def _process_image(filename):
"""Process a image and annotation file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
"""
# Read the image file.
# filename = '../../.jpg'
image_data = tf.gfile.FastGFile(filename, 'rb').read()
import cv2
img=cv2.imread(filename)
shape=img.shape
# # 解码RGB JPEG.
# image = tf.image.decode_jpeg(image_data)
'''读取单张图片的信息'''
imgid = filename.split('.')[0].split('/')[-1]
imgdata = anno_func.load_img(anns, DATADIR, imgid)
imgdata_draw, mask_ellipse, img = anno_func.draw_all(anns, DATADIR, imgid, imgdata)
img_info = img['objects']
label_classes = []
label_text = []
bbox_list = []
ymin = []
xmin = []
ymax = []
xmax = []
for obj in range(len(img_info)):
label_text.append(img_info[obj]['category'].encode())
bbox = img_info[obj]['bbox']
assert len(bbox) == 4
bbox_list.append([bbox['xmin'], bbox['ymin'], bbox['xmax'], bbox['ymax']])
label_classes.append(int(target_classes[img_info[obj]['category']][0]))
return image_data, label_classes, label_text,bbox_list,shape
def get_actual_data_from_xml(img_path):
actual_item = []
img2 = []
img1 = []
try:
img_id = img_path.split('\\')[-1].split('.')[0]
# datadir = os.path.join(dataset_dir, os.path.pardir)
imgdata, img_msg = anno_func.load_img(anns, dataset_dir, img_id)
imgdata = imgdata[400:1424, :, :]
split_list = np.hsplit(imgdata, 2)
split_interval = len(split_list[0])
shape = split_list[0].shape
img_width, img_height, depth = shape
img_info = img_msg['objects']
for info in img_info:
label = info['category'].strip()
label = classes[label][0]
xmin = info['bbox']['xmin']
xmax = info['bbox']['ymin']
ymin = info['bbox']['xmax'] - 400.0
ymax = info['bbox']['ymax'] - 400.0
# 1024.0为设定的剪切图片的大小
if ymin > 1024.0:
break
if ymax > 1024.0:
ymax = 1024.0
if xmin < split_interval and xmax < split_interval:
xmin, xmax = xmin, xmax
img1.append([((xmin + xmax) / 2 / img_width), ((ymin + ymax) / 2 / img_height),
((xmin - xmax) / img_width),
((ymin - ymax) / img_height), label])
elif xmin < split_interval and xmax < 2 * split_interval:
xmin, xmax = xmin, split_interval
img1.append([((xmin + xmax) / 2 / img_width), ((ymin + ymax) / 2 / img_height),
((xmin - xmax) / img_width),
((ymin - ymax) / img_height), label])
elif (xmin >= split_interval and xmin < 2 * split_interval) and (
xmax < 2 * split_interval): # 超越第一张图片的大小
xmin, xmax = xmin - split_interval, xmax - split_interval
img2.append([((xmin + xmax) / 2 / img_width), ((ymin + ymax) / 2 / img_height),
((xmin - xmax) / img_width),
((ymin - ymax) / img_height), label])
elif (xmin >= split_interval and xmin < 2 * split_interval) and (xmax >= 2 * split_interval): # 一倍间距
xmin, xmax = xmin - split_interval, xmax - split_interval
img2.append([((xmin + xmax) / 2 / img_width), ((ymin + ymax) / 2 / img_height),
((xmin - xmax) / img_width),
((ymin - ymax) / img_height), label])
else:
continue
actual_item.extend([img1, img2])
return actual_item
except:
return None
def draw_pic(annos, datadir, imgid):
"""
Draw boundary by annotation, function call of anno_func.py
Library pylab is required
:param annos: annotation of region
:param datadir: image directory
:param imgid: image id
:return: null
"""
imgdata = anno_func.load_img(annos, datadir, imgid)
imgdata_draw = anno_func.draw_all(annos, datadir, imgid, imgdata)
pl.figure(figsize=(20, 20))
pl.imshow(imgdata_draw)
pl.show()
def process_image(dataset_dir, filename):
'''获取每张图片的信息'''
filename_id = filename.split('\\')[-1].split('.')[0]
ANNOTATIONS = os.path.join(dataset_dir, os.path.pardir, 'annotations.json')
ids = open(dataset_dir + 'ids.txt').read().splitlines()
annotations = json.loads(open(ANNOTATIONS).read())
CLASSES = annotations['types']
classes = dict()
for i, name in enumerate(CLASSES):
classes[name] = (i + 1, i + 1)
classes['Background'] = (0, 0)
# filename_id='571'
dataset_dir = os.path.join(
dataset_dir, os.path.pardir) #为了配合annos存储的是'path': 'test/33132.jpg'
image_data = tf.gfile.FastGFile(filename, 'rb').read()
imgdata, img = anno_func.load_img(annotations, dataset_dir, filename_id)
shape = imgdata.shape
img_info = img['objects']
bboxes = []
labels = []
labels_text = []
xmin = []
ymin = []
xmax = []
ymax = []
for info in img_info:
labels_text.append(info['category'].encode('utf-8'))
labels.append(classes[info['category']][0])
xmin.append(info['bbox']['xmin'])
ymin.append(info['bbox']['ymin'])
xmax.append(info['bbox']['xmax'])
ymax.append(info['bbox']['ymax'])
bbox = info['bbox']
bboxes.append([
bbox['xmin'] / shape[0], bbox['ymin'] / shape[1],
bbox['xmax'] / shape[0], bbox['ymax'] / shape[1]
])
# r_e_shape.append(info)
return image_data, shape, labels, labels_text, bboxes
def get_img_info(img):
'''读取单张图片的信息'''
imgid = img.split('.')[0]
imgdata = anno_func.load_img(anns, DATADIR, imgid)
imgdata_draw, mask_ellipse, img = anno_func.draw_all(
anns, DATADIR, imgid, imgdata)
img_info = img['objects']
label_classes = []
label_text = []
bbox_list = []
ymin = []
xmin = []
ymax = []
xmax = []
for obj in range(len(img_info)):
label_text.append(img_info[obj]['category'].encode())
bbox = img_info[obj]['bbox']
assert len(bbox) == 4
bbox_list.append(
[bbox['xmin'], bbox['ymin'], bbox['xmax'], bbox['ymax']])
label_classes.append(int(target_classes[img_info[obj]['category']]))
return bbox_list, label_classes, label_text
import json import pylab as pl import random import numpy as np import cv2 import anno_func datadir = "/home/richardchen123/Documents/data/YOLOv4/data" filedir = datadir + "/annotations.json" ids = open(datadir + "/test/ids.txt").read().splitlines() annos = json.loads(open(filedir).read()) imgid = random.sample(ids, 1)[0] print(imgid) imgdata = anno_func.load_img(annos, datadir, imgid) imgdata_draw = anno_func.draw_all(annos, datadir, imgid, imgdata) pl.figure(figsize=(20, 20)) pl.imshow(imgdata_draw)
def loadInput(self):
self.imgid = random.sample(self.ids, 1)[0]
self.indata = anno_func.load_img(self.annos, self.datadir, self.imgid)
return self.imgid