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- # 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.
- import numpy as np
- import random
- import math
- import cv2
- import scipy
- def meet_emit_constraint(src_bbox, sample_bbox):
- center_x = (src_bbox[2] + src_bbox[0]) / 2
- center_y = (src_bbox[3] + src_bbox[1]) / 2
- if center_x >= sample_bbox[0] and \
- center_x <= sample_bbox[2] and \
- center_y >= sample_bbox[1] and \
- center_y <= sample_bbox[3]:
- return True
- return False
- def clip_bbox(src_bbox):
- src_bbox[0] = max(min(src_bbox[0], 1.0), 0.0)
- src_bbox[1] = max(min(src_bbox[1], 1.0), 0.0)
- src_bbox[2] = max(min(src_bbox[2], 1.0), 0.0)
- src_bbox[3] = max(min(src_bbox[3], 1.0), 0.0)
- return src_bbox
- def bbox_area(src_bbox):
- if src_bbox[2] < src_bbox[0] or src_bbox[3] < src_bbox[1]:
- return 0.
- else:
- width = src_bbox[2] - src_bbox[0]
- height = src_bbox[3] - src_bbox[1]
- return width * height
- def is_overlap(object_bbox, sample_bbox):
- if object_bbox[0] >= sample_bbox[2] or \
- object_bbox[2] <= sample_bbox[0] or \
- object_bbox[1] >= sample_bbox[3] or \
- object_bbox[3] <= sample_bbox[1]:
- return False
- else:
- return True
- def filter_and_process(sample_bbox, bboxes, labels, scores=None):
- new_bboxes = []
- new_labels = []
- new_scores = []
- for i in range(len(bboxes)):
- new_bbox = [0, 0, 0, 0]
- obj_bbox = [bboxes[i][0], bboxes[i][1], bboxes[i][2], bboxes[i][3]]
- if not meet_emit_constraint(obj_bbox, sample_bbox):
- continue
- if not is_overlap(obj_bbox, sample_bbox):
- continue
- sample_width = sample_bbox[2] - sample_bbox[0]
- sample_height = sample_bbox[3] - sample_bbox[1]
- new_bbox[0] = (obj_bbox[0] - sample_bbox[0]) / sample_width
- new_bbox[1] = (obj_bbox[1] - sample_bbox[1]) / sample_height
- new_bbox[2] = (obj_bbox[2] - sample_bbox[0]) / sample_width
- new_bbox[3] = (obj_bbox[3] - sample_bbox[1]) / sample_height
- new_bbox = clip_bbox(new_bbox)
- if bbox_area(new_bbox) > 0:
- new_bboxes.append(new_bbox)
- new_labels.append([labels[i][0]])
- if scores is not None:
- new_scores.append([scores[i][0]])
- bboxes = np.array(new_bboxes)
- labels = np.array(new_labels)
- scores = np.array(new_scores)
- return bboxes, labels, scores
- def bbox_area_sampling(bboxes, labels, scores, target_size, min_size):
- new_bboxes = []
- new_labels = []
- new_scores = []
- for i, bbox in enumerate(bboxes):
- w = float((bbox[2] - bbox[0]) * target_size)
- h = float((bbox[3] - bbox[1]) * target_size)
- if w * h < float(min_size * min_size):
- continue
- else:
- new_bboxes.append(bbox)
- new_labels.append(labels[i])
- if scores is not None and scores.size != 0:
- new_scores.append(scores[i])
- bboxes = np.array(new_bboxes)
- labels = np.array(new_labels)
- scores = np.array(new_scores)
- return bboxes, labels, scores
- def generate_sample_bbox(sampler):
- scale = np.random.uniform(sampler[2], sampler[3])
- aspect_ratio = np.random.uniform(sampler[4], sampler[5])
- aspect_ratio = max(aspect_ratio, (scale**2.0))
- aspect_ratio = min(aspect_ratio, 1 / (scale**2.0))
- bbox_width = scale * (aspect_ratio**0.5)
- bbox_height = scale / (aspect_ratio**0.5)
- xmin_bound = 1 - bbox_width
- ymin_bound = 1 - bbox_height
- xmin = np.random.uniform(0, xmin_bound)
- ymin = np.random.uniform(0, ymin_bound)
- xmax = xmin + bbox_width
- ymax = ymin + bbox_height
- sampled_bbox = [xmin, ymin, xmax, ymax]
- return sampled_bbox
- def generate_sample_bbox_square(sampler, image_width, image_height):
- scale = np.random.uniform(sampler[2], sampler[3])
- aspect_ratio = np.random.uniform(sampler[4], sampler[5])
- aspect_ratio = max(aspect_ratio, (scale**2.0))
- aspect_ratio = min(aspect_ratio, 1 / (scale**2.0))
- bbox_width = scale * (aspect_ratio**0.5)
- bbox_height = scale / (aspect_ratio**0.5)
- if image_height < image_width:
- bbox_width = bbox_height * image_height / image_width
- else:
- bbox_height = bbox_width * image_width / image_height
- xmin_bound = 1 - bbox_width
- ymin_bound = 1 - bbox_height
- xmin = np.random.uniform(0, xmin_bound)
- ymin = np.random.uniform(0, ymin_bound)
- xmax = xmin + bbox_width
- ymax = ymin + bbox_height
- sampled_bbox = [xmin, ymin, xmax, ymax]
- return sampled_bbox
- def data_anchor_sampling(bbox_labels, image_width, image_height, scale_array,
- resize_width):
- num_gt = len(bbox_labels)
- # np.random.randint range: [low, high)
- rand_idx = np.random.randint(0, num_gt) if num_gt != 0 else 0
- if num_gt != 0:
- norm_xmin = bbox_labels[rand_idx][0]
- norm_ymin = bbox_labels[rand_idx][1]
- norm_xmax = bbox_labels[rand_idx][2]
- norm_ymax = bbox_labels[rand_idx][3]
- xmin = norm_xmin * image_width
- ymin = norm_ymin * image_height
- wid = image_width * (norm_xmax - norm_xmin)
- hei = image_height * (norm_ymax - norm_ymin)
- range_size = 0
- area = wid * hei
- for scale_ind in range(0, len(scale_array) - 1):
- if area > scale_array[scale_ind] ** 2 and area < \
- scale_array[scale_ind + 1] ** 2:
- range_size = scale_ind + 1
- break
- if area > scale_array[len(scale_array) - 2]**2:
- range_size = len(scale_array) - 2
- scale_choose = 0.0
- if range_size == 0:
- rand_idx_size = 0
- else:
- # np.random.randint range: [low, high)
- rng_rand_size = np.random.randint(0, range_size + 1)
- rand_idx_size = rng_rand_size % (range_size + 1)
- if rand_idx_size == range_size:
- min_resize_val = scale_array[rand_idx_size] / 2.0
- max_resize_val = min(2.0 * scale_array[rand_idx_size],
- 2 * math.sqrt(wid * hei))
- scale_choose = random.uniform(min_resize_val, max_resize_val)
- else:
- min_resize_val = scale_array[rand_idx_size] / 2.0
- max_resize_val = 2.0 * scale_array[rand_idx_size]
- scale_choose = random.uniform(min_resize_val, max_resize_val)
- sample_bbox_size = wid * resize_width / scale_choose
- w_off_orig = 0.0
- h_off_orig = 0.0
- if sample_bbox_size < max(image_height, image_width):
- if wid <= sample_bbox_size:
- w_off_orig = np.random.uniform(xmin + wid - sample_bbox_size,
- xmin)
- else:
- w_off_orig = np.random.uniform(xmin,
- xmin + wid - sample_bbox_size)
- if hei <= sample_bbox_size:
- h_off_orig = np.random.uniform(ymin + hei - sample_bbox_size,
- ymin)
- else:
- h_off_orig = np.random.uniform(ymin,
- ymin + hei - sample_bbox_size)
- else:
- w_off_orig = np.random.uniform(image_width - sample_bbox_size, 0.0)
- h_off_orig = np.random.uniform(image_height - sample_bbox_size,
- 0.0)
- w_off_orig = math.floor(w_off_orig)
- h_off_orig = math.floor(h_off_orig)
- # Figure out top left coordinates.
- w_off = float(w_off_orig / image_width)
- h_off = float(h_off_orig / image_height)
- sampled_bbox = [
- w_off, h_off, w_off + float(sample_bbox_size / image_width),
- h_off + float(sample_bbox_size / image_height)
- ]
- return sampled_bbox
- else:
- return 0
- def jaccard_overlap(sample_bbox, object_bbox):
- if sample_bbox[0] >= object_bbox[2] or \
- sample_bbox[2] <= object_bbox[0] or \
- sample_bbox[1] >= object_bbox[3] or \
- sample_bbox[3] <= object_bbox[1]:
- return 0
- intersect_xmin = max(sample_bbox[0], object_bbox[0])
- intersect_ymin = max(sample_bbox[1], object_bbox[1])
- intersect_xmax = min(sample_bbox[2], object_bbox[2])
- intersect_ymax = min(sample_bbox[3], object_bbox[3])
- intersect_size = (intersect_xmax - intersect_xmin) * (
- intersect_ymax - intersect_ymin)
- sample_bbox_size = bbox_area(sample_bbox)
- object_bbox_size = bbox_area(object_bbox)
- overlap = intersect_size / (
- sample_bbox_size + object_bbox_size - intersect_size)
- return overlap
- def intersect_bbox(bbox1, bbox2):
- if bbox2[0] > bbox1[2] or bbox2[2] < bbox1[0] or \
- bbox2[1] > bbox1[3] or bbox2[3] < bbox1[1]:
- intersection_box = [0.0, 0.0, 0.0, 0.0]
- else:
- intersection_box = [
- max(bbox1[0], bbox2[0]),
- max(bbox1[1], bbox2[1]),
- min(bbox1[2], bbox2[2]),
- min(bbox1[3], bbox2[3])
- ]
- return intersection_box
- def bbox_coverage(bbox1, bbox2):
- inter_box = intersect_bbox(bbox1, bbox2)
- intersect_size = bbox_area(inter_box)
- if intersect_size > 0:
- bbox1_size = bbox_area(bbox1)
- return intersect_size / bbox1_size
- else:
- return 0.
- def satisfy_sample_constraint(sampler,
- sample_bbox,
- gt_bboxes,
- satisfy_all=False):
- if sampler[6] == 0 and sampler[7] == 0:
- return True
- satisfied = []
- for i in range(len(gt_bboxes)):
- object_bbox = [
- gt_bboxes[i][0], gt_bboxes[i][1], gt_bboxes[i][2], gt_bboxes[i][3]
- ]
- overlap = jaccard_overlap(sample_bbox, object_bbox)
- if sampler[6] != 0 and \
- overlap < sampler[6]:
- satisfied.append(False)
- continue
- if sampler[7] != 0 and \
- overlap > sampler[7]:
- satisfied.append(False)
- continue
- satisfied.append(True)
- if not satisfy_all:
- return True
- if satisfy_all:
- return np.all(satisfied)
- else:
- return False
- def satisfy_sample_constraint_coverage(sampler, sample_bbox, gt_bboxes):
- if sampler[6] == 0 and sampler[7] == 0:
- has_jaccard_overlap = False
- else:
- has_jaccard_overlap = True
- if sampler[8] == 0 and sampler[9] == 0:
- has_object_coverage = False
- else:
- has_object_coverage = True
- if not has_jaccard_overlap and not has_object_coverage:
- return True
- found = False
- for i in range(len(gt_bboxes)):
- object_bbox = [
- gt_bboxes[i][0], gt_bboxes[i][1], gt_bboxes[i][2], gt_bboxes[i][3]
- ]
- if has_jaccard_overlap:
- overlap = jaccard_overlap(sample_bbox, object_bbox)
- if sampler[6] != 0 and \
- overlap < sampler[6]:
- continue
- if sampler[7] != 0 and \
- overlap > sampler[7]:
- continue
- found = True
- if has_object_coverage:
- object_coverage = bbox_coverage(object_bbox, sample_bbox)
- if sampler[8] != 0 and \
- object_coverage < sampler[8]:
- continue
- if sampler[9] != 0 and \
- object_coverage > sampler[9]:
- continue
- found = True
- if found:
- return True
- return found
- def crop_image_sampling(img, sample_bbox, image_width, image_height,
- target_size):
- # no clipping here
- xmin = int(sample_bbox[0] * image_width)
- xmax = int(sample_bbox[2] * image_width)
- ymin = int(sample_bbox[1] * image_height)
- ymax = int(sample_bbox[3] * image_height)
- w_off = xmin
- h_off = ymin
- width = xmax - xmin
- height = ymax - ymin
- cross_xmin = max(0.0, float(w_off))
- cross_ymin = max(0.0, float(h_off))
- cross_xmax = min(float(w_off + width - 1.0), float(image_width))
- cross_ymax = min(float(h_off + height - 1.0), float(image_height))
- cross_width = cross_xmax - cross_xmin
- cross_height = cross_ymax - cross_ymin
- roi_xmin = 0 if w_off >= 0 else abs(w_off)
- roi_ymin = 0 if h_off >= 0 else abs(h_off)
- roi_width = cross_width
- roi_height = cross_height
- roi_y1 = int(roi_ymin)
- roi_y2 = int(roi_ymin + roi_height)
- roi_x1 = int(roi_xmin)
- roi_x2 = int(roi_xmin + roi_width)
- cross_y1 = int(cross_ymin)
- cross_y2 = int(cross_ymin + cross_height)
- cross_x1 = int(cross_xmin)
- cross_x2 = int(cross_xmin + cross_width)
- sample_img = np.zeros((height, width, 3))
- sample_img[roi_y1: roi_y2, roi_x1: roi_x2] = \
- img[cross_y1: cross_y2, cross_x1: cross_x2]
- sample_img = cv2.resize(
- sample_img, (target_size, target_size), interpolation=cv2.INTER_AREA)
- return sample_img
- def box_horizontal_flip(bboxes, width):
- oldx1 = bboxes[:, 0].copy()
- oldx2 = bboxes[:, 2].copy()
- bboxes[:, 0] = width - oldx2 - 1
- bboxes[:, 2] = width - oldx1 - 1
- if bboxes.shape[0] != 0 and (bboxes[:, 2] < bboxes[:, 0]).all():
- raise ValueError(
- "RandomHorizontalFlip: invalid box, x2 should be greater than x1")
- return bboxes
- def segms_horizontal_flip(segms, height, width):
- def _flip_poly(poly, width):
- flipped_poly = np.array(poly)
- flipped_poly[0::2] = width - np.array(poly[0::2]) - 1
- return flipped_poly.tolist()
- def _flip_rle(rle, height, width):
- if 'counts' in rle and type(rle['counts']) == list:
- rle = mask_util.frPyObjects([rle], height, width)
- mask = mask_util.decode(rle)
- mask = mask[:, ::-1, :]
- rle = mask_util.encode(np.array(mask, order='F', dtype=np.uint8))
- return rle
- def is_poly(segm):
- if not isinstance(segm, (list, dict)):
- raise Exception("Invalid segm type: {}".format(type(segm)))
- return isinstance(segm, list)
- flipped_segms = []
- for segm in segms:
- if is_poly(segm):
- # Polygon format
- flipped_segms.append([_flip_poly(poly, width) for poly in segm])
- else:
- # RLE format
- import pycocotools.mask as mask_util
- flipped_segms.append(_flip_rle(segm, height, width))
- return flipped_segms
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