def draw_maze(self):
wall_block_image = Image.open("../IMAGE/wall_block.png")
wall_block_image = wall_block_image.resize((size_of_block, size_of_block), Image.ANTIALIAS)
wall_block_image = ImageTk.PhotoImage(wall_block_image)
food_image = Image.open("../IMAGE/food.png")
food_image = food_image.resize((size_of_block, size_of_block), Image.ANTIALIAS)
food_image = ImageTk.PhotoImage(food_image)
ghost_image = Image.open("../IMAGE/ghost.png")
ghost_image = ghost_image.resize((size_of_block, size_of_block), Image.ANTIALIAS)
ghost_image = ImageTk.PhotoImage(ghost_image)
score_image = Image.open("../IMAGE/score.png")
score_image = score_image.resize((180, 130), Image.ANTIALIAS)
score_image = ImageTk.PhotoImage(score_image)
score_image_frame = self.maze_frame.create_image(self.size[1] * size_of_block + 20, (self.size[0] * size_of_block)//2 - 50, anchor='nw', image=score_image)
for row in range(self.size[0]):
for column in range(self.size[1]):
if self.maze[row][column] == 1:
self.maze_frame.create_image(column * size_of_block, row * size_of_block, anchor='nw', image=wall_block_image)
if self.maze[row][column] == 2:
self.food.append([self.maze_frame.create_image(column * size_of_block, row * size_of_block, anchor='nw', image=food_image), (row, column)])
for row in range(self.size[0]):
for column in range(self.size[1]):
if self.maze[row][column] == 3:
self.monster.append([self.maze_frame.create_image(column * size_of_block, row * size_of_block, anchor='nw', image=ghost_image), (row, column)])
self.maze_frame.image = [wall_block_image, food_image, ghost_image, score_image]
def input():
print("enter the file name")
from inputproject import wq
qw = wq
print(qw)
im = Image.open(qw, "r")
im.show()
def draw_pacman(self):
pacman_image = Image.open("../IMAGE/pacman.png")
pacman_image = pacman_image.resize((size_of_block, size_of_block), Image.ANTIALIAS)
pacman_image = ImageTk.PhotoImage(pacman_image)
self.pacman = self.maze_frame.create_image(self.pacman_pos[1] * size_of_block, self.pacman_pos[0] * size_of_block, anchor='nw', image=pacman_image)
self.maze_frame.image.append(pacman_image)
def edit(): # function to edit images
count = 0
for item in dirs: # traversing through all directories
percentage = (count /
len(dirs)) * 100 # just to show percentage of conversion
print("Editing images ==> " + str(percentage) + '%')
count += 1
if os.path.isfile(
source + item
): # check if directories are files or not, ignore if not files
img = Image.open(source + item) # loading images
file_path, splited_text = os.path.splitext(
source + item
) # file_path,splited_text are used to store splitted text, file_path contains full path of images while splited_text will be empty
file_name = file_path.split('/')[
-1] # spliting the name of file from full path
img_resize = img.resize(
(128, 128),
Image.ANTIALIAS) # resizing images from 192x192 to 128x128
img_rotate = img_resize.rotate(
90) # rotating images to 90 degree clock_wise
if img_rotate.mode != 'RGB': # converting images to RGB's to save without error
img_rotate = img_rotate.convert('RGB')
img_rotate.save(
destination + file_name + '.jpeg', 'JPEG',
quality=90) # saving edited images to destination path
def getImagesAndLabels(path): #method to get the images and label data
imagePaths = [os.path.join(path, f)
for f in os.listdir(path)] # Get all file path
faceSamples = [] # create empty face sample list
ids = [] # create empty id list
for imagePath in imagePaths: # Loop for all the file path
PIL_img = Image.open(imagePath).convert(
'L') # Get the image and convert it to grayscale
img_numpy = np.array(PIL_img, 'uint8') # PIL image to numpy array
id = int(
os.path.split(imagePath)[-1].split(".")[1]) # Get the image id
faces = detector.detectMultiScale(
img_numpy) # Get the face from the training images
for (x, y, w,
h) in faces: # Loop for each face, append to their respective ID
faceSamples.append(img_numpy[y:y + h, x:x +
w]) # Add the image to face samples
ids.append(id) # Add the ID to IDs
return faceSamples, ids
def get_image(path):
img = np.asarray(Image.open(path))
if len(img.shape) == 3:
if img.shape[2] > 3:
return np.array([[pixel[:3] for pixel in row] for row in img])
elif len(img.shape) == 2:
return np.array([[[pixel] * 3 for pixel in row] for row in img])
return img
def abhi():
import tkinter as qw
root = qw.Tk()
im = Image.open('F:\wallpaper\Wallpaperz\arizona_waterfalls-wide.jpg')
# im = im.resize((950, 1050))
# img = ImageTk.PhotoImage(Image.open('F:\wallpaper\Wallpaperz\arizona_waterfalls-wide.jpg'))
panel = Label(root, image=im)
panel.pack(side="bottom", fill="both", expand="yes")
root.mainloop()
def GOBoardElement(self, window):
imgWidth = "500"
imgHeight = "500"
img = Image.open("GO9by9.png")
img = img.resize(imgWidth + "x" + imgHeight)
img = ImageTk.PhotoImage(img)
panel = window.Label(window, image=img)
panel.image = img
panel.place(relheight=.095, relwidth=0.25, relx=0.7, rely=0.03)
def colorfilter():
global panel
x = openfn()
u = color123()
color_image = Image.open(x)
bw = color_image.convert('L')
img = bw.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
def resize(imgFile): im1 = Image.open(imgFile) width, height = im1.size ratio = (width*100/height) #if ratio < 60 or ratio > 150: # print imgFile #im1.close() print imgFile, "(original):", width, "x", height im2 = im1.resize((150,150), Image.ANTIALIAS) im2.save(imgFile) width, height = im2.size print imgFile, "(new):", width, "x", height
def open_img():
global panel
x = openfn()
img = Image.open(x)
img = img.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
#panel.image = NONE
panel.pack(side="top", fill="both", expand="NO")
def decrypt():
# load the image and convert it into a numpy array and display on the GUI.
load = Image.open("Syngenta.jpg")
load.thumbnail(image_display_size, Image.ANTIALIAS)
load = np.asarray(load)
load = Image.fromarray(np.uint8(load))
render = ImageTk.PhotoImage(load)
img = Label(app, image=render)
img.image = render
img.place(x=100, y=50)
# Algorithm to decrypt the data from the image
img = cv2.imread("Syngenta.jpg")
data = []
stop = False
for index_i, i in enumerate(img):
i.tolist()
for index_j, j in enumerate(i):
if ((index_j) % 3 == 2):
# first pixel
data.append(bin(j[0])[-1])
# second pixel
data.append(bin(j[1])[-1])
# third pixel
if (bin(j[2])[-1] == '1'):
stop = True
break
else:
# first pixel
data.append(bin(j[0])[-1])
# second pixel
data.append(bin(j[1])[-1])
# third pixel
data.append(bin(j[2])[-1])
if (stop):
break
message = []
# join all the bits to form letters (ASCII Representation)
for i in range(int((len(data) + 1) / 8)):
message.append(data[i * 8:(i * 8 + 8)])
# join all the letters to form the message.
message = [chr(int(''.join(i), 2)) for i in message]
message = ''.join(message)
message_label = Label(app,
text=message,
bg='lavender',
font=("Times New Roman", 10))
message_label.place(x=30, y=400)
def train_classifier(data_dir):
path = [os.path.join(data_dir, f) for f in os.listdir(data_dir)]
faces = []
ids = []
for imgs in path:
img = Image.open(imgs).convert("L")
imgeNp = np.array(img, 'uint8')
id = int(os.path.split(imgs)[1].split(".")[1])
faces.append(imgeNp)
ids.append(id)
ids = np.array(ids)
clf = cv2.face.LBPHFaceRecognizer_create()
clf.train(faces, ids)
clf.write("aomclassifier.xml")
def input_fn(request_body, content_type='application/json'):
logger.info('Deserializing the input data.')
if content_type == 'application/json':
input_data = json.loads(request_body)
url = input_data['url']
logger.info(f'Image url: {url}')
image_data = Image.open(requests.get(url, stream=True).raw)
image_transform = transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(size=224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
return image_transform(image_data)
raise Exception(f'Requested unsupported ContentType in content_type {content_type}')
def Recognize(self, fn):
im = Image.open(fn)
im = util.CenterExtend(im, radius=20)
vec = np.asarray(im.convert('L')).copy()
Y = []
for i in range(vec.shape[0]):
for j in range(vec.shape[1]):
if vec[i][j] <= 200:
Y.append([i, j])
gmm = GaussianMixture(n_components=7,
covariance_type='tied',
reg_covar=1e2,
tol=1e3,
n_init=9)
gmm.fit(Y)
centers = gmm.means_
points = []
for i in range(7):
scoring = 0.0
for w_i in range(3):
for w_j in range(3):
p_x = centers[i][0] - 1 + w_i
p_y = centers[i][1] - 1 + w_j
cr = util.crop(im, p_x, p_y, radius=20)
cr = cr.resize((40, 40), Image.ANTIALIAS)
X = np.asarray(cr.convert('L'), dtype='float')
X = (X.astype("float") - 180) / 200
x0 = np.expand_dims(X, axis=0)
x1 = np.expand_dims(x0, axis=3)
global model
if self.model.predict(x1)[0][0] < 0.5:
scoring += 1
if scoring > 4:
points.append((centers[i][0] - 20, centers[i][1] - 20))
return points
def main(file):
#file = 'substinces.PNG'
# opening an image from the source path
img = Image.open(file)
dot = file.index(".")
name = file[0:dot]
# path where the tesseract module is installed
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files (x86)/Tesseract-OCR/tesseract.exe'
# converts the image to result and saves it into result variable
result = pytesseract.image_to_string(img)
# write text in a text file and save it to source path
with open(name + '.txt', mode='w') as file:
file.write(result)
with open(name + '.txt', mode='r') as file:
orig = file.read()
def processPic(self):
""" Looks for geodata in EXIF.
Args: -
Returns:
None: when either the pic is not open or no geodata is found.
data: data with geodata represented as {"latitude": value, "longitude": value, "datetime": value}.
"""
try:
image = Image.open(self.lclPath)
except:
return None
else:
exif_data = exif.get_exif_data(image)
data = exif.get_lat_lon_datatime(exif_data)
if data and (data["latitude"] != "" and data["longitude"] != ""):
return data
def UploadInfo(self):
#ticket = str(self.ticket_number.get())+".PNG"
#print(ticket)
try:
self.path = "D:/Apache Software Foundation/Tomcat 9.0/webapps/cenrayid/WEB-INF/classes/photo/" + self.ticket_number.get(
) + ".jpg"
#path="/Photo/"+str(self.ticket_number.get())+".png"
#self.img = ImageTk.PhotoImage(Image.open(path))
self.img = Image.open(self.path)
self.img = self.img.resize((100, 100), Image.ANTIALIAS)
self.img = ImageTk.PhotoImage(self.img)
'''
self.img=PhotoImage(file="D:/TimeKeeper_stuffs/Photo/"+self.ticket_number.get()+".png")
self.img=self.img.zoom(25,25)
self.img=self.img.subsample(50)
'''
Label(self.root, image=self.img).place(x=50, y=450)
except Exception as e:
#print(e)
Label(self.root, text="unable to load image").place(x=50, y=450)
self.dictionarydata = {}
#***********************************************************************
# created object of class DataBase;
# if we want to use different database then just change DataBase class
self.db = DataBase()
#************************************************************************
#print(self.ticket_number.get())
self.dictionarydata = self.db.load(self.ticket_number.get())
#print(self.dictionarydata)
if (self.dictionarydata):
self.fullname.set(self.dictionarydata['full_name'])
self.department.set(self.dictionarydata['department'])
#self.cardLost.set(self.dictionarydata['card_lost'])
self.memo.set(self.dictionarydata['reason_for_memo'])
self.remark.insert('end', self.dictionarydata['remark'])
self.shift.set(self.dictionarydata['shift'])
self.dept_code.set(self.dictionarydata['department_code'])
return True
else:
return False
def mirror():
global panel
x = openfn()
image_obj = Image.open(x)
rotated_image = image_obj.transpose(Image.FLIP_LEFT_RIGHT)
img = rotated_image.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
rotated_image.save('mirror.JPEG')
threading.Timer(5.0, saveb).start()
def cropy():
global panel
x = openfn()
image_obj = Image.open(x)
cropped_image = image_obj.crop((161, 166, 706, 1050))
img = cropped_image.resize((400, 500), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
cropped_image.save('crop.JPEG')
threading.Timer(5.0, saveb).start()
def blackwhite():
global panel
x = openfn()
color_image = Image.open(x)
bw = color_image.convert('L')
img = bw.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
bw.save('blackandwhite.jpg')
threading.Timer(5.0, saveb).start()
def rotate():
global panel
x = openfn()
image_obj = Image.open(x)
rotated_image = image_obj.rotate(90)
img = rotated_image.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
#rotated_image.save(saved_location)
#rotated_image.show()
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
rotated_image.save('rotate.JPEG')
threading.Timer(5.0, saveb).start()
def sharpen():
global panel
x = openfn()
im = Image.open(x)
im_sharp = im.filter(ImageFilter.SHARPEN)
img = im_sharp.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack(side="top", fill="both", expand="NO")
#im_sharp.save('image_sharpened.jpg', 'JPEG')
#im_sharp.show()
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
im_sharp.save('sharpen.jpg')
threading.Timer(5.0, saveb).start()
def blur():
global panel
x = openfn()
img = Image.open(x)
im = img.filter(ImageFilter.BLUR)
img = im.resize((1250, 900), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
#panel.image = NONE
panel.pack(side="top", fill="both", expand="NO")
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
im.save('blur.jpg')
threading.Timer(5.0, saveb).start()
def brightness():
global panel
x = openfn()
im = Image.open(x)
im3 = ImageEnhance.Brightness(im)
im2 = im3.enhance(2.0)
img = im2.resize((1000, 800), Image.ANTIALIAS)
img = ImageTk.PhotoImage(img)
panel = Label(root, image=img)
panel.image = img
panel.pack()
#panel.image = NONE
def saveb():
MsgBox = tk.messagebox.askquestion('SAVE',
'do you want to save the img',
icon='warning')
if MsgBox == 'yes':
im3.enhance(2.0).save("brightness.jpg")
threading.Timer(5.0, saveb).start()
def getImagesAndLabels(path):
#get the path of all the files in the folder
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
#create empth face list
faceSamples = []
#create empty ID list
Ids = []
#now looping through all the image paths and loading the Ids and the images
for imagePath in imagePaths:
#loading the image and converting it to gray scale
pilImage = Image.open(imagePath).convert('L')
#Now we are converting the PIL image into numpy array
imageNp = np.array(pilImage, 'uint8')
#getting the Id from the image
Id = int(os.path.split(imagePath)[-1].split(".")[1])
# extract the face from the training image sample
faces = detector.detectMultiScale(imageNp)
#If a face is there then append that in the list as well as Id of it
for (x, y, w, h) in faces:
faceSamples.append(imageNp[y:y + h, x:x + w])
Ids.append(Id)
return faceSamples, Ids
def getImagesWithID(path):
imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
#print (imagePaths)
#getImagesWithID(path)
faces = []
IDs = []
for imagePath in imagePaths:
faceImg = Image.open(imagePath).convert('L')
# this is pil image so we need to convert it into numpy array so that open cv can work with it
faceNp = np.array(faceImg, 'uint8')
#now we want face ids
ID = int(os.path.split(imagePath)[-1].split('.')[1])
#now we have id and images now we can directly store it to into the faces and ids.
faces.append(faceNp)
IDs.append(ID)
cv2.imshow("training",
faceNp) #to show which images that are captured.
cv2.waitKey(70)
return np.array(IDs), faces
def __getitem__(self, item):
image = Image.open(self.image_paths[item])
image = image.convert('RGB')
targets = self.targets[item]
if self.resize is not None:
image = image.resize((self.resize[1], self.resize[0]),
resample=Image.BILINEAR)
image = np.array(image)
if self.augmentations is not None:
augmented = self.augmentations(image=image)
image = augmented['image']
image = np.transpose(image, (2, 0, 1)).astype(np.float64)
return {
'image': torch.tensor(image, dtype=torch.float),
'targets': torch.tensor(targets, dtype=torch.long)
}
def getIImage(b64_img):
valid_types = ('png','jpg','jpeg')
imgBytes = base64.decodebytes(b64_img.encode('UTF-8'))
imgType = imghdr.what(None, imgBytes)
if imgType not in valid_types: # we should convert it!
imgSI = BytesIO(imgBytes)
imgSO = BytesIO()
img = Image.open(imgSI)
if img.format.lower() in valid_types:
imgB64 = b64_img
imgType = img.format
else:
img.save(imgSO, 'png')
imgSO.seek(0)
imgB64 = base64.encodebytes(imgSO.read()).decode('UTF-8').replace("\n", "")
imgType = 'png'
img.close()
imgSO.close()
imgSI.close()
else:
imgB64 = b64_img
return imgB64, imgType
def load_images(file):
"""
Function loads black and white image and convert it to CIE LAB
Parameters
----------
file : str
Path to image file
Returns
-------
ndarray
3D array with image
"""
try:
img = Image.open(file)
except (OSError, ValueError, IOError, ZeroDivisionError) as e:
print("Can not open file", file, "Error: ", e)
return None
img = img.convert(mode="RGB") # ensure that image rgb
rgb = np.array(img)
return color.rgb2lab(rgb)
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 14 16:07:12 2020
@author: Sourav
"""#Import required Image library
import cv2
import os
from pil import Image,ImageFilter
im = Image.open('G:\chk2.jpg')
#im.show()
cropped = im.crop((472,147,703,241))
gaussImage = cropped.filter(ImageFilter.GaussianBlur(20))
im.paste(gaussImage,(472,147,703,241))
im.save('G:\masked1.jpg')
# Reading an image in default mode
image = cv2.imread('G:\masked1.jpg')
directory = r'G:\Chequemasking'
os.chdir(directory)
print(os.listdir(directory))
# font
font = cv2.FONT_HERSHEY_PLAIN
org = (472,220)
# fontScale
def open_file_as_pil_image(source_file):
"""Return a new Image object from source file."""
return Image.open(source_file)
from pil import Image, ImageDraw, ImageFont
def add_num(img):
draw = ImageDraw.Draw(img)
myfont = ImageFont.truetype('C:/windows/fonts/Arial.ttf', size=40)
fillcolor = "#ff0000"
width, height = img.size
draw.text((width-40, 0), '99', font=myfont, fill=fillcolor)
img.save('result.jpg', 'jpeg')
return 0
if __name__ == '__main__':
image = Image.open('C:\Users\Administrator\Pictures\zoro.jpg')
add_num(image)