from __future__ import absolute_import from __future__ import division from __future__ import print_function from PIL import Image, ImageEnhance, ImageDraw from PIL import ImageFile import numpy as np import random import math import cv2 ImageFile.LOAD_TRUNCATED_IMAGES = True #otherwise IOError raised image file is truncated class sampler(): def __init__(self, max_sample, max_trial, min_scale, max_scale, min_aspect_ratio, max_aspect_ratio, min_jaccard_overlap, max_jaccard_overlap, min_object_coverage, max_object_coverage, use_square=False): self.max_sample = max_sample self.max_trial = max_trial self.min_scale = min_scale self.max_scale = max_scale self.min_aspect_ratio = min_aspect_ratio self.max_aspect_ratio = max_aspect_ratio self.min_jaccard_overlap = min_jaccard_overlap self.max_jaccard_overlap = max_jaccard_overlap self.min_object_coverage = min_object_coverage self.max_object_coverage = max_object_coverage self.use_square = use_square class bbox(): def __init__(self, xmin, ymin, xmax, ymax): self.xmin = xmin self.ymin = ymin self.xmax = xmax self.ymax = ymax def intersect_bbox(bbox1, bbox2): if bbox2.xmin > bbox1.xmax or bbox2.xmax < bbox1.xmin or \ bbox2.ymin > bbox1.ymax or bbox2.ymax < bbox1.ymin: intersection_box = bbox(0.0, 0.0, 0.0, 0.0) else: intersection_box = bbox( max(bbox1.xmin, bbox2.xmin), max(bbox1.ymin, bbox2.ymin), min(bbox1.xmax, bbox2.xmax), min(bbox1.ymax, bbox2.ymax)) 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 bbox_area(src_bbox): if src_bbox.xmax < src_bbox.xmin or src_bbox.ymax < src_bbox.ymin: return 0. else: width = src_bbox.xmax - src_bbox.xmin height = src_bbox.ymax - src_bbox.ymin return width * height def generate_sample(sampler, image_width, image_height): scale = np.random.uniform(sampler.min_scale, sampler.max_scale) aspect_ratio = np.random.uniform(sampler.min_aspect_ratio, sampler.max_aspect_ratio) 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) # guarantee a squared image patch after cropping if sampler.use_square: 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 = bbox(xmin, ymin, xmax, ymax) return sampled_bbox def data_anchor_sampling(sampler, bbox_labels, image_width, image_height, scale_array, resize_width, resize_height): 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][1] norm_ymin = bbox_labels[rand_idx][2] norm_xmax = bbox_labels[rand_idx][3] norm_ymax = bbox_labels[rand_idx][4] 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 for scale_ind in range(0, len(scale_array) - 1): area = wid * hei if area > scale_array[scale_ind] ** 2 and area < \ scale_array[scale_ind + 1] ** 2: range_size = scale_ind + 1 break scale_choose = 0.0 if range_size == 0: rand_idx_size = range_size + 1 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) 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 = np.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 = 0.0 h_off = 0.0 w_off = float(w_off_orig / image_width) h_off = float(h_off_orig / image_height) sampled_bbox = bbox(w_off, h_off, w_off + float(sample_bbox_size / image_width), h_off + float(sample_bbox_size / image_height)) return sampled_bbox def jaccard_overlap(sample_bbox, object_bbox): if sample_bbox.xmin >= object_bbox.xmax or \ sample_bbox.xmax <= object_bbox.xmin or \ sample_bbox.ymin >= object_bbox.ymax or \ sample_bbox.ymax <= object_bbox.ymin: return 0 intersect_xmin = max(sample_bbox.xmin, object_bbox.xmin) intersect_ymin = max(sample_bbox.ymin, object_bbox.ymin) intersect_xmax = min(sample_bbox.xmax, object_bbox.xmax) intersect_ymax = min(sample_bbox.ymax, object_bbox.ymax) 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 satisfy_sample_constraint(sampler, sample_bbox, bbox_labels): if sampler.min_jaccard_overlap == 0 and sampler.max_jaccard_overlap == 0: has_jaccard_overlap = False else: has_jaccard_overlap = True if sampler.min_object_coverage == 0 and sampler.max_object_coverage == 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(bbox_labels)): object_bbox = bbox(bbox_labels[i][1], bbox_labels[i][2], bbox_labels[i][3], bbox_labels[i][4]) if has_jaccard_overlap: overlap = jaccard_overlap(sample_bbox, object_bbox) if sampler.min_jaccard_overlap != 0 and \ overlap < sampler.min_jaccard_overlap: continue if sampler.max_jaccard_overlap != 0 and \ overlap > sampler.max_jaccard_overlap: continue found = True if has_object_coverage: object_coverage = bbox_coverage(object_bbox, sample_bbox) if sampler.min_object_coverage != 0 and \ object_coverage < sampler.min_object_coverage: continue if sampler.max_object_coverage != 0 and \ object_coverage > sampler.max_object_coverage: continue found = True if found: return True return found def generate_batch_samples(batch_sampler, bbox_labels, image_width, image_height): sampled_bbox = [] for sampler in batch_sampler: found = 0 for i in range(sampler.max_trial): if found >= sampler.max_sample: break sample_bbox = generate_sample(sampler, image_width, image_height) if satisfy_sample_constraint(sampler, sample_bbox, bbox_labels): sampled_bbox.append(sample_bbox) found = found + 1 return sampled_bbox def generate_batch_random_samples(batch_sampler, bbox_labels, image_width, image_height, scale_array, resize_width, resize_height): sampled_bbox = [] for sampler in batch_sampler: found = 0 for i in range(sampler.max_trial): if found >= sampler.max_sample: break sample_bbox = data_anchor_sampling( sampler, bbox_labels, image_width, image_height, scale_array, resize_width, resize_height) if satisfy_sample_constraint(sampler, sample_bbox, bbox_labels): sampled_bbox.append(sample_bbox) found = found + 1 return sampled_bbox def clip_bbox(src_bbox): src_bbox.xmin = max(min(src_bbox.xmin, 1.0), 0.0) src_bbox.ymin = max(min(src_bbox.ymin, 1.0), 0.0) src_bbox.xmax = max(min(src_bbox.xmax, 1.0), 0.0) src_bbox.ymax = max(min(src_bbox.ymax, 1.0), 0.0) return src_bbox def meet_emit_constraint(src_bbox, sample_bbox): center_x = (src_bbox.xmax + src_bbox.xmin) / 2 center_y = (src_bbox.ymax + src_bbox.ymin) / 2 if center_x >= sample_bbox.xmin and \ center_x <= sample_bbox.xmax and \ center_y >= sample_bbox.ymin and \ center_y <= sample_bbox.ymax: return True return False def project_bbox(object_bbox, sample_bbox): if object_bbox.xmin >= sample_bbox.xmax or \ object_bbox.xmax <= sample_bbox.xmin or \ object_bbox.ymin >= sample_bbox.ymax or \ object_bbox.ymax <= sample_bbox.ymin: return False else: proj_bbox = bbox(0, 0, 0, 0) sample_width = sample_bbox.xmax - sample_bbox.xmin sample_height = sample_bbox.ymax - sample_bbox.ymin proj_bbox.xmin = (object_bbox.xmin - sample_bbox.xmin) / sample_width proj_bbox.ymin = (object_bbox.ymin - sample_bbox.ymin) / sample_height proj_bbox.xmax = (object_bbox.xmax - sample_bbox.xmin) / sample_width proj_bbox.ymax = (object_bbox.ymax - sample_bbox.ymin) / sample_height proj_bbox = clip_bbox(proj_bbox) if bbox_area(proj_bbox) > 0: return proj_bbox else: return False def transform_labels(bbox_labels, sample_bbox): sample_labels = [] for i in range(len(bbox_labels)): sample_label = [] object_bbox = bbox(bbox_labels[i][1], bbox_labels[i][2], bbox_labels[i][3], bbox_labels[i][4]) if not meet_emit_constraint(object_bbox, sample_bbox): continue proj_bbox = project_bbox(object_bbox, sample_bbox) if proj_bbox: sample_label.append(bbox_labels[i][0]) sample_label.append(float(proj_bbox.xmin)) sample_label.append(float(proj_bbox.ymin)) sample_label.append(float(proj_bbox.xmax)) sample_label.append(float(proj_bbox.ymax)) sample_label = sample_label + bbox_labels[i][5:] sample_labels.append(sample_label) return sample_labels def transform_labels_sampling(bbox_labels, sample_bbox, resize_val, min_face_size): sample_labels = [] for i in range(len(bbox_labels)): sample_label = [] object_bbox = bbox(bbox_labels[i][1], bbox_labels[i][2], bbox_labels[i][3], bbox_labels[i][4]) if not meet_emit_constraint(object_bbox, sample_bbox): continue proj_bbox = project_bbox(object_bbox, sample_bbox) if proj_bbox: real_width = float((proj_bbox.xmax - proj_bbox.xmin) * resize_val) real_height = float((proj_bbox.ymax - proj_bbox.ymin) * resize_val) if real_width * real_height < float(min_face_size * min_face_size): continue else: sample_label.append(bbox_labels[i][0]) sample_label.append(float(proj_bbox.xmin)) sample_label.append(float(proj_bbox.ymin)) sample_label.append(float(proj_bbox.xmax)) sample_label.append(float(proj_bbox.ymax)) sample_label = sample_label + bbox_labels[i][5:] sample_labels.append(sample_label) return sample_labels def crop_image(img, bbox_labels, sample_bbox, image_width, image_height, resize_width, resize_height, min_face_size): sample_bbox = clip_bbox(sample_bbox) xmin = int(sample_bbox.xmin * image_width) xmax = int(sample_bbox.xmax * image_width) ymin = int(sample_bbox.ymin * image_height) ymax = int(sample_bbox.ymax * image_height) sample_img = img[ymin:ymax, xmin:xmax] resize_val = resize_width sample_labels = transform_labels_sampling(bbox_labels, sample_bbox, resize_val, min_face_size) return sample_img, sample_labels def crop_image_sampling(img, bbox_labels, sample_bbox, image_width, image_height, resize_width, resize_height, min_face_size): # no clipping here xmin = int(sample_bbox.xmin * image_width) xmax = int(sample_bbox.xmax * image_width) ymin = int(sample_bbox.ymin * image_height) ymax = int(sample_bbox.ymax * 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, (resize_width, resize_height), interpolation=cv2.INTER_AREA) resize_val = resize_width sample_labels = transform_labels_sampling(bbox_labels, sample_bbox, resize_val, min_face_size) return sample_img, sample_labels def random_brightness(img, settings): prob = np.random.uniform(0, 1) if prob < settings.brightness_prob: delta = np.random.uniform(-settings.brightness_delta, settings.brightness_delta) + 1 img = ImageEnhance.Brightness(img).enhance(delta) return img def random_contrast(img, settings): prob = np.random.uniform(0, 1) if prob < settings.contrast_prob: delta = np.random.uniform(-settings.contrast_delta, settings.contrast_delta) + 1 img = ImageEnhance.Contrast(img).enhance(delta) return img def random_saturation(img, settings): prob = np.random.uniform(0, 1) if prob < settings.saturation_prob: delta = np.random.uniform(-settings.saturation_delta, settings.saturation_delta) + 1 img = ImageEnhance.Color(img).enhance(delta) return img def random_hue(img, settings): prob = np.random.uniform(0, 1) if prob < settings.hue_prob: delta = np.random.uniform(-settings.hue_delta, settings.hue_delta) img_hsv = np.array(img.convert('HSV')) img_hsv[:, :, 0] = img_hsv[:, :, 0] + delta img = Image.fromarray(img_hsv, mode='HSV').convert('RGB') return img def distort_image(img, settings): prob = np.random.uniform(0, 1) # Apply different distort order if prob > 0.5: img = random_brightness(img, settings) img = random_contrast(img, settings) img = random_saturation(img, settings) img = random_hue(img, settings) else: img = random_brightness(img, settings) img = random_saturation(img, settings) img = random_hue(img, settings) img = random_contrast(img, settings) return img def expand_image(img, bbox_labels, img_width, img_height, settings): prob = np.random.uniform(0, 1) if prob < settings.expand_prob: if settings.expand_max_ratio - 1 >= 0.01: expand_ratio = np.random.uniform(1, settings.expand_max_ratio) height = int(img_height * expand_ratio) width = int(img_width * expand_ratio) h_off = math.floor(np.random.uniform(0, height - img_height)) w_off = math.floor(np.random.uniform(0, width - img_width)) expand_bbox = bbox(-w_off / img_width, -h_off / img_height, (width - w_off) / img_width, (height - h_off) / img_height) expand_img = np.ones((height, width, 3)) expand_img = np.uint8(expand_img * np.squeeze(settings.img_mean)) expand_img = Image.fromarray(expand_img) expand_img.paste(img, (int(w_off), int(h_off))) bbox_labels = transform_labels(bbox_labels, expand_bbox) return expand_img, bbox_labels, width, height return img, bbox_labels, img_width, img_height