def Learing(E, I1, K1, K2, Q): # define a function to learn the value of w
#m = [K1, K2, I1]
w = [0, 0, 0]
# a = []
# e = []
apha = 0.00001
#epoch = 1
W = 400
H = 300
ans = np.zeros(H*W)
#y = 0
#while (epoch==1 or epoch<(W*H) and np.abs(w[y]-w[y-1])>0):
for x in range(W*H):
a = w[0]*K1[x] + w[1]*K2[x] + w[2]*I1[x]
e = E[x] - a
w[0] = w[0] + apha * e * K1[x]
w[1] = w[1] + apha * e * K2[x]
w[2] = w[2] + apha * e * I1[x]
#epoch += 1
#y += 1
for x in range(W*H):
ans[x] = (Q[x] - K1[x]*w[0] - K2[x]*w[1]) / w[2]
#print ans
ans = ans.reshape((300, 400))
print ans
Final_img = Image.fromarray(ans)
Image._show(Final_img)
Final_img.save('Final_img.jpg')
return w
def login(username, password):
url = 'http://i.mooc.chaoxing.com/space/index?'
browser.get(url)
browser.find_element_by_id('unameId').send_keys(username)
browser.find_element_by_id('passwordId').send_keys(password)
location = browser.find_element_by_id('numVerCode').location
screenImg = "code.png"
browser.get_screenshot_as_file(screenImg)
# size=browser.find_element_by_id('numVerCode').size
# # #browser.maximize_window()
# # top = location['y']+90
# # bottom = location['y'] + size['height']+95
# # left=location['x']+140
# # right = location['x'] + size['width']+190
#
# #小窗口
# top = location['y']+90
# bottom = location['y'] + size['height']+95
# left=location['x']+90
# right = location['x'] + size['width']+110
# img = Image.open(screenImg).crop((left, top, right, bottom))
# # img = img.convert('RGBA') # 转换模式：L | RGB
# # img = img.convert('L') # 转换模式：L | RGB
# # img = ImageEnhance.Contrast(img) # 增强对比度
# # img = img.enhance(2.0) # 增加饱和度
# img.save(screenImg)
# img.show()
x = Image.open('code.png')
Image._show(x)
code = input('请输入验证码：')
browser.find_element_by_id('numcode').send_keys(code)
browser.find_element_by_css_selector('.zl_btn_right').click()
print('登陆成功')
def run():
last = [[]]
curr = ca_matrix
while True:
if np.all(last == curr):
break
last = np.copy(curr)
for i in range(msize):
for j in range(msize):
judge_cell_state(i, j)
curr = np.copy(ca_matrix)
print(ca_matrix)
print('---------------------')
# 绘制图片
for i in range(msize):
for j in range(msize):
if curr[i][j]:
curr[i][j] = 0
else:
curr[i][j] = 255
image = Image.fromarray(curr)
Image._show(image)
print('over')
def reverse_distort():
m_r, m_g, m_b = create_distortion_maps()
newImage: Image.Image = Image.new('RGB', (aImage.width, aImage.height))
draw: ImageDraw.ImageDraw = ImageDraw.Draw(newImage)
new_pixels = {}
for distorted, undistorted in m_r.items():
pixel = pixels[distorted[1]][distorted[0]]
if undistorted not in new_pixels:
new_pixels[undistorted] = [pixel[0],0,0]
else:
new_pixels[undistorted][0] = pixel[0]
for distorted, undistorted in m_g.items():
pixel = pixels[distorted[1]][distorted[0]]
if undistorted not in new_pixels:
new_pixels[undistorted] = [0, pixel[1], 0]
else:
new_pixels[undistorted][1] = pixel[1]
for distorted, undistorted in m_b.items():
pixel = pixels[distorted[1]][distorted[0]]
if undistorted not in new_pixels:
new_pixels[undistorted] = [0, 0, pixel[2]]
else:
new_pixels[undistorted][2] = pixel[2]
for undistorted, rgb in new_pixels.items():
draw.point((undistorted[0], undistorted[1]), fill=tuple(rgb))
Image._show(newImage)
def make_ticket(fio, from_, to, date):
fio = fio
flight_from = from_
flight_to = to
flight_date = date
filename = r'images/ticket_template.png'
#lesson_path = r'python_base/lesson_013'
path_to_file = os.path.join(os.getcwd(), filename)
picture = Image.open(path_to_file)
draw = ImageDraw.Draw(picture)
font = ImageFont.truetype('arial.ttf', 15)
draw.text((40, 120), fio, fill=ImageColor.colormap['black'], font=font)
draw.text((40, 190),
flight_from,
fill=ImageColor.colormap['black'],
font=font)
draw.text((40, 260),
flight_to,
fill=ImageColor.colormap['black'],
font=font)
draw.text((280, 260),
flight_date,
fill=ImageColor.colormap['black'],
font=font)
Image._show(picture)
pass
def profpic(u_name):
try:
api.searchUsername(u_name)
user = api.LastJson['user']
item = user['hd_profile_pic_versions']
for eachit in item:
print(str(eachit['height']) + " x " + str(eachit['width']))
print(eachit['url'])
print(
str(user['hd_profile_pic_url_info']['height']) + " x " +
str(user['hd_profile_pic_url_info']['width']))
url_ = user['hd_profile_pic_url_info']['url']
print(url_)
Picture_request = requests.get(url_)
home = str(Path.home())
path_i = home + "\\Desktop\\temp.jpg"
if Picture_request.status_code == 200:
with open(path_i, 'wb') as f:
f.write(Picture_request.content)
hd_image = Image.open(path_i)
Image._show(hd_image)
except Exception as e:
print("try again")
print(str(e))
def test():
filename_queue = tf.train.string_input_producer(['Climatic.png']) # list of files to read
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
my_img = tf.image.decode_png(value) # use png or jpg decoder based on your files.
init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
for i in range(1): # length of your filename list
image = my_img.eval(session=sess) # here is your image Tensor :)
print(image.shape)
Image._show(Image.fromarray(np.asarray(image)))
coord.request_stop()
coord.join(threads)
def make_file_pipeline(self, image_files, label_files, batch_size=None, im_width=100, im_height=100, shuffle=True,
sess=None):
if batch_size == None:
batch_size = self.batch_size
# with open(image_files[0], "rb") as f:
# lines = f.readlines()
# print(lines)
# image_files_prod = tf.train.string_input_producer(["S:\\Users\\Nibiru\\Source\\PyCharm-Projects\\Neural-Network-v2\\data\\train\\101_blur.jpg"], shuffle=shuffle, seed=1)
# label_files_prod = tf.train.string_input_producer(["S:\\Users\\Nibiru\\Source\\PyCharm-Projects\\Neural-Network-v2\\data\\train\\101.jpg"], shuffle=shuffle, seed=1)
image_files_prod = tf.train.string_input_producer(image_files, shuffle=shuffle, seed=1)
label_files_prod = tf.train.string_input_producer(label_files, shuffle=shuffle, seed=1)
reader = tf.WholeFileReader()
image_file, image = reader.read(image_files_prod)
label_file, label = reader.read(label_files_prod)
decoded = tf.image.decode_jpeg(image, channels=3)
decoded_label = tf.image.decode_jpeg(label, channels=3)
image = decoded.eval(session=sess) # here is your image Tensor :)
print(image.shape)
Image._show(Image.fromarray(np.asarray(image)))
# image = tf.to_float() / 256.0
# label = tf.to_float()/ 256.0
image = tf.reshape(image, [im_width, im_height, 3])
label = tf.reshape(label, [im_width, im_height, 3])
image_batch, label_batch = tf.train.batch([image, label], batch_size=batch_size, capacity=1000)
return image_batch, label_batch
def ShowImage(self,img,imagename,imagechannel=1):
# if len(img)==0:
# return 1; #1---无图像
#数据是图像
ty=type(img);
if str(ty).__contains__("PIL"):
Image._show(img);
return 1;
else:
#---数据是数组
try:
channel=img.shape;
if len(channel)==2:
#----此时表示需要显示一个二维数组（矩形为图片）
plt.imshow(img,'gray');
plt.axis('off');
plt.title(imagename);
plt.show();
return 0;
elif len(channel)==3:
# ----此时表示需要显示一个二维数组（矩形为图片）
plt.imshow(img);
plt.axis('off');
plt.title(imagename);
plt.show();
return 0;
elif len(channel)==4:
#----此时表示是显示一个图像集合
if channel[0]>100:return 2; #图像过多，则不显示
show_width_num, show_height_num = self.__caculate_factor(channel[0]); # ----获取最优显示布局
plt.figure(imagename);
if imagechannel==1:
for index in range(channel[0]):
image_x=img[index, :, :, 0]; #对于单通的图像就这么显示
# image_x = img[index, :, :, :];
# ---------------in order to show the image ,do some ops
# pinjie = np.zeros([image_x.shape[0], image_x.shape[1], 2]);
# image = np.concatenate((image_x, pinjie), axis=2);
plt.subplot(show_width_num, show_height_num, index + 1);
# plt.title("predict:" + str(predict[index]) + "; actual:" + str(actual[index]));
plt.imshow(image_x,cmap=plt.cm.gray), plt.axis('off'); # 显示图片
# plt.imshow(image_x,cmap='gray'), plt.axis('off'); # 显示图片
plt.show();
else:
for index in range(channel[0]):
plt.subplot(show_width_num, show_height_num, index + 1);
# plt.title("predict:" + str(predict[index]) + "; actual:" + str(actual[index]));
plt.imshow(img), plt.axis('off'); # 显示图片
plt.show();
return 0;
else:
return 3;
except:
return 4;
def PowerLaw(self,gama,c=1):
saida = np.zeros([self.m,self.n])
for i in range(self.m):
for j in range(self.n):
saida[i,j] = int(c*(self.matriz[i,j]**gama))
img = Image.fromarray(saida)
Image._show(img)
def ShowBinayImage(image: np.ndarray):
im = Image.new("RGB", image.shape, 0)
for i in range(len(image)):
for j in range(len(image[i])):
im.putpixel((j, i), (0, 0, 0) if image[i][j] > 0 else
(255, 255, 255))
Image._show(im)
def main():
# Argument Getter
arguments = argparse.ArgumentParser(description="Street View Image Getter for some project")
arguments.add_argument('-k', '--googleKey', required=True, help="Google API Key")
arg = arguments.parse_args()
image_test = get_images_from_gmaps(arg)
Image._show(image_test)
def textToImage():
snap = Snap().getConnection()
userID = localFiles.getLocalUserInfo()[0]
list = snap.getImageFile(userID)[0]
#Displays all snaps found
for element in list:
img = Image.open(BytesIO(base64.b64decode(element)))
Image._show(img)
def plot_image_big(image):
# Ensure the pixel-values are between 0 and 255.
image = np.clip(image, 0.0, 255.0)
# Convert pixels to bytes.
image = image.astype(np.uint8)
# Convert to a PIL-image and display it.
Image._show(PIL.Image.fromarray(image))
def implot(image):
plt.figure(figsize=(8,8))
image = image.numpy()
image = np.transpose(image,(1,2,0))
var = [0.172,0.168,0.195]
mean = [0.718,0.734,0.675]
image= image*var + mean
im = Image.fromarray((image*255).astype(np.uint8))
Image._show(im)
def main():
img = Image.open("OriginalImage.jpg")
imgArray = np.array(img)
#RGB to GrayScale
tempArray = RGBtoGray(imgArray)
Image._show(Image.fromarray(tempArray))
FilterAndShow(tempArray, [0, -1, 1])
FilterAndShow(tempArray, [1, -1, 0])
FilterAndShow(tempArray, [-0.5, 0, 0.5])
def reverse_distort2():
newImage: Image.Image = Image.new('RGB', (aImage.width, aImage.height))
draw: ImageDraw.ImageDraw = ImageDraw.Draw(newImage)
for row in range(height):
for col in range(width):
pixel = get_distorted_pixel(col, row)
#print(pixel)
draw.point((col, row), fill=pixel)
Image._show(newImage)
newImage.save("newimage.png")
def subitracao(mat1, mat2, m, n):
saida = np.zeros([m, n])
print(saida.shape)
for i in range(m):
for j in range(n):
if mat1[i, j] - mat2[i, j] < 0:
saida[i, j] = 0
else:
saida[i, j] = mat1[i, j] - mat2[i, j]
img = Image.fromarray(saida)
Image._show(img)
def getValue(self):
#filePath,resizeFactor
print("enter the file path")
filePath=input("File path")
print("enter the resize factor")
resizeFactor=input("resize factor")
self.filePath=filePath
self.resizeFactor=resizeFactor
print(resizeFactor)
ap=Image(filename='/home/jerald/Pictures/one.jpg')
ap._show()
def soma(mat1, mat2, m, n):
saida = np.zeros([m, n])
print(saida.shape)
for i in range(m):
for j in range(n):
#saida[i,j] = (mat1[i,j]+ mat2[i,j])//2
if mat1[i, j] + mat2[i, j] > 255:
saida[i, j] = 255
else:
saida[i, j] = mat1[i, j] + mat2[i, j]
img = Image.fromarray(saida)
Image._show(img)
def google_map(good_hs, bad_hs):
# show a Google map!
colors = ['red', 'orange', 'yellow', 'green', 'blue', 'purple', 'black']
# if isinstance(location, str):
# geo = google_geo(location)
# lat = geo['lat']
# lng = geo['lng']
# else:
# lat = location[0]
# lng = location[1]
# sort hotspots by number of birds! TODO: do this in find_good_hotspots, also sort p array
good_hs = sorted(good_hs, key=lambda n: n['expected_n'])
if len(good_hs) > 6:
use_hs = good_hs[0:6]
else:
use_hs = good_hs
print(len(use_hs))
# Generate URL with markers
base_url = 'http://maps.googleapis.com/maps/api/staticmap?&size=1000x1000&sensor=false]'
url_param = ''
# markers for good hotspots
url_param = '&markers=size:mid%%7Ccolor:%s' %colors[0]
ind = 0
for x in use_hs:
url_param += '&markers=size:mid%%7Ccolor:%s%%7Clabel:%i' %(colors[ind], ind+1)
url_param += '%%7C%f,%f' %(x['lat'],x['lng'])
ind += 1
url_param += '&markers=size:tiny%%7Ccolor:%s' % colors[-1]
for x in bad_hs[1:20]: #FIXME: make reasonable estimate of size
url_param+='%%7C%f,%f' %(x['lat'],x['lng'])
image_bytes = urllib.urlopen(base_url+url_param).read()
image = Image.open(StringIO(image_bytes)) #StringIO makes file object out of image data
# TODO
# - take in lat,lng of hotspots
# - plot hotspot markers, sorted by # expected new birds.
Image._show(image)
return image
def negative(self):
L = 255
saida = np.zeros([self.m , self.n])
m1, n1 = saida.shape
print('linhas : {} coluna: {}'.format(m1,n1))
for i in range(self.m):
for j in range(self.n):
saida[i,j] = L - self.matriz[i,j]
img = Image.fromarray(saida)
Image._show(img)
def show_image_from_nmlz_data(data, color_mode):
"""
Shows image in system compatible Image Viewer
:param data: Image numpy array
:param color_mode: Image color mode [F, RGB]
:return:
"""
if not verbose: return
if is_rgb_color_mode(color_mode):
ch = [data[0] * 255, data[1] * 255, data[2] * 255]
Image._show(image_from_rgb_channels(ch[0], ch[1], ch[2]))
else:
Image._show(Image.fromarray(data * 255, color_mode))
def reflexao(mat, m, n, eixo):
x = lambda x: x
y = lambda y: y
saida = np.zeros([m, n])
if eixo == 1:
x = lambda x: m - 1 - x
else:
y = lambda y: n - 1 - y
for i in range(m):
for j in range(n):
saida[i, j] = mat[x(i), y(j)]
img = Image.fromarray(saida)
Image._show(img)
def ShowCoords(img_file, coord_file):
IMG_DCT = "photos_used/"
img = Image.open(IMG_DCT + img_file)
draw = ImageDraw.Draw(img)
COORDS_DCT = "coords/"
coord_list = CreateData.LoadCoords(coord_file, COORDS_DCT)
for c in coord_list:
if c[2] == 1:
x = c[0]
y = c[1]
draw.line([(x - 4, y - 4), (x + 4, y + 4)], fill="green", width=2)
draw.line([(x - 4, y + 4), (x + 4, y - 4)], fill="green", width=2)
del draw
Image._show(img)
def compareRow(img1, img2, img3):
results = {}
Image._show(ImageChops.logical_xor(img1.convert("1"), img2.convert("1")))
AB = compareImages(img1, img2)
BC = compareImages(img2, img3)
if 'result' in AB and 'result' in BC:
if AB['result'] and BC['result'] == 'same':
results.update({'result': 'same'})
else:
results.update({'result': 'different'})
return results
def get_captcha(login_url):
login_html = s.get(login_url, headers=headers).content
# print(login_html)
# try:
# captcha_img_url = re.findall(
# r'<img style="CURSOR: pointer" onclick="exchangePic(this)" height="23" title="点击刷新" src="(.*?)" width="58" align="absMiddle" border="0">', login_html)[0]
# print(captcha_img_url)
image = Image.open('Image.png')
captcha_code = pytesseract.image_to_string(image)
Image._show(image)
print(captcha_code)
def _show_seg_label(data_path):
lbl = Image.open(data_path)
lbl_category = FT.to_tensor(lbl) * 255
if lbl_category.shape[0] == 3:
lbl_category = lbl_category[0, :, :] # (300, 300)
lbl_category = lbl_category.data.numpy()
lbl_category = lbl_category.astype('int') # int64
dd_cm = np.array(dd_colormap, dtype='uint8')
lbl_RGB = dd_cm[lbl_category]
lbl_RGB = Image.fromarray(lbl_RGB)
Image._show(lbl_RGB)
def test_reader(recordsFile):
image, label = recordsReader(recordsFile)
with tf.Session() as sess: # 开始一个会话
init_op = tf.initialize_all_variables()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(1):
image, label = sess.run([image, label]) # 在会话中取出image和label
# img = Image.fromarray(example, 'RGB') # 如果img是RGB图像
# img = Image.fromarray(example)
#
# img.save('./' + '_'+'Label_' + str(l) + '.jpg') # 存下图片
Image._show(Image.fromarray(image))
coord.request_stop()
coord.join(threads)
def open_image_as_array(path, open_mode, color_mode):
"""
:param path: Path to image on disc
:param open_mode: Mode to open image
:param color_mode: Image color mode
:return: Return image as numpy array object
"""
image = adapt_img_size(Image.open(path, open_mode).convert(color_mode))
if verbose:
Image._show(image)
if is_rgb_color_mode(color_mode):
image_ch = np.array(get_channels(image))
else:
image_ch = np.array(image)
image.close
return image_ch
def show_image(identifier, images_root = definitions.TRAIN_IMAGES):
'''
shows the image associated with the identifier. This is the image that was rendered
from the structured representation with that identifier.
As there are are 6 permutations for each such structured_representation,
this methods shows the image where the prder of boxes fits their order in the structured representation.
:param identifier: a unique identifier of the form {sentence_id}-{img id}.
for example '150-2'
:param images_root: train/dev/test
'''
im_path = __image_for_id(identifier, images_root)
img = Image.open(im_path)
Image._show(img)
return
def evaluate(model, data_loader, device, file_names):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
for (image, labels, masks) in metric_logger.log_every(data_loader, 100, header):
image = list(img.to(device) for img in image)
targets = list({k: v.to(device, dtype=torch.long) for k,v in t.items()} for t in labels)
torch.cuda.synchronize()
model_time = time.time()
outputs = model(image)
outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
ids = [targets[i]['image_id'].numpy() for i in range(len(targets))]
res = {target["image_id"].item(): output for target, output in zip(targets, outputs)}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time, evaluator_time=evaluator_time)
boxes = outputs[0]['boxes'].numpy()
bmask1 = get_bbox_mask(mask=masks[0], bbox=boxes)
Image._show(Image.fromarray(bmask1))
dfs = 2
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
coco_evaluator.accumulate()
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
return coco_evaluator
def wrapper_acgpn():
for filename in os.listdir('../Data_preprocessing/test_label'):
if filename != variableModels.get()[:8] + '.png':
os.remove(filename[:8] + '.png')
if len(os.listdir('../Data_preprocessing/test_label')) == 0:
copyfile('./pictures/Label/' + variableModels.get()[:8] + '.png',
'../Data_preprocessing/test_label/' + variableModels.get()[:8] + '.png')
copyfile('./pictures/Models/' + variableModels.get(), '../Data_preprocessing/test_img/' + variableModels.get())
copyfile('./pictures/dresses/' + variableDresses.get(), '../Data_preprocessing/test_color/' + variableDresses.get())
launch_acgpn()
preproc_img = Image.open('./sample/' + variableModels.get())
w, h = preproc_img.size
Image._show(preproc_img.crop((384, 0, w, h)))
def decode(image_ch, color_mode="F", mode="D2"):
"""
:param image_ch: Numpy array of encoded image channel(s)
:param color_mode: Image color mode [F, RGB]
:param mode: Mode of transformation [D2, D4]
:return: Decoded image object
"""
if verbose:
print("DECODING ...\n",
"\nColor mode: " + color_mode,
"\nTransformation mode: " + mode)
CL = check_mode(mode)
if is_rgb_color_mode(color_mode):
decoded_img = decode_rgb(image_ch, CL)
else:
decoded_img = decode_grayscale(image_ch, CL)
if verbose:
Image._show(decoded_img)
return decoded_img
import mahotas
import numpy
import pylab
import scipy
from PIL import Image,ImageDraw
from Tkinter import *
from tkFileDialog import askopenfilename
from PIL import Image, Image
print "Reading image to im"
im = Image.open("track.png")
im = im.resize((900,600),Image.ANTIALIAS);
Image._show(im)
px = []
py = []
def onmouse(event):
px.append(event.x)
py.append(event.y)
return px,py
#===============================================================================
# print 'Reading image from file'
def google_map(good_hotspots):
# show a Google map of hotspots ranked by goodness!
# sort hotspots by expected number of birds!
hs_sort = sorted(good_hotspots, key=lambda x: x['expected_n'], reverse=True)
expected_n = [x['expected_n'] for x in good_hotspots]
cutoff = max(expected_n) - (max(expected_n) - min(expected_n))*0.5
use_good = []
use_bad = []
for x in hs_sort[:20]:
if x['expected_n'] >= cutoff:
use_good.append(x)
else:
use_bad.append(x)
good_marker_colors = ['red', 'orange', 'yellow', 'green', 'blue', 'purple']
while len(good_marker_colors) < len(use_good):
good_marker_colors.extend(good_marker_colors)
bad_marker_colors = ['black']
# Print to screen
print '\nTop hotspots:'
for idx_good, hs in enumerate(use_good):
print '%i: %s , expected number of new birds = %.2f' \
% (idx_good+1, hs['locality'], hs['expected_n'])
print '\nAdditional hotspots:'
for hs in use_bad:
print '%s , expected number of new birds = %.2f' \
% (hs['locality'], hs['expected_n'])
# Use top 20%:
# max_hotspots = 20
# if len(good_hotspots) > 1:
# ind_cutoff = int(len(good_hotspots)*0.2)
# cutoff = 'Top 20%'
# else:
# ind_cutoff = len(good_hotspots)
# cutoff = 'All good'
#
# print '\n%s of hotspots:' % cutoff
# for idx, hs in enumerate(hs_sort):
# if idx == ind_cutoff:
# print '\nOthers'
# print '%i: %s , expected number of new birds = %.2f' % (idx+1, hs['locality'], hs['expected_n'])
# use_good = hs_sort[:ind_cutoff]
# use_bad = hs_sort[ind_cutoff:]
# Generate URL with markers
base_url = 'http://maps.googleapis.com/maps/api/staticmap?&size=1000x1000&sensor=false]'
url_param = ''
# markers for good hotspots
url_param = '&markers=size:mid%%7Ccolor:%s' %good_marker_colors[0]
for idx_good, x in enumerate(use_good):
url_param += '&markers=size:mid%%7Ccolor:%s%%7Clabel:%i' \
% (good_marker_colors[idx_good], idx_good+1)
url_param += '%%7C%f,%f' %(x['lat'], x['lng'])
# markers for bad hotspots
url_param += '&markers=size:mid%%7Ccolor:%s' % bad_marker_colors[0]
for idx_bad, x in enumerate(use_bad):
url_param += '&markers=size:small%%7Ccolor:%s%%7Clabel:%i' \
% (bad_marker_colors[0], idx_bad + idx_good +2)
url_param += '%%7C%f,%f' %(x['lat'], x['lng'])
image_bytes = urllib.urlopen(base_url+url_param).read()
image = Image.open(StringIO(image_bytes)) # StringIO makes file object out of image data
Image._show(image)
return
def read_images(path, is_directory=True):
images = []
png_files = []
jpeg_files = []
reader = tf.WholeFileReader()
png_files_path = glob.glob(os.path.join(path, '*.[pP][nN][gG]'))
jpeg_files_path = glob.glob(os.path.join(path, '*.[jJ][pP][eE][gG]')) #,
jpg_files_path = glob.glob(os.path.join(path, '*.[jJ][pP][gG]')) # glob.glob(os.path.join(path + '*.[jJ][pP][nN][gG]'))
print(png_files_path)
# jpeg_files_path = [glob.glob(path + '*.jpg'), glob.glob(path + '*.jpeg')]
if is_directory:
for filename in png_files_path:
png_files.append(filename)
for filename in jpeg_files_path:
jpeg_files.append(filename)
for filename in jpg_files_path:
jpeg_files.append(filename)
else:
_, extension = os.path.splitext(path)
print(extension)
if extension.lower() == '.png':
key, value = reader.read(tf.train.string_input_producer(path))
img = tf.image.decode_png(value)
print(img)
Image._show(Image.fromarray(np.asarray(img)))
return img
# Decode if there is a PNG file:
if len(png_files) > 0:
png_file_queue = tf.train.string_input_producer(png_files)
pkey, pvalue = reader.read(png_file_queue)
p_img = tf.image.decode_png(pvalue)
if len(jpeg_files) > 0:
jpeg_file_queue = tf.train.string_input_producer(jpeg_files)
jkey, jvalue = reader.read(jpeg_file_queue)
j_img = tf.image.decode_jpeg(jvalue)
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
if len(png_files) > 0:
for i in range(len(png_files)):
png = p_img.eval()
images.append(png)
# Image._showxv(Image.fromarray(np.asarray(png)))
if len(jpeg_files) > 0:
for i in range(len(jpeg_files)):
jpeg = j_img.eval()
images.append(jpeg)
coord.request_stop()
coord.join(threads)
return images
