# -*- coding:utf-8 -*-
# author: gfjiangly
# time: 2019/5/6 18:55
# e-mail: jgf0719@foxmail.com
# software: PyCharm
import os
import os.path as osp
import sys
import numpy as np
import cv2.cv2 as cv
from PIL import Image, ImageDraw
import copy
import matplotlib
from matplotlib import pyplot as plt
from matplotlib import font_manager as fm
from matplotlib.pyplot import MultipleLocator
import cvtools
from cv2 import IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_UNCHANGED
imread_flags = {
'color': IMREAD_COLOR,
'grayscale': IMREAD_GRAYSCALE,
'unchanged': IMREAD_UNCHANGED
}
# 使用PIL lazy方式读图像，防止读大图像死机; 支持中文路径
[文档]def imread(img_or_path, flag='color'):
"""Read an image.
Args:
img_or_path (ndarray or str): Either a numpy array or image path.
If it is a numpy array (loaded image), then it will be returned
as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale` and `unchanged`.
Returns:
ndarray: Loaded image array.
"""
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif isinstance(img_or_path, str):
flag = imread_flags[flag] if isinstance(flag, str) else flag
cvtools.check_file_exist(img_or_path, 'img file does not exist: {}'.format(img_or_path))
try:
"PIL: Open an image file, without loading the raster data"
img_pil = Image.open(img_or_path)
# im = cv2.imdecode(np.fromfile(image_name, dtype=np.uint8), cv2.IMREAD_COLOR)
except (FileNotFoundError, Image.DecompressionBombError) as e:
print(e)
return None
img_opencv = cv.imdecode(np.fromfile(img_or_path, dtype=np.uint8), flag)
if img_opencv is None:
img_opencv = np.array(img_pil)
if len(img_opencv.shape) == 3:
img_opencv = img_opencv[:, :, -1]
return img_opencv
else:
raise TypeError('"img" must be a numpy array or a filename')
[文档]def imwrite(img, file_path, params=None, auto_mkdir=True):
"""Write image to file
Args:
img (ndarray): Image array to be written.
file_path (str): Image file path.
params (None or list): Same as opencv's :func:`imwrite` interface.
auto_mkdir (bool): If the parent folder of `file_path` does not exist,
whether to create it automatically.
Returns:
bool: Successful or not.
"""
if not isinstance(img, np.ndarray):
raise TypeError('"img" must be a numpy array!')
if auto_mkdir:
cvtools.makedirs(file_path)
# return cv.imwrite(file_path, img, params)
# support path included chinese
return cv.imencode(osp.splitext(file_path)[-1], img, params)[1].tofile(file_path)
# detect_line: out_boxes, out_scores, out_classes
[文档]def save_rect_image(image_path, detect_line, th_h=0.9, th_l=0.5):
image = Image.open(image_path)
path_h = os.getcwd()+'/results/crop/' + str(th_h) + '/'
path_l = os.getcwd()+'/results/crop/' + str(th_l) + '/'
out_boxes, out_scores, out_classes = detect_line
if not os.path.exists(path_h):
os.makedirs(path_h, mode=0o777)
if not os.path.exists(path_l):
os.makedirs(path_l, mode=0o777)
_, file_name = os.path.split(image_path)
filename, _ = os.path.splitext(file_name)
for i, c in reversed(list(enumerate(out_classes))):
box = out_boxes[i]
top, left, bottom, right = box
img = image.crop((left, top, right, bottom))
score = out_scores[i]
if score > th_h:
img.save(path_h+'_'.join([filename, str(score), str(i)])+'.jpg')
elif score > th_l:
img.save(path_l+'_'.join([filename, str(score), str(i)])+'.jpg')
# 批量绘制矩形框，src为txt标签文件,dst为图片输出目录
[文档]def draw_rect_test_labels(src, dst, first=sys.maxsize):
colour = ['blue', 'red', 'green', 'white']
if not os.path.exists(dst):
os.mkdir(dst)
with open(src, 'r') as fp:
for count, line in enumerate(fp.readlines()):
if count < first:
line = line.strip().split()
im = Image.open(line[0])
draw = ImageDraw.Draw(im)
boxes = line[1:]
for box in boxes:
bbox_coordinates_str = box.split(',')
coordinates = [float(i) for i in bbox_coordinates_str][0:4]
draw.rectangle(coordinates, None, colour[int(bbox_coordinates_str[4])])
img_name = line[0].replace('\\', '/').split('/')[-1]
im.save(dst+img_name)
[文档]def draw_boxes_texts(img,
boxes,
texts=None,
colors=None,
line_width=1,
draw_start=False,
box_format='x1y1x2y2'):
"""Draw bboxes on an image.
Args:
img (str or ndarray): The image to be displayed.
boxes (list or ndarray): A list of ndarray of shape (k, 4).
texts (list): A list of shape (k).
colors (list[tuple or Color]): A list of colors.
line_width (int): Thickness of lines.
draw_start (bool): Draw a dot at the first vertex of the box.
box_format (str): x1y1x2y2(default), x1y1wh, xywh, xywha, polygon
"""
assert box_format in ('x1y1x2y2', 'x1y1wh', 'xywh', 'xywha',
'polygon'), 'not supported box format!'
img = imread(img)
if len(boxes) == 0:
return img
boxes = copy.deepcopy(boxes)
# convert bbox type to int
if not isinstance(boxes, np.ndarray):
if box_format != 'polygon':
boxes = np.array(boxes)
if box_format != 'xywha':
boxes = boxes.astype(np.int)
if len(boxes.shape) == 1:
boxes = [boxes]
else:
boxes = [list(map(int, box)) for box in boxes]
else:
boxes = boxes.astype(np.int)
if texts is not None and not isinstance(texts, (list, np.ndarray)):
texts = [texts]
if isinstance(img, Image.Image):
img = cv.cvtColor(np.asarray(img), cv.COLOR_RGB2BGR)
if not isinstance(img, np.ndarray):
return
if colors == 'random':
colors = np.random.randint(0, 255, size=(len(boxes), 3))
colors = [tuple(map(int, color)) for color in colors]
text_color = (0, 255, 255)
thickness = line_width
font = cv.FONT_HERSHEY_SIMPLEX
for idx, box in enumerate(boxes):
# default color: red, BGR order
box_color = (0, 0, 255) if colors is None else colors[idx]
if box_format == 'x1y1x2y2':
cv.rectangle(img, tuple(box[0:2]),
tuple(box[2:4]), box_color, thickness)
elif box_format == 'x1y1wh':
box[0:4] = cvtools.x1y1wh_to_x1y1x2y2(list(box[0:4]))
cv.rectangle(img, tuple(box[0:2]),
tuple(box[2:4]), box_color, thickness)
elif box_format == 'xywh':
box[0:4] = cvtools.xywh_to_x1y1x2y2(list(box[0:4]))
cv.rectangle(img, tuple(box[0:2]),
tuple(box[2:4]), box_color, thickness)
elif box_format == 'xywha':
rrect = tuple(box[:2]), tuple(box[2:4]), box[4]
box = cv.boxPoints(rrect).astype(np.int)
# box = np.int0(box)
cv.drawContours(img, [box], 0, box_color, thickness)
box = box.reshape((-1,))
elif box_format == 'polygon':
# for i in np.arange(2, len(box), 2):
# cv.line(img, tuple(box[i-2:i]),
# tuple(box[i:i+2]), box_color, thickness)
# cv.line(img, tuple(box[-2:]),
# tuple(box[:2]), box_color, thickness)
# 如果img内存非连续，cv的所有绘制都会失效
cv.polylines(img, np.int32([np.array(box).reshape(-1, 2)]), 1, box_color, thickness)
# cv.line(img, tuple(box[:2]), tuple(box[2:4]), box_color, thickness)
# cv.line(img, tuple(box[2:4]), tuple(box[4:6]), box_color, thickness)
# cv.line(img, tuple(box[4:6]), tuple(box[6:8]), box_color, thickness)
# cv.line(img, tuple(box[6:]), tuple(box[:2]), box_color, thickness)
if draw_start:
cv.circle(img, tuple(box[:2]),
radius=5, color=text_color, thickness=-1)
if texts is not None:
cv.putText(img, texts[idx],
(box[0]+2, box[1]-2), font, 0.5, text_color, 1)
return img
[文档]def draw_class_distribution(y, save_name='class_distribution.png'):
"""绘制饼图,其中y是标签列表
"""
# 设置matplotlib正常显示中文和负号
matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文
matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号
from collections import Counter
target_stats = Counter(y)
labels = list(target_stats.keys())
sizes = list(target_stats.values())
explode = tuple([0.1] * len(target_stats))
fig, axes = plt.subplots(figsize=(10, 5), ncols=2)
ax1, ax2 = axes.ravel()
patches, texts, autotexts = ax1.pie(
sizes, explode=explode, labels=labels, autopct='%1.0f%%',
shadow=False, startangle=170)
ax1.axis('equal')
# 重新设置字体大小
proptease = fm.FontProperties()
proptease.set_size('xx-small')
# font size include: ‘xx-small’,x-small’,'small’,'medium’,
# ‘large’,‘x-large’,‘xx-large’ or number, e.g. '12'
plt.setp(autotexts, fontproperties=proptease)
plt.setp(texts, fontproperties=proptease)
ax1.set_title('Class Distribution ', loc='center')
# ax2 只显示图例（legend）
ax2.axis('off')
ax2.legend(patches, labels, loc='center left', prop={'size': 10})
plt.tight_layout()
plt.savefig(save_name, dpi=300)
[文档]def draw_hist(data, bins=10,
x_label="区间", y_label="频数/频率",
title="频数/频率分布直方图",
show=True,
save_name='hist.png', density=True):
"""
绘制直方图
data: 必选参数，绘图数据
bins: 直方图的长条形数目，可选项，默认为10
"""
# 设置matplotlib正常显示中文和负号
matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 用黑体显示中文
matplotlib.rcParams['axes.unicode_minus'] = False # 正常显示负号
# normed:是否将得到的直方图向量归一化，可选项，默认为0，代表不归一化，显示频数。
# normed=1，表示归一化，显示频率。
# facecolor:长条形的颜色
# edgecolor:长条形边框的颜色
# alpha:透明度
n, bins, patches = plt.hist(data, bins=bins, density=density,
facecolor="blue", edgecolor='None')
# 显示横轴标签
plt.xlabel(x_label)
# 显示纵轴标签
plt.ylabel(y_label)
# 显示图标题
plt.title(title)
# ax为两条坐标轴的实例
# ax = plt.gca()
# x_major_locator = MultipleLocator(0.1)
# ax.xaxis.set_major_locator(x_major_locator)
# plt.xlim(-1., 1.)
if show:
plt.show()
plt.savefig(save_name, dpi=300)