python︱matplotlib使⽤（读⼊、显⽰、写出、opencv混⽤、格式转换...）opencv和matplotlib是⽐较常⽤的图像分析模块。在Ipython⾥⾯，opencv不能直接显⽰出来，所以有些时候会借助matplotlib来显⽰。%matplotlib inline
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1、matplotlib的读⼊与显⽰
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
img = Image.open('lena.png') # 读取的图像显⽰的<matplotlib.image.AxesImage object at 0x7f9f0c60f7f0>
img.show()
img.format
region = anspose(Image.ROTATE_180) #翻转
out = size((128, 128)) # 改变⼤⼩
out1 = ate(45) #旋转
plt.imshow(img) # 显⽰
mean=np.array([104., 117., 124.]) #均值
np.shape(img)
img1 -= mean
plt.imshow(img1)
Image.open之后，是以<matplotlib.image.AxesImage object格式保存。
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2、Image.open格式<—>矩阵
image.open转矩阵
img = Image.open('lena.png')
img1 = np.array(img)
矩阵转 image.open
img = Image.open('lena.png')
img1 = np.array(img)
Image.fromarray(img1 )
3、字节bytes<—>image.open格式以及矩阵格式
有的图⽚读⼊⽅式以.read()，读⼊之后为Bytes类型。
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
ad()
这是要转换为图像格式以及array格式该怎么操作（BytesIO字节读⼊函数）：
# 转image.open格式——常规
from PIL import Image, ImageDraw, ImageFont
from io import BytesIO
import numpy as np
Image.open(BytesIO(get_file_content(pic_path)))
# 转成array格式——常规
np.array(Image.open(BytesIO(get_file_content(pic_path))))
BytesIO返回的格式为：<_io.BytesIO at 0x137c4f6f68>
3.1 从url读⼊并保存
⼀般情况下请求url的图像内容，使⽤的是：
from skimage import io
io.imread(url)
但是因为反扒策略，会出现报错：
RemoteDisconnected: Remote end closed connection without response
那么可以就可以使⽤：
quest
headers = {'User-Agent': 'User-Agent:Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 S afari/537.36'}
req = request.Request(img_l, headers=headers)
pic_infos = request.urlopen(req).read()  # bytes格式读⼊
# 两种保存
misc.imsave(savepath, np.array(Image.open(BytesIO(pic_infos)))  )
Image.open(BytesIO(pic_infos)).save(savepath)
4 base64编码
图⽚路径 -> base64编码
def get_file_content_base64(filePath):
#with open(filePath, 'rb') as fp:
#    ad()
with open(filePath,"rb") as f:
# b64encode是编码，b64decode是解码
base64_data = base64.ad())
return base64_data.decode('utf-8')
图⽚解码-> 显⽰
Image.open(BytesIO(base64.b64decode(body_result['image'])))
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5、PIL格式保存
img = Image.open('lena.png')
img .save(savepath)
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6、在ipython中显⽰图⽚
im=Image.open('/home/Picture/test.jpg')
im.show()
有些时候，show()出来，还没有出来，这时候需要加上：
%matplotlib inline
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7、图像裁剪、旋转、改变
im=Image.open('/home/Picture/test.jpg')
box=(100,100,500,500)
# box=(x,y,x+w,y+h)
p(box) #此时，region是⼀个新的图像对象。
img = Image.open('lena.png') # 读取的图像显⽰的
region = anspose(Image.ROTATE_180) #翻转
out = size((128, 128)) # 改变⼤⼩
out1 = ate(45) #旋转
plt.imshow(img) # 显⽰
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8、opencv打开的图像⽤plt显⽰与保存
显⽰可以直接imshow
import matplotlib.pyplot as plt
import cv2
img = cv2.imread('01.jpg',cv2.IMREAD_COLOR)
%matplotlib inline
plt.subplot(111)
plt.imshow(img)
常规plt的保存⽅式为：
plt.savefig("test.png")
但是该⽅式，保存下来的结果带坐标系的内容，所以仅供观赏。
除了⽤cv2.imwrite保存cv2读的内容，保存可以⽤misc （不太对） ：
cv2.imwrite('img.jpg',img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
from scipy import misc
# numpy直接保存出来
misc.imsave('img.jpg', img)
当然会出现通道错误，⽐如（左图是misc保存的，右图是cv2.imwrite保存的）：
延伸⼀：更精致的画框（带中⽂）
code起初来源于项⽬：
主要有两个函数:get_colors_for_classes(num_classes)
draw_boxes(image, boxes, box_classes, class_names, scores=None,fnt ="" )。
其中：get_colors_for_classes(num_classes)，输⼊个数，就会返回相应的颜⾊，RGB值：
get_colors_for_classes(2)
>>> [(0, 255, 255), (255, 0, 0)]
那么draw_boxes函数各个参数的意思为：
image：矩阵(width, height, 3)
rectangle函数opencvboxes:np.array，(y_min, x_min, y_max, x_max)，⽐如：array([[120, 516, 221, 714], [306, 753, 363, 847], [148, 14, 222, 78]])
box_classes = [1,2,6]，这⾥是下⾯的class_names索引，代表本次标注的内容；
class_names：[‘a’,‘b’,‘c’,‘d’,‘e’,‘f’]，这⾥的class_names指的是全部的标签名称，不代表本次，代表全部；
scores=None：或者为np.array(['0.1','0.2','0.3'])
fnt =“” ，这个为字体，⼀般要中⽂输⼊的话，需要指定中⽂的字体。
import colorsys
import random
import numpy as np
from PIL import Image, ImageDraw, ImageFont
def get_colors_for_classes(num_classes):
"""Return list of random colors for number of classes given."""
# Use previously generated colors if num_classes is the same.
if (hasattr(get_colors_for_classes, "colors") and
len(get_colors_lors) == num_classes):
return get_colors_lors
hsv_tuples = [(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(10101)  # Fixed seed for consistent colors across runs.
random.shuffle(colors)  # Shuffle colors to decorrelate adjacent classes.
random.seed(None)  # Reset seed to default.
get_colors_lors = colors  # Save colors for future calls.
return colors
def draw_boxes(image, boxes, box_classes, class_names, scores=None,fnt ="" ):
"""Draw bounding boxes on image.
Draw bounding boxes with class name and optional box score on image.
Args:
image: An `array` of shape (width, height, 3) with values in [0, 1].
boxes: An `array` of shape (num_boxes, 4) containing box corners as
(y_min, x_min, y_max, x_max).
box_classes: A `list` of indicies into `class_names`.
class_names: A `list` of `string` class names.
`scores`: A `list` of scores for each box.
Returns:
A copy of `image` modified with given bounding boxes.
"""
#image = Image.fromarray(np.floor(image * 255 + 0.5).astype('uint8'))
image = Image.fromarray(image)
font = uetype(
font=fnt,
size=np.floor(3e-2 * image.size[1] + 0.5).astype('int32'))
thickness = (image.size[0] + image.size[1]) // 300
colors = get_colors_for_classes(len(class_names))
for i, c in list(enumerate(box_classes)):
box_class = class_names[c]
box = boxes[i]
if isinstance(scores, np.ndarray):
score = scores[i]
label = '{} {:.2f}'.format(box_class, score)
label = '{} {:.2f}'.format(box_class, score)
else:
label = '{}'.format(box_class)
draw = ImageDraw.Draw(image)
label_size = size(label, font)
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))        right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
print(label, (left, top), (right, bottom))
if top - label_size[1] >= 0:
text_origin = np.array([left, top - label_size[1]])
else:
text_origin = np.array([left, top + 1])
# My kingdom for a good redistributable image drawing library.
for i in range(thickness):
[left + i, top + i, right - i, bottom - i], outline=colors[c])
[tuple(text_origin), tuple(text_origin + label_size)],
fill=colors[c])
<(text_origin, label, fill=(0, 0, 0), font=font)
del draw
return np.array(image)

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