plt.close()
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return image
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