forked from mindspore-Ecosystem/mindspore
376 lines
14 KiB
Python
376 lines
14 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""SSD dataset"""
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from __future__ import division
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import os
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import math
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import itertools as it
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import numpy as np
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import cv2
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import mindspore.dataset as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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from mindspore.mindrecord import FileWriter
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from config import ConfigSSD
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config = ConfigSSD()
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class GeneratDefaultBoxes():
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"""
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Generate Default boxes for SSD, follows the order of (W, H, archor_sizes).
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`self.default_boxes` has a shape of [archor_sizes, H, W, 4], the last dimension is [x, y, w, h].
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`self.default_boxes_ltrb` has a shape as `self.default_boxes`, the last dimension is [x1, y1, x2, y2].
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"""
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def __init__(self):
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fk = config.IMG_SHAPE[0] / np.array(config.STEPS)
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self.default_boxes = []
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for idex, feature_size in enumerate(config.FEATURE_SIZE):
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sk1 = config.SCALES[idex] / config.IMG_SHAPE[0]
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sk2 = config.SCALES[idex + 1] / config.IMG_SHAPE[0]
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sk3 = math.sqrt(sk1 * sk2)
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if config.NUM_DEFAULT[idex] == 3:
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all_sizes = [(0.5, 1.0), (1.0, 1.0), (1.0, 0.5)]
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else:
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all_sizes = [(sk1, sk1), (sk3, sk3)]
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for aspect_ratio in config.ASPECT_RATIOS[idex]:
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w, h = sk1 * math.sqrt(aspect_ratio), sk1 / math.sqrt(aspect_ratio)
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all_sizes.append((w, h))
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all_sizes.append((h, w))
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assert len(all_sizes) == config.NUM_DEFAULT[idex]
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for i, j in it.product(range(feature_size), repeat=2):
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for w, h in all_sizes:
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cx, cy = (j + 0.5) / fk[idex], (i + 0.5) / fk[idex]
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box = [np.clip(k, 0, 1) for k in (cx, cy, w, h)]
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self.default_boxes.append(box)
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def to_ltrb(cx, cy, w, h):
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return cx - w / 2, cy - h / 2, cx + w / 2, cy + h / 2
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# For IoU calculation
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self.default_boxes_ltrb = np.array(tuple(to_ltrb(*i) for i in self.default_boxes), dtype='float32')
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self.default_boxes = np.array(self.default_boxes, dtype='float32')
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default_boxes_ltrb = GeneratDefaultBoxes().default_boxes_ltrb
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default_boxes = GeneratDefaultBoxes().default_boxes
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x1, y1, x2, y2 = np.split(default_boxes_ltrb[:, :4], 4, axis=-1)
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vol_anchors = (x2 - x1) * (y2 - y1)
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matching_threshold = config.MATCH_THRESHOLD
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def ssd_bboxes_encode(boxes):
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"""
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Labels anchors with ground truth inputs.
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Args:
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boxex: ground truth with shape [N, 5], for each row, it stores [x, y, w, h, cls].
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Returns:
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gt_loc: location ground truth with shape [num_anchors, 4].
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gt_label: class ground truth with shape [num_anchors, 1].
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num_matched_boxes: number of positives in an image.
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"""
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def jaccard_with_anchors(bbox):
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"""Compute jaccard score a box and the anchors."""
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# Intersection bbox and volume.
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xmin = np.maximum(x1, bbox[0])
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ymin = np.maximum(y1, bbox[1])
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xmax = np.minimum(x2, bbox[2])
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ymax = np.minimum(y2, bbox[3])
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w = np.maximum(xmax - xmin, 0.)
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h = np.maximum(ymax - ymin, 0.)
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# Volumes.
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inter_vol = h * w
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union_vol = vol_anchors + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) - inter_vol
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jaccard = inter_vol / union_vol
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return np.squeeze(jaccard)
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pre_scores = np.zeros((config.NUM_SSD_BOXES), dtype=np.float32)
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t_boxes = np.zeros((config.NUM_SSD_BOXES, 4), dtype=np.float32)
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t_label = np.zeros((config.NUM_SSD_BOXES), dtype=np.int64)
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for bbox in boxes:
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label = int(bbox[4])
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scores = jaccard_with_anchors(bbox)
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mask = (scores > matching_threshold)
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if not np.any(mask):
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mask[np.argmax(scores)] = True
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mask = mask & (scores > pre_scores)
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pre_scores = np.maximum(pre_scores, scores)
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t_label = mask * label + (1 - mask) * t_label
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for i in range(4):
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t_boxes[:, i] = mask * bbox[i] + (1 - mask) * t_boxes[:, i]
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index = np.nonzero(t_label)
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# Transform to ltrb.
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bboxes = np.zeros((config.NUM_SSD_BOXES, 4), dtype=np.float32)
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bboxes[:, [0, 1]] = (t_boxes[:, [0, 1]] + t_boxes[:, [2, 3]]) / 2
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bboxes[:, [2, 3]] = t_boxes[:, [2, 3]] - t_boxes[:, [0, 1]]
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# Encode features.
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bboxes_t = bboxes[index]
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default_boxes_t = default_boxes[index]
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bboxes_t[:, :2] = (bboxes_t[:, :2] - default_boxes_t[:, :2]) / (default_boxes_t[:, 2:] * config.PRIOR_SCALING[0])
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bboxes_t[:, 2:4] = np.log(bboxes_t[:, 2:4] / default_boxes_t[:, 2:4]) / config.PRIOR_SCALING[1]
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bboxes[index] = bboxes_t
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num_match_num = np.array([len(np.nonzero(t_label)[0])], dtype=np.int32)
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return bboxes, t_label.astype(np.int32), num_match_num
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def ssd_bboxes_decode(boxes, index):
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"""Decode predict boxes to [x, y, w, h]"""
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boxes_t = boxes[index]
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default_boxes_t = default_boxes[index]
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boxes_t[:, :2] = boxes_t[:, :2] * config.PRIOR_SCALING[0] * default_boxes_t[:, 2:] + default_boxes_t[:, :2]
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boxes_t[:, 2:4] = np.exp(boxes_t[:, 2:4] * config.PRIOR_SCALING[1]) * default_boxes_t[:, 2:4]
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bboxes = np.zeros((len(boxes_t), 4), dtype=np.float32)
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bboxes[:, [0, 1]] = boxes_t[:, [0, 1]] - boxes_t[:, [2, 3]] / 2
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bboxes[:, [2, 3]] = boxes_t[:, [0, 1]] + boxes_t[:, [2, 3]] / 2
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return bboxes
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def preprocess_fn(image, box, is_training):
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"""Preprocess function for dataset."""
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def _rand(a=0., b=1.):
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"""Generate random."""
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return np.random.rand() * (b - a) + a
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def _infer_data(image, input_shape, box):
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img_h, img_w, _ = image.shape
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input_h, input_w = input_shape
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scale = min(float(input_w) / float(img_w), float(input_h) / float(img_h))
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nw = int(img_w * scale)
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nh = int(img_h * scale)
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image = cv2.resize(image, (nw, nh))
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new_image = np.zeros((input_h, input_w, 3), np.float32)
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dh = (input_h - nh) // 2
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dw = (input_w - nw) // 2
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new_image[dh: (nh + dh), dw: (nw + dw), :] = image
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image = new_image
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#When the channels of image is 1
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if len(image.shape) == 2:
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image = np.expand_dims(image, axis=-1)
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image = np.concatenate([image, image, image], axis=-1)
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box = box.astype(np.float32)
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box[:, [0, 2]] = (box[:, [0, 2]] * scale + dw) / input_w
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box[:, [1, 3]] = (box[:, [1, 3]] * scale + dh) / input_h
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return image, np.array((img_h, img_w), np.float32), box
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def _data_aug(image, box, is_training, image_size=(300, 300)):
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"""Data augmentation function."""
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ih, iw, _ = image.shape
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w, h = image_size
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if not is_training:
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return _infer_data(image, image_size, box)
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# Random settings
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scale_w = _rand(0.75, 1.25)
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scale_h = _rand(0.75, 1.25)
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flip = _rand() < .5
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nw = iw * scale_w
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nh = ih * scale_h
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scale = min(w / nw, h / nh)
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nw = int(scale * nw)
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nh = int(scale * nh)
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# Resize image
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image = cv2.resize(image, (nw, nh))
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# place image
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new_image = np.zeros((h, w, 3), dtype=np.float32)
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dw = (w - nw) // 2
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dh = (h - nh) // 2
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new_image[dh:dh + nh, dw:dw + nw, :] = image
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image = new_image
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# Flip image or not
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if flip:
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image = cv2.flip(image, 1, dst=None)
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# Convert image to gray or not
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gray = _rand() < .25
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if gray:
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# When the channels of image is 1
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if len(image.shape) == 2:
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image = np.expand_dims(image, axis=-1)
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image = np.concatenate([image, image, image], axis=-1)
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box = box.astype(np.float32)
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# Transform box with shape[x1, y1, x2, y2].
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box[:, [0, 2]] = (box[:, [0, 2]] * scale * scale_w + dw) / w
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box[:, [1, 3]] = (box[:, [1, 3]] * scale * scale_h + dh) / h
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if flip:
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box[:, [0, 2]] = 1 - box[:, [2, 0]]
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box, label, num_match_num = ssd_bboxes_encode(box)
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return image, box, label, num_match_num
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return _data_aug(image, box, is_training, image_size=config.IMG_SHAPE)
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def create_coco_label(is_training):
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"""Get image path and annotation from COCO."""
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from pycocotools.coco import COCO
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coco_root = config.COCO_ROOT
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data_type = config.VAL_DATA_TYPE
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if is_training:
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data_type = config.TRAIN_DATA_TYPE
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#Classes need to train or test.
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train_cls = config.COCO_CLASSES
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train_cls_dict = {}
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for i, cls in enumerate(train_cls):
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train_cls_dict[cls] = i
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anno_json = os.path.join(coco_root, config.INSTANCES_SET.format(data_type))
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coco = COCO(anno_json)
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classs_dict = {}
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cat_ids = coco.loadCats(coco.getCatIds())
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for cat in cat_ids:
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classs_dict[cat["id"]] = cat["name"]
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image_ids = coco.getImgIds()
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image_files = []
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image_anno_dict = {}
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for img_id in image_ids:
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image_info = coco.loadImgs(img_id)
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file_name = image_info[0]["file_name"]
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anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
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anno = coco.loadAnns(anno_ids)
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image_path = os.path.join(coco_root, data_type, file_name)
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annos = []
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for label in anno:
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bbox = label["bbox"]
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class_name = classs_dict[label["category_id"]]
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if class_name in train_cls:
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x_min, x_max = bbox[0], bbox[0] + bbox[2]
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y_min, y_max = bbox[1], bbox[1] + bbox[3]
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annos.append(list(map(round, [x_min, y_min, x_max, y_max])) + [train_cls_dict[class_name]])
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if len(annos) >= 1:
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image_files.append(image_path)
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image_anno_dict[image_path] = np.array(annos)
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return image_files, image_anno_dict
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def anno_parser(annos_str):
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"""Parse annotation from string to list."""
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annos = []
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for anno_str in annos_str:
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anno = list(map(int, anno_str.strip().split(',')))
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annos.append(anno)
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return annos
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def filter_valid_data(image_dir, anno_path):
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"""Filter valid image file, which both in image_dir and anno_path."""
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image_files = []
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image_anno_dict = {}
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if not os.path.isdir(image_dir):
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raise RuntimeError("Path given is not valid.")
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if not os.path.isfile(anno_path):
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raise RuntimeError("Annotation file is not valid.")
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with open(anno_path, "rb") as f:
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lines = f.readlines()
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for line in lines:
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line_str = line.decode("utf-8").strip()
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line_split = str(line_str).split(' ')
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file_name = line_split[0]
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image_path = os.path.join(image_dir, file_name)
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if os.path.isfile(image_path):
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image_anno_dict[image_path] = anno_parser(line_split[1:])
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image_files.append(image_path)
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return image_files, image_anno_dict
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def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="ssd.mindrecord", file_num=8):
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"""Create MindRecord file."""
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mindrecord_dir = config.MINDRECORD_DIR
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mindrecord_path = os.path.join(mindrecord_dir, prefix)
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writer = FileWriter(mindrecord_path, file_num)
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if dataset == "coco":
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image_files, image_anno_dict = create_coco_label(is_training)
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else:
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image_files, image_anno_dict = filter_valid_data(config.IMAGE_DIR, config.ANNO_PATH)
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ssd_json = {
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"image": {"type": "bytes"},
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"annotation": {"type": "int32", "shape": [-1, 5]},
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}
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writer.add_schema(ssd_json, "ssd_json")
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for image_name in image_files:
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with open(image_name, 'rb') as f:
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img = f.read()
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annos = np.array(image_anno_dict[image_name], dtype=np.int32)
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row = {"image": img, "annotation": annos}
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writer.write_raw_data([row])
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writer.commit()
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def create_ssd_dataset(mindrecord_file, batch_size=32, repeat_num=10, device_num=1, rank=0,
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is_training=True, num_parallel_workers=4):
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"""Creatr SSD dataset with MindDataset."""
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ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank,
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num_parallel_workers=num_parallel_workers, shuffle=is_training)
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decode = C.Decode()
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ds = ds.map(input_columns=["image"], operations=decode)
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compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
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if is_training:
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hwc_to_chw = C.HWC2CHW()
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ds = ds.map(input_columns=["image", "annotation"],
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output_columns=["image", "box", "label", "num_match_num"],
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columns_order=["image", "box", "label", "num_match_num"],
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operations=compose_map_func, python_multiprocessing=True, num_parallel_workers=num_parallel_workers)
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ds = ds.map(input_columns=["image"], operations=hwc_to_chw, python_multiprocessing=True,
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num_parallel_workers=num_parallel_workers)
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ds = ds.batch(batch_size, drop_remainder=True)
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ds = ds.repeat(repeat_num)
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else:
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hwc_to_chw = C.HWC2CHW()
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ds = ds.map(input_columns=["image", "annotation"],
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output_columns=["image", "image_shape", "annotation"],
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columns_order=["image", "image_shape", "annotation"],
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operations=compose_map_func)
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ds = ds.map(input_columns=["image"], operations=hwc_to_chw, num_parallel_workers=num_parallel_workers)
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ds = ds.batch(batch_size, drop_remainder=True)
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ds = ds.repeat(repeat_num)
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return ds
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