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utils.py
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#! /usr/bin/env python
# coding=utf-8
#================================================================
# Copyright (C) 2019 * Ltd. All rights reserved.
#
# Editor : VIM
# File name : utils.py
# Author : YunYang1994
# Created date: 2019-02-28 13:14:19
# Description :
#
#================================================================
import cv2
import random
import colorsys
import numpy as np
import tensorflow as tf
from core.config import cfg
def read_class_names(class_file_name):
'''loads class name from a file'''
names = {}
with open(class_file_name, 'r') as data:
for ID, name in enumerate(data):
names[ID] = name.strip('\n')
return names
def get_anchors(anchors_path):
'''loads the anchors from a file'''
with open(anchors_path) as f:
anchors = f.readline()
anchors = np.array(anchors.split(','), dtype=np.float32)
return anchors.reshape(3, 3, 2)
def image_preporcess(image, target_size, gt_boxes=None):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
ih, iw = target_size
h, w, _ = image.shape
scale = min(iw/w, ih/h)
nw, nh = int(scale * w), int(scale * h)
image_resized = cv2.resize(image, (nw, nh))
image_paded = np.full(shape=[ih, iw, 3], fill_value=128.0)
dw, dh = (iw - nw) // 2, (ih-nh) // 2
image_paded[dh:nh+dh, dw:nw+dw, :] = image_resized
image_paded = image_paded / 255.
if gt_boxes is None:
return image_paded
else:
gt_boxes[:, [0, 2]] = gt_boxes[:, [0, 2]] * scale + dw
gt_boxes[:, [1, 3]] = gt_boxes[:, [1, 3]] * scale + dh
return image_paded, gt_boxes
def draw_bbox(image, bboxes, classes=read_class_names(cfg.YOLO.CLASSES), show_label=True):
"""
bboxes: [x_min, y_min, x_max, y_max, probability, cls_id] format coordinates.
"""
num_classes = len(classes)
image_h, image_w, _ = image.shape
hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
random.seed(0)
random.shuffle(colors)
random.seed(None)
for i, bbox in enumerate(bboxes):
coor = np.array(bbox[:4], dtype=np.int32)
fontScale = 0.5
score = bbox[4]
class_ind = int(bbox[5])
bbox_color = colors[class_ind]
bbox_thick = int(0.6 * (image_h + image_w) / 600)
c1, c2 = (coor[0], coor[1]), (coor[2], coor[3])
cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
if show_label:
bbox_mess = '%s: %.2f' % (classes[class_ind], score)
t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick//2)[0]
cv2.rectangle(image, c1, (c1[0] + t_size[0], c1[1] - t_size[1] - 3), bbox_color, -1) # filled
cv2.putText(image, bbox_mess, (c1[0], c1[1]-2), cv2.FONT_HERSHEY_SIMPLEX,
fontScale, (0, 0, 0), bbox_thick//2, lineType=cv2.LINE_AA)
return image
def bboxes_iou(boxes1, boxes2):
boxes1 = np.array(boxes1)
boxes2 = np.array(boxes2)
boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])
boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])
left_up = np.maximum(boxes1[..., :2], boxes2[..., :2])
right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:])
inter_section = np.maximum(right_down - left_up, 0.0)
inter_area = inter_section[..., 0] * inter_section[..., 1]
union_area = boxes1_area + boxes2_area - inter_area
ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps)
return ious
def read_pb_return_tensors(graph, pb_file, return_elements):
with tf.gfile.FastGFile(pb_file, 'rb') as f:
frozen_graph_def = tf.GraphDef()
frozen_graph_def.ParseFromString(f.read())
with graph.as_default():
return_elements = tf.import_graph_def(frozen_graph_def,
return_elements=return_elements)
return return_elements
def nms(bboxes, iou_threshold, sigma=0.3, method='nms'):
"""
:param bboxes: (xmin, ymin, xmax, ymax, score, class)
Note: soft-nms, https://arxiv.org/pdf/1704.04503.pdf
https://github.com/bharatsingh430/soft-nms
"""
classes_in_img = list(set(bboxes[:, 5]))
best_bboxes = []
for cls in classes_in_img:
cls_mask = (bboxes[:, 5] == cls)
cls_bboxes = bboxes[cls_mask]
while len(cls_bboxes) > 0:
max_ind = np.argmax(cls_bboxes[:, 4])
best_bbox = cls_bboxes[max_ind]
best_bboxes.append(best_bbox)
cls_bboxes = np.concatenate([cls_bboxes[: max_ind], cls_bboxes[max_ind + 1:]])
iou = bboxes_iou(best_bbox[np.newaxis, :4], cls_bboxes[:, :4])
weight = np.ones((len(iou),), dtype=np.float32)
assert method in ['nms', 'soft-nms']
if method == 'nms':
iou_mask = iou > iou_threshold
weight[iou_mask] = 0.0
if method == 'soft-nms':
weight = np.exp(-(1.0 * iou ** 2 / sigma))
cls_bboxes[:, 4] = cls_bboxes[:, 4] * weight
score_mask = cls_bboxes[:, 4] > 0.
cls_bboxes = cls_bboxes[score_mask]
return best_bboxes
def postprocess_boxes(pred_bbox, org_img_shape, input_size, score_threshold):
valid_scale=[0, np.inf]
pred_bbox = np.array(pred_bbox)
pred_xywh = pred_bbox[:, 0:4]
pred_conf = pred_bbox[:, 4]
pred_prob = pred_bbox[:, 5:]
# # (1) (x, y, w, h) --> (xmin, ymin, xmax, ymax)
pred_coor = np.concatenate([pred_xywh[:, :2] - pred_xywh[:, 2:] * 0.5,
pred_xywh[:, :2] + pred_xywh[:, 2:] * 0.5], axis=-1)
# # (2) (xmin, ymin, xmax, ymax) -> (xmin_org, ymin_org, xmax_org, ymax_org)
org_h, org_w = org_img_shape
resize_ratio = min(input_size / org_w, input_size / org_h)
dw = (input_size - resize_ratio * org_w) / 2
dh = (input_size - resize_ratio * org_h) / 2
pred_coor[:, 0::2] = 1.0 * (pred_coor[:, 0::2] - dw) / resize_ratio
pred_coor[:, 1::2] = 1.0 * (pred_coor[:, 1::2] - dh) / resize_ratio
# # (3) clip some boxes those are out of range
pred_coor = np.concatenate([np.maximum(pred_coor[:, :2], [0, 0]),
np.minimum(pred_coor[:, 2:], [org_w - 1, org_h - 1])], axis=-1)
invalid_mask = np.logical_or((pred_coor[:, 0] > pred_coor[:, 2]), (pred_coor[:, 1] > pred_coor[:, 3]))
pred_coor[invalid_mask] = 0
# # (4) discard some invalid boxes
bboxes_scale = np.sqrt(np.multiply.reduce(pred_coor[:, 2:4] - pred_coor[:, 0:2], axis=-1))
scale_mask = np.logical_and((valid_scale[0] < bboxes_scale), (bboxes_scale < valid_scale[1]))
# # (5) discard some boxes with low scores
classes = np.argmax(pred_prob, axis=-1)
scores = pred_conf * pred_prob[np.arange(len(pred_coor)), classes]
score_mask = scores > score_threshold
mask = np.logical_and(scale_mask, score_mask)
coors, scores, classes = pred_coor[mask], scores[mask], classes[mask]
return np.concatenate([coors, scores[:, np.newaxis], classes[:, np.newaxis]], axis=-1)