203 lines
6.9 KiB
Python
203 lines
6.9 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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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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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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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, bbox_res, catid2name, threshold):
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"""
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save result as txt
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"""
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with open(save_path, 'w') as f:
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for dt in 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], 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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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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