def show_signature(img_path, fignum=1):
print('Showing signature: ' + img_path)
img = plt.imread(join(imgdir, img_path))
sig_img = sliding_window_signature(img)
fig = plt.figure(fignum)
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.imshow(img)
ax2.imshow(sig_img)
fig.show()
fig.canvas.draw()
def show_signature(img_path, fignum=1):
print('Showing signature: '+img_path)
img = plt.imread(join(imgdir, img_path))
sig_img = sliding_window_signature(img)
fig = plt.figure(fignum)
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
ax1.imshow(img)
ax2.imshow(sig_img)
fig.show()
fig.canvas.draw()
def prepare_ShanghaiTech_dataset(root, part, dm_generator, resetFlag=False):
root = os.path.join(root, "ShanghaiTech")
paths_dict = dict()
print('\t #Preparing Dataset : ShanghaiTech part ', part, ' :')
# generate the ShanghaiA's ground truth
if not part == "A" and not part == "B":
raise Exception("Invalide parts passed for shanghai ")
train_path = os.path.join(root, 'part_' + part, 'train_data')
test_path = os.path.join(root, 'part_' + part, 'test_data')
# save both train and test paths
paths_dict["images"] = os.path.join(train_path, 'images')
paths_dict["ground-truth"] = os.path.join(train_path, 'ground-truth')
path_sets = [paths_dict["images"], paths_dict["ground-truth"]]
img_paths = []
# Grab all .jpg images paths
for path in path_sets:
for img_path in glob.glob(os.path.join(path, '*.jpg')):
img_paths.append(img_path)
# Generate density map for each image
for img_path in img_paths:
if os.path.exists(
img_path.replace('.jpg', '.npy').replace(
'images', 'ground-truth')) and not resetFlag:
#print("\t Already exists.")
continue
print('\t\t Generating Density map for : ',
os.path.basename(img_path),
" :",
end=' ')
# load matrix containing ground truth infos
mat = io.loadmat(
img_path.replace('.jpg', '.mat').replace('images',
'ground-truth').replace(
'IMG_', 'GT_IMG_'))
img = plt.imread(img_path) #768行*1024列
density_map = np.zeros((img.shape[0], img.shape[1]))
points = mat["image_info"][0, 0][0, 0][0] #1546person*2(col,row)
# Generate the density map
density_map = dm_generator.generate_densitymap(img, points)
# save density_map on disk
np.save(
img_path.replace('.jpg', '.npy').replace('images', 'ground-truth'),
density_map)
print('\t Done.')
return paths_dict
def crop_images(input_folder, crop_percentage):
im_files = glob.glob(os.path.join(input_folder, '*.png'))
print(im_files)
for imf in im_files:
im = plt.imread(imf)
orig_shape = np.array(im.shape[0:2])
new_shape = np.round(orig_shape * crop_percentage).astype(
'int') #crop to 60% of the image size
offset = np.round(0.5 * (orig_shape - new_shape)).astype('int')
im_out = im[offset[0]:offset[0] + new_shape[0],
offset[1]:offset[1] + new_shape[1]]
plt.imsave(imf, im_out)
def readTrafficSigns(rootpath, data):
'''Reads traffic sign data for German Traffic Sign Recognition Benchmark.
Arguments: path to the traffic sign data, for example './GTSRB/Training'
Returns: list of images, list of corresponding labels'''
images = [] # images
labels = [] # corresponding labels
# loop over all 43 classes
if data == 'Train':
for c in range(0, 43):
prefix = rootpath + '/' + format(
c, '05d') + '/' # subdirectory for class
gtFile = open(prefix + 'GT-' + format(c, '05d') +
'.csv') # annotations file
gtReader = csv.reader(
gtFile, delimiter=';') # csv parser for annotations file
next(gtReader) # skip header
# loop over all images in current annotations file
for row in gtReader:
images.append(
plt.imread(prefix +
row[0])) # the 1th column is the filename
labels.append(row[7]) # the 8th column is the label
gtFile.close()
elif data == 'Test':
prefix = rootpath + '/' # subdirectory for class
gtFile = open(prefix + 'GT-final_test' + '.csv') # annotations file
gtReader = csv.reader(gtFile,
delimiter=';') # csv parser for annotations file
next(gtReader) # skip header
# loop over all images in current annotations file
for row in gtReader:
images.append(plt.imread(prefix +
row[0])) # the 1th column is the filename
labels.append(row[7]) # the 8th column is the label
gtFile.close()
return images, labels
def prepare_dataset(root, dirname, dm_generator, resetFlag=False):
root = os.path.join(root, dirname)
paths_dict = dict()
print('\t #Preparing Dataset : ', dirname)
# save both train and test paths
paths_dict["images"] = os.path.join(root, 'images')
paths_dict["ground-truth"] = os.path.join(root, 'ground-truth')
path_sets = [paths_dict["images"], paths_dict["ground-truth"]]
img_paths = []
# Grab all .jpg images paths
for img_path in glob.glob(os.path.join(paths_dict["images"], '*.jpg')):
img_paths.append(img_path)
# Generate density map for each image
for img_path in img_paths:
if os.path.exists(
img_path.replace('.jpg', '.npy').replace(
'images', 'ground-truth')) and not resetFlag:
#print("\t Already exists.")
continue
print('\t\t Generating Density map for : ', os.path.basename(img_path),
" :")
# load matrix containing ground truth infos
mat = io.loadmat(
img_path.replace('.jpg', '.mat').replace('images', 'ground-truth'))
img = plt.imread(img_path) #768行*1024列
density_map = np.zeros((img.shape[0], img.shape[1]))
# points = mat["image_info"][0,0][0,0][0] #1546person*2(col,row)
key = [el for el in list(mat) if el.lower().endswith('points')][0]
points = [tuple(el) for el in mat[key]] #1546person*2(col,row)
# Generate the density map
density_map = dm_generator.generate_densitymap(img, points)
# save density_map on disk
np.save(
img_path.replace('.jpg', '.npy').replace('images', 'ground-truth'),
density_map)
print('\t Done.')
return paths_dict
elif m == '06':
return int(d)+14
elif m == '07':
return int(d)+34
df["date"] = [t[0:4] + t[5:7] + t[8:10] for t in df["lastupdated"]]
df["time"] = [t[11:13] + t[14:16] for t in df["lastupdated"]]
df["day"] = [date2int(t) for t in df["lastupdated"]] # day 0 is May 17, 2014
df["dayofwk"] = [(t+6)%7 for t in df["day"]] # 0 indexed Sunday
df.head()
# <codecell>
plt.figure(figsize=(10,15))
im = plt.imread('chicago.png')
implot = plt.imshow(im)
x = (df['west'] - df['west'].min())*477/(df['east'].max() - df['west'].min())
y = 798-(df['north'] - df['south'].min())*798/(df['north'].max() - df['south'].min())
s = df['currentspeed'] / df['currentspeed'].max()
plt.scatter(x,y,c=s,linewidth=0,s=1000,alpha=0.1)
#x0 = (df.ix[0]['west'] - df['west'].min())*477/(df['east'].max() - df['west'].min())
#y0 = 798-(df.ix[0]['north'] - df['south'].min())*798/(df['north'].max() - df['south'].min())
#plt.scatter(x0,y0,c='r',s=2000)
#x0 = (df.ix[0]['east'] - df['west'].min())*477/(df['east'].max() - df['west'].min())
#y0 = 798-(df.ix[0]['south'] - df['south'].min())*798/(df['north'].max() - df['south'].min())
#plt.scatter(x0,y0,c='r',s=2000)
plt.xlim(0,477)
plt.ylim(798,0)
'''
import numpy as np
import matplotlib as plt
from submission import *
from helper import *
data = np.load('../data/some_corresp.npz')
intrinsics = np.load('../data/intrinsics.npz')
pts1 = data['pts1']
pts2 = data['pts2']
K1 = intrinsics['K1']
K2 = intrinsics['K2']
im1 = plt.imread('../data/im1.png')
im2 = plt.imread('../data/im2.png')
M = max(im1.shape[0], im1.shape[1])
F = eightpoint(pts1, pts2, M)
E = essentialMatrix(F, K1, K2)
print('F: ')
for rows in F:
print(rows)
print('E: ')
for rows in E:
print(rows)
.format(epoch+1, num_epochs, loss/n_batches))
losses.append(loss/n_batches)
#if epoch == 1:
pic_org = to_img(img.cpu().data)
pic_cropped = to_img_cropped(cropped_img.cpu().data)
#pic_noised = to_img(noised_img.cpu().data)
pic_pred = to_img(output.cpu().data)
res = torch.cat((pic_org,pic_cropped, pic_pred), dim=3)
save_image(res[:8], f'{output_dir}/res_{epoch}.png') # save 8 images
# save the model
torch.save(net.state_dict(), f'{output_dir}/conv_autoencoder_4.pth')
# show performance of autoencoder after some epochs
imgs = [plt.imread(f'{output_dir}/res_4_{i}.png') for i in range(3)]
NUM_ROWS = 1
IMGS_IN_ROW = 1
f, ax = plt.subplots(NUM_ROWS, IMGS_IN_ROW, figsize=(5,10))
for i in range(1):
ax[i].imshow(imgs[i])
ax[i].set_title(f'Results after {i} epoch') #Change if changed to epoch or mod epoch
plt.tight_layout()
plt.show()
#Change for the 3rd version!!
from os import listdir
import matplotlib as plt
for file in listdir("C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter"):
# print(files)
if file.endswith(".jpg"):
print(file)
# str.endswith(suffix)
im = Image.open(
"C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter\\" + file)
print(file[:-3] + "png")
im.save("C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter\\" +
file[:-3] + "png")
im = Image.open(
"C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter\\test2_84.png")
im.size
img = plt.pyplot.imread(
"C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter\\test2_84.png")
img = plt.pyplot.imread(
"C:\\Users\\bdgecyt\\Desktop\\dataset\\Household Shelter\\271_25-1_00_23_41_0_2.png"
)
plt.pyplot.imread('Household Shelter/271_25-1_00_23_41_0_2.png')
image = plt.imread('Household Shelter/271_25-1_00_23_41_0_2.png')
matplotlib.pyplot.imread
'''
if cv2.waitKey()&0xff==ord('q') #这里与oxff运算是为了提取ASCII码，因为不同系统可能不一样 #ord('q')返回113
break #若按下的键的ASCII与q的ASCII码相等，就执行break语句
'''ord()函数是以单个字符为参数，返回其对应的ASCII码；与ord()对应的chr()和unichr()[它与chr()不同
的是返回unicode字符]以0到255整数做参数，返回对应字符。如chr(113)返回'q'。'''
&是按位与运算，and是逻辑运算
cv2.waitKey(5)表示等待5 毫秒，但是只要按了键，就不必等待5毫秒。要和time.sleep(5)分清楚
==============================================================================================
21.对matplotlib、pylab、imread、cv2之间的关系与区别
如果不使用opencv中的cv2模块来读取显示图片时，应该这么做：
import matplotlib as plt
import pylab
img=plt.imread('a.jpg')
plt.imshow(img) #----只这一句话并不会显示图像窗口
pylab.show() #----加上这句话才会显示图像窗口
-------------------------------------------------------
对比一下使用cv2模块：
import cv2
img=cv2.imread('a.jpg')
cv2.imshow('my_window',img)
cv2.waitKey()
cv2.destroyAllWindow()
================================================================================================
22.python中数组的各种属性的用法：
a=np.random.random(4)
type(a) #---输出 <class 'numpy.ndarray'>