From 30489edbdf8f01f11c0ffdfdfcb32d7a04bb5df9 Mon Sep 17 00:00:00 2001 From: Guanghua Yu <742925032@qq.com> Date: Mon, 30 Sep 2019 09:56:54 +0800 Subject: [PATCH] Add data anchor sampling for face detection. (#3450) * add CropImageWithDataAnchorSampling * Refine bbox sampling code * Update comments * fix import and variable problem --- ppdet/data/transform/op_helper.py | 258 +++++++++++++++++++++++++++++- ppdet/data/transform/operators.py | 153 +++++++++++++++++- 2 files changed, 404 insertions(+), 7 deletions(-) diff --git a/ppdet/data/transform/op_helper.py b/ppdet/data/transform/op_helper.py index f035ea758..f46f9c4e5 100644 --- a/ppdet/data/transform/op_helper.py +++ b/ppdet/data/transform/op_helper.py @@ -18,6 +18,9 @@ from __future__ import division from __future__ import print_function import numpy as np +import random +import math +import cv2 def meet_emit_constraint(src_bbox, sample_bbox): @@ -40,20 +43,35 @@ def clip_bbox(src_bbox): def bbox_area(src_bbox): - width = src_bbox[2] - src_bbox[0] - height = src_bbox[3] - src_bbox[1] - return width * height + 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(labels)): + 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 @@ -72,6 +90,25 @@ def filter_and_process(sample_bbox, bboxes, labels, scores=None): 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]) + 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]) @@ -89,6 +126,112 @@ def generate_sample_bbox(sampler): 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 \ @@ -108,6 +251,29 @@ def jaccard_overlap(sample_bbox, object_bbox): 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, @@ -136,3 +302,87 @@ def satisfy_sample_constraint(sampler, 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 diff --git a/ppdet/data/transform/operators.py b/ppdet/data/transform/operators.py index 8cb4830e5..62e38588d 100644 --- a/ppdet/data/transform/operators.py +++ b/ppdet/data/transform/operators.py @@ -31,7 +31,9 @@ from PIL import Image, ImageEnhance from ppdet.core.workspace import serializable from .op_helper import (satisfy_sample_constraint, filter_and_process, - generate_sample_bbox, clip_bbox) + generate_sample_bbox, clip_bbox, data_anchor_sampling, + satisfy_sample_constraint_coverage, crop_image_sampling, + generate_sample_bbox_square, bbox_area_sampling) logger = logging.getLogger(__name__) @@ -526,8 +528,6 @@ class CropImage(BaseOperator): batch_sampler (list): Multiple sets of different parameters for cropping. satisfy_all (bool): whether all boxes must satisfy. - avoid_no_bbox (bool): whether to to avoid the - situation where the box does not appear. e.g.[[1, 1, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.1, 1.0], [1, 50, 0.3, 1.0, 0.5, 2.0, 0.3, 1.0], @@ -538,6 +538,8 @@ class CropImage(BaseOperator): [max sample, max trial, min scale, max scale, min aspect ratio, max aspect ratio, min overlap, max overlap] + avoid_no_bbox (bool): whether to to avoid the + situation where the box does not appear. """ super(CropImage, self).__init__() self.batch_sampler = batch_sampler @@ -599,6 +601,151 @@ class CropImage(BaseOperator): return sample +@register_op +class CropImageWithDataAchorSampling(BaseOperator): + def __init__(self, + batch_sampler, + anchor_sampler=None, + target_size=None, + das_anchor_scales=[16, 32, 64, 128], + sampling_prob=0.5, + min_size=8., + avoid_no_bbox=True): + """ + Args: + anchor_sampler (list): anchor_sampling sets of different + parameters for cropping. + batch_sampler (list): Multiple sets of different + parameters for cropping. + e.g.[[1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]] + [[1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], + [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], + [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], + [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], + [1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0]] + [max sample, max trial, min scale, max scale, + min aspect ratio, max aspect ratio, + min overlap, max overlap, min coverage, max coverage] + target_size (bool): target image size. + das_anchor_scales (list[float]): a list of anchor scales in data + anchor smapling. + min_size (float): minimum size of sampled bbox. + avoid_no_bbox (bool): whether to to avoid the + situation where the box does not appear. + """ + super(CropImageWithDataAchorSampling, self).__init__() + self.anchor_sampler = anchor_sampler + self.batch_sampler = batch_sampler + self.target_size = target_size + self.sampling_prob = sampling_prob + self.min_size = min_size + self.avoid_no_bbox = avoid_no_bbox + self.scale_array = np.array(das_anchor_scales) + + def __call__(self, sample, context): + """ + Crop the image and modify bounding box. + Operators: + 1. Scale the image weight and height. + 2. Crop the image according to a radom sample. + 3. Rescale the bounding box. + 4. Determine if the new bbox is satisfied in the new image. + Returns: + sample: the image, bounding box are replaced. + """ + assert 'image' in sample, "image data not found" + im = sample['image'] + gt_bbox = sample['gt_bbox'] + gt_class = sample['gt_class'] + image_width = sample['w'] + image_height = sample['h'] + gt_score = None + if 'gt_score' in sample: + gt_score = sample['gt_score'] + sampled_bbox = [] + gt_bbox = gt_bbox.tolist() + + prob = np.random.uniform(0., 1.) + if prob > self.sampling_prob: # anchor sampling + assert self.anchor_sampler + for sampler in self.anchor_sampler: + found = 0 + for i in range(sampler[1]): + if found >= sampler[0]: + break + sample_bbox = data_anchor_sampling( + gt_bbox, image_width, image_height, self.scale_array, + self.target_size) + if sample_bbox == 0: + break + if satisfy_sample_constraint_coverage(sampler, sample_bbox, + gt_bbox): + sampled_bbox.append(sample_bbox) + found = found + 1 + im = np.array(im) + while sampled_bbox: + idx = int(np.random.uniform(0, len(sampled_bbox))) + sample_bbox = sampled_bbox.pop(idx) + + crop_bbox, crop_class, crop_score = filter_and_process( + sample_bbox, gt_bbox, gt_class, gt_score) + crop_bbox, crop_class, crop_score = bbox_area_sampling( + crop_bbox, crop_class, crop_score, self.target_size, + self.min_size) + + if self.avoid_no_bbox: + if len(crop_bbox) < 1: + continue + im = crop_image_sampling(im, sample_bbox, image_width, + image_height, self.target_size) + sample['image'] = im + sample['gt_bbox'] = crop_bbox + sample['gt_class'] = crop_class + sample['gt_score'] = crop_score + return sample + return sample + + else: + for sampler in self.batch_sampler: + found = 0 + for i in range(sampler[1]): + if found >= sampler[0]: + break + sample_bbox = generate_sample_bbox_square( + sampler, image_width, image_height) + if satisfy_sample_constraint_coverage(sampler, sample_bbox, + gt_bbox): + sampled_bbox.append(sample_bbox) + found = found + 1 + im = np.array(im) + while sampled_bbox: + idx = int(np.random.uniform(0, len(sampled_bbox))) + sample_bbox = sampled_bbox.pop(idx) + sample_bbox = clip_bbox(sample_bbox) + + crop_bbox, crop_class, crop_score = filter_and_process( + sample_bbox, gt_bbox, gt_class, gt_score) + # sampling bbox according the bbox area + crop_bbox, crop_class, crop_score = bbox_area_sampling( + crop_bbox, crop_class, crop_score, self.target_size, + self.min_size) + + if self.avoid_no_bbox: + if len(crop_bbox) < 1: + continue + 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) + im = im[ymin:ymax, xmin:xmax] + sample['image'] = im + sample['gt_bbox'] = crop_bbox + sample['gt_class'] = crop_class + sample['gt_score'] = crop_score + return sample + return sample + + @register_op class NormalizeBox(BaseOperator): """Transform the bounding box's coornidates to [0,1].""" -- GitLab