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| import os
import xml.etree.ElementTree as ET
import numpy as np
import cv2
import pickle
import copy
import yolo.config as cfg
import matplotlib.pyplot as plt
np.random.seed(1234)
classes = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa',
'train', 'tvmonitor']
class pascal_voc(object):
def __init__(self, phase, rebuild=False):
self.devkil_path = os.path.join(cfg.PASCAL_PATH, 'VOCdevkit')
self.data_path = os.path.join(self.devkil_path, 'VOC2007')
self.cache_path = cfg.CACHE_PATH
self.batch_size = cfg.BATCH_SIZE
self.image_size = cfg.IMAGE_SIZE
self.cell_size = cfg.CELL_SIZE
self.classes = cfg.CLASSES
self.class_to_ind = dict(zip(self.classes, range(len(self.classes))))
self.flipped = False
self.phase = phase
self.rebuild = rebuild
self.cursor = 0
self.epoch = 1
self.gt_labels = None
self.prepare()
def get(self):
images = np.zeros(
(self.batch_size, self.image_size, self.image_size, 3))
labels = np.zeros(
(self.batch_size, self.cell_size, self.cell_size, 25))
count = 0
while count < self.batch_size:
imname = self.gt_labels[self.cursor]['imname']
flipped = self.gt_labels[self.cursor]['flipped']
images[count, :, :, :] = self.image_read(imname, flipped)
labels[count, :, :, :] = self.gt_labels[self.cursor]['label']
count += 1
self.cursor += 1
if self.cursor >= len(self.gt_labels):
np.random.shuffle(self.gt_labels)
self.cursor = 0
self.epoch += 1
return images, labels
def image_read(self, imname, flipped=False):
image = cv2.imread(imname)
image = cv2.resize(image, (self.image_size, self.image_size))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
image = (image / 255.0) * 2.0 - 1.0
if flipped:
image = image[:, ::-1, :]
return image
def prepare(self):
gt_labels = self.load_labels()
if self.flipped:
print('Appending horizontally-flipped training examples ...')
gt_labels_cp = copy.deepcopy(gt_labels)
for idx in range(len(gt_labels_cp)):
gt_labels_cp[idx]['flipped'] = True
gt_labels_cp[idx]['label'] = \
gt_labels_cp[idx]['label'][:, ::-1, :]
for i in range(self.cell_size):
for j in range(self.cell_size):
if gt_labels_cp[idx]['label'][i, j, 0] == 1:
gt_labels_cp[idx]['label'][i, j, 1] = \
self.image_size - 1 - \
gt_labels_cp[idx]['label'][i, j, 1]
gt_labels += gt_labels_cp
np.random.shuffle(gt_labels)
self.gt_labels = gt_labels
return gt_labels
def load_labels(self):
cache_file = os.path.join(
self.cache_path, 'pascal_' + self.phase + '_gt_labels.pkl')
if os.path.isfile(cache_file) and not self.rebuild:
print('Loading gt_labels from: ' + cache_file)
with open(cache_file, 'rb') as f:
gt_labels = pickle.load(f)
return gt_labels
print('Processing gt_labels from: ' + self.data_path)
if not os.path.exists(self.cache_path):
os.makedirs(self.cache_path)
if self.phase == 'train':
txtname = os.path.join(
self.data_path, 'ImageSets', 'Main', 'trainval.txt')
else:
txtname = os.path.join(
self.data_path, 'ImageSets', 'Main', 'test.txt')
with open(txtname, 'r') as f:
self.image_index = [x.strip() for x in f.readlines()]
gt_labels = []
for index in self.image_index:
label, num = self.load_pascal_annotation(index)
if num == 0:
continue
imname = os.path.join(self.data_path, 'JPEGImages', index + '.jpg')
gt_labels.append({'imname': imname,
'label': label,
'flipped': False})
print('Saving gt_labels to: ' + cache_file)
with open(cache_file, 'wb') as f:
pickle.dump(gt_labels, f)
return gt_labels
def load_pascal_annotation(self, index):
"""
Load image and bounding boxes info from XML file in the PASCAL VOC
format. 002939
"""
imname = os.path.join(self.data_path, 'JPEGImages', index + '.jpg')
im = cv2.imread(imname)
h_ratio = 1.0 * self.image_size / im.shape[0]
w_ratio = 1.0 * self.image_size / im.shape[1]
label = np.zeros((self.cell_size, self.cell_size, 25))
filename = os.path.join(self.data_path, 'Annotations', index + '.xml')
tree = ET.parse(filename)
objs = tree.findall('object')
for obj in objs:
bbox = obj.find('bndbox')
x1 = max(min((float(bbox.find('xmin').text) - 1) * w_ratio, self.image_size - 1), 0)
y1 = max(min((float(bbox.find('ymin').text) - 1) * h_ratio, self.image_size - 1), 0)
x2 = max(min((float(bbox.find('xmax').text) - 1) * w_ratio, self.image_size - 1), 0)
y2 = max(min((float(bbox.find('ymax').text) - 1) * h_ratio, self.image_size - 1), 0)
cls_ind = self.class_to_ind[obj.find('name').text.lower().strip()]
boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]
x_ind = int(boxes[0] * self.cell_size / self.image_size)
y_ind = int(boxes[1] * self.cell_size / self.image_size)
if label[y_ind, x_ind, 0] == 1:
continue
label[y_ind, x_ind, 0] = 1
label[y_ind, x_ind, 1:5] = boxes
label[y_ind, x_ind, 5 + cls_ind] = 1
return label, len(objs)
"""
3 , 4 = [0. 0. 0. 0. 0.]
3 , 5 = [ 1. 325.248 229.6 111.104 228.48 ]
class_one_hot = [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
class_index = 8
class_name = chair
3 , 6 = [0. 0. 0. 0. 0.]
4 , 0 = [0. 0. 0. 0. 0.]
4 , 1 = [0. 0. 0. 0. 0.]
4 , 2 = [ 1. 132.16 288.4 172.928 316.96 ]
class_one_hot = [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
class_index = 8
class_name = chair
4 , 3 = [0. 0. 0. 0. 0.]
data['label'].shape # 7,7,25 (confidence+ (x,y,w,h) + 20-classes)
confidence: 1 if gt_box center falls in this grid, otherwise 0
box(x,y,w,h): gt_box center x,y,w,h; otherwize [0,0,0,0]
class: 20-one-hot-vector if gt_box; othersize [0]*20
how flip works: flip x dim
(1) flip grid x-dim: data['label'] = data['label'][:, ::-1, :]
y-grid = y-grid [0,1,2,3,4,5,6]
x-grid flip [0,1,2,3,4,5,6] ===>[6,5,4,3,2,1,0]
(2) flip data['label']
confidence = confidence
cx: flip cx = 417-cx: data['label'][i, j, 1] = 448 - 1 - data['label'][i, j, 1]
cy = cy
w = w
h = h
class = class
"""
def print_data(data):
for y in range(7):
for x in range(7):
print(y, ",", x, "= ", data['label'][y, x, :5])
if data['label'][y, x, 0] > 0:
class_one_hot = data['label'][y, x, 5:]
class_index = np.argmax(class_one_hot)
print(" class_one_hot = ", class_one_hot)
print(" class_index = ", class_index)
print(" class_name = ", classes[class_index])
def flip_data(data):
data['flipped'] = True
data['label'] = data['label'][:, ::-1, :]
for y in range(7):
for x in range(7):
if data['label'][y, x, 0] == 1:
data['label'][y, x, 1] = 448 - 1 - data['label'][y, x, 1]
def show_image(filename):
image = cv2.imread(filename)
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.axis("off")
plt.imshow(rgb_image)
plt.show()
print("========================PASCAL=================================")
pascal = pascal_voc('train')
print(pascal.class_to_ind)
print(len(pascal.gt_labels))
data = pascal.gt_labels[0]
print(data.keys())
print(data['imname'])
print(data['label'].shape)
print(classes)
show_image(data['imname'])
print("========================DATA=================================")
print_data(data)
print("=========================FLIPPED================================")
flipped = copy.deepcopy(data)
flip_data(flipped)
print_data(flipped)
|
