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 create_image(filename,
size=(1280, 720),
message="Text",
bg_color=random_color(),
number=-1,
text_color="white"):
"""Creates a image .png file with the text"""
# create an image that we can draw on
img = Image.new("RGB", size, bg_color)
draw = ImageDraw.Draw(img)
# load font for the main text (message)
font_main_text = ImageFont.truetype("FiraCode-Regular.ttf", 50)
# get position so that the message is located in the middle of the image
width_2, height_2 = draw.textsize(message, font=font_main_text)
position_text = ((size[0] - width_2) / 2, (size[1] - height_2) / 2)
# draw the text on the image
draw.text(position_text, message, fill=text_color, font=font_main_text)
# if wanted (number!=-1) add a sub text to the image
if number != -1:
# load font for the sub text
font_sub_text = ImageFont.truetype("FiraCode-Regular.ttf", 30)
# determine position of the sub text in the middle but slightly down
width_2, height_2 = draw.textsize(">> Demo file #" + str(number),
font=font_sub_text)
position_text = ((size[0] - width_2) / 2,
(size[1] - height_2) / 2 + 100)
# draw the sub text on the image
draw.text(position_text,
">> Demo file #" + str(number),
fill=text_color,
font=font_sub_text)
# save draw object as a image
draw = ImageDraw.Draw(img)
img.save(filename)
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 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 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 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 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 __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 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 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)
def parse(self, label_path, img_path, img_type=".png"):
try:
(dir_path, dir_names,
filenames) = next(os.walk(os.path.abspath(label_path)))
data = {}
progress_length = len(filenames)
progress_cnt = 0
printProgressBar(0,
progress_length,
prefix='\nYOLO Parsing:'.ljust(15),
suffix='Complete',
length=40)
for filename in filenames:
txt = open(os.path.join(dir_path, filename), "r")
filename = filename.split(".")[0]
img = Image.open(
os.path.join(img_path, "".join([filename, img_type])))
img_width = str(img.size[0])
img_height = str(img.size[1])
img_depth = 3
size = {
"width": img_width,
"height": img_height,
"depth": img_depth
}
obj = {}
obj_cnt = 0
for line in txt:
elements = line.split(" ")
name_id = elements[0]
xminAddxmax = float(elements[1]) * (2.0 * float(img_width))
yminAddymax = float(
elements[2]) * (2.0 * float(img_height))
w = float(elements[3]) * float(img_width)
h = float(elements[4]) * float(img_height)
xmin = (xminAddxmax - w) / 2
ymin = (yminAddymax - h) / 2
xmax = xmin + w
ymax = ymin + h
bndbox = {
"xmin": float(xmin),
"ymin": float(ymin),
"xmax": float(xmax),
"ymax": float(ymax)
}
obj_info = {"name": name_id, "bndbox": bndbox}
obj[str(obj_cnt)] = obj_info
obj_cnt += 1
obj["num_obj"] = obj_cnt
data[filename] = {"size": size, "objects": obj}
printProgressBar(progress_cnt + 1,
progress_length,
prefix='YOLO Parsing:'.ljust(15),
suffix='Complete',
length=40)
progress_cnt += 1
return True, data
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
msg = "ERROR : {}, moreInfo : {}\t{}\t{}".format(
e, exc_type, fname, exc_tb.tb_lineno)
return False, msg
def parse(label_path, img_path, img_type=".png"):
try:
with open("box_groups.txt", "w") as bboxGroups:
(dir_path, dir_names,
filenames) = next(os.walk(os.path.abspath(label_path)))
data = {}
progress_length = len(filenames)
progress_cnt = 0
printProgressBar(0,
progress_length,
prefix='\nKITTI Parsing:'.ljust(15),
suffix='Complete',
length=40)
for filename in filenames:
txt = open(os.path.join(dir_path, filename), "r")
filename = filename.split(".")[0]
img = Image.open(
os.path.join(img_path, "".join([filename, img_type])))
img_width = str(img.size[0])
img_height = str(img.size[1])
img_depth = 3
size = {
"width": img_width,
"height": img_height,
"depth": img_depth
}
obj = {}
obj_cnt = 0
for line in txt:
elements = line.split(" ")
name = elements[0]
if name == "DontCare":
continue
xmin = elements[4]
ymin = elements[5]
xmax = elements[6]
ymax = elements[7]
bndbox = {
"xmin": float(xmin),
"ymin": float(ymin),
"xmax": float(xmax),
"ymax": float(ymax)
}
bboxGroups.write("{} {} {} {}\n".format(
float(xmin), float(ymin),
float(xmax) - float(xmin),
float(ymax) - float(ymin)))
obj_info = {"name": name, "bndbox": bndbox}
obj[str(obj_cnt)] = obj_info
obj_cnt += 1
obj["num_obj"] = obj_cnt
data[filename] = {"size": size, "objects": obj}
printProgressBar(progress_cnt + 1,
progress_length,
prefix='KITTI Parsing:'.ljust(15),
suffix='Complete',
length=40)
progress_cnt += 1
return True, data
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
msg = "ERROR : {}, moreInfo : {}\t{}\t{}".format(
e, exc_type, fname, exc_tb.tb_lineno)
return False, msg
def parse(csv_path, img_path):
try:
raw_f = open(csv_path, 'r', encoding='utf-8')
csv_f = csv.reader(raw_f)
progress_length = sum(1 for row in csv_f)
raw_f.seek(0)
progress_cnt = 0
printProgressBar(0,
progress_length,
prefix='\nUDACITY Parsing:'.ljust(15),
suffix='Complete',
length=40)
data = {}
for line in csv_f:
raw_line = line[0].split(" ")
raw_line_length = len(raw_line)
filename = raw_line[0].split(".")[0]
xmin = float(raw_line[1])
ymin = float(raw_line[2])
xmax = float(raw_line[3])
ymax = float(raw_line[4])
cls = raw_line[6].split('"')[1]
if raw_line_length is 8:
state = raw_line[7].split('"')[1]
cls = cls + state
img = Image.open(
os.path.join(img_path, "".join([filename, ".jpg"])))
img_width = str(img.size[0])
img_height = str(img.size[1])
img_depth = 3
size = {
"width": img_width,
"height": img_height,
"depth": img_depth
}
bndbox = {
"xmin": xmin,
"ymin": ymin,
"xmax": xmax,
"ymax": ymax
}
obj_info = {"name": cls, "bndbox": bndbox}
if filename in data:
obj_idx = str(int(data[filename]["objects"]["num_obj"]))
data[filename]["objects"][str(obj_idx)] = obj_info
data[filename]["objects"]["num_obj"] = int(obj_idx) + 1
elif filename not in data:
obj = {"num_obj": "1", "0": obj_info}
data[filename] = {"size": size, "objects": obj}
printProgressBar(progress_cnt + 1,
progress_length,
prefix='UDACITY Parsing:'.ljust(15),
suffix='Complete',
length=40)
progress_cnt += 1
return True, data
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
msg = "ERROR : {}, moreInfo : {}\t{}\t{}".format(
e, exc_type, fname, exc_tb.tb_lineno)
return False, msg
from selenium.webdriver import Chrome, ChromeOptions
from selenium.webdriver.chrome.options import Options
from selenium.webdriver import ActionChains
option = ChromeOptions()
# option.add_experimental_option('excludeSwitches',['enable-automation'])
chrome_option = Options()
# chrome_option.add_argument('--headless')
# chrome_option.add_argument('--dosable-gpu')
es = Chrome(options=option, chrome_options=chrome_option)
es.get('https://www.baidu.com/')
sleep(5)
es.save_screenshot('./day05/1.png')
img = es.find_element_by_xpath('//div[@id="lg"]/img')
sleep(3)
location = img.location
size = img.size
rangle = (int(location['x']), int(location['y']),
int(location['x'] + size['width']),
int(location['y'] + size['height']))
print(rangle)
i = Image.open('./day05/1.png')
frame = i.crop(rangle)
frame.save('./day05/frame.png')
# 构建数据流对图片进行点击操作
action = ActionChains(es)
# move_to_element_with_offset() 第一个参数是作为目标的图片,第二个参数和第三个参数分别为横向便宜和纵向偏移
# click() 方法,进行点击
# perform() 将设定好的数据流立即执行
action.move_to_element_with_offset(img, 5, 5).click().perform()
sleep(3)
es.quit()
"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\|()1{}[]?-_+~<>i!lI;:,\"^`'. ")
def get_char(r, g, b, alpha=256):
if alpha == 0:
return ' '
length = len(ascii_char)
gray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)
unit = (256.0 + 1) / length
return ascii_char[int(gray / unit)]
if __name__ == '__main__':
im = Image.open(IMG)
im = im.resize((WIDTH, HEIGHT), Image.NEAREST)
txt = ""
for i in range(HEIGHT):
for j in range(WIDTH):
txt += get_char(*im.getpixe((j, i)))
txt += '\n'
print(txt)
if OUTPUT:
with open(OUTPUT, 'w') as f:
f.write(txt)
else:
def open_file_as_pil_image(source_file):
"""Return a new Image object from source file."""
return Image.open(source_file)
def CenterExtend(im, width=400, height=88, radius=20):
x1 = np.full((height+radius+radius, width+radius+radius), 255, dtype='uint8')
x2 = np.asarray(im.convert('L'))
x1[radius:radius+height,radius:radius+width] = x2
return Image.fromarray(x1, 'L')
def __init__(self, master=None):
super().__init__(master, padx=12, pady=3)
if getattr(sys, 'frozen', False):
self.baseDir = os.path.dirname(sys.executable)
else:
self.baseDir = os.path.dirname(os.path.realpath(__file__))
self.loadConfig()
self.master = master
self.queue = queue.initQueue()
self.grid()
rowIndex = 0
self.logoImage = ImageTk.PhotoImage(Image.open('logo.png'))
self.logoLabel = tk.Label(self, image=self.logoImage)
self.logoLabel.grid(row=rowIndex, column=0, columnspan=5)
rowIndex += 1
self.deckNameLabel = tk.Label(self, text='Deckname')
self.deckNameLabel.grid(row=rowIndex, column=0, sticky=tk.W)
self.deckNameEntry = tk.Entry(self, width=60)
self.deckNameEntry.grid(row=rowIndex,
column=1,
columnspan=3,
stick=tk.W)
self.deckNameEntry.insert(0, 'Deck')
rowIndex += 1
self.inputLabel = tk.Label(self, text='File or URL')
self.inputLabel.grid(row=rowIndex, column=0, sticky=tk.W)
self.inputEntry = tk.Entry(self, width=60)
self.inputEntry.grid(row=rowIndex, column=1, columnspan=3, sticky=tk.W)
self.inputButton = tk.Button(self,
text='Browse',
command=self.openFile)
self.inputButton.grid(row=rowIndex, column=4, sticky=tk.E)
rowIndex += 1
self.outputLabel = tk.Label(self, text='Output folder (optional)')
self.outputLabel.grid(row=rowIndex, column=0, sticky=tk.W)
self.outputEntry = tk.Entry(self, width=60)
self.outputEntry.grid(row=rowIndex,
column=1,
columnspan=3,
sticky=tk.W)
self.outputEntry.insert(0, self.config['outputFolder'])
self.outputButton = tk.Button(self,
text='Browse',
command=self.openFolder)
self.outputButton.grid(row=rowIndex, column=4, sticky=tk.E)
rowIndex += 1
self.imgurLabel = tk.Label(self, text='ImgurID (optional)')
self.imgurLabel.grid(row=rowIndex, column=0, sticky=tk.W)
self.imgurEntry = tk.Entry(self, width=60)
self.imgurEntry.grid(row=rowIndex, column=1, columnspan=3, sticky=tk.W)
if self.config['imgurId']:
self.imgurEntry.insert(0, self.config['imgurId'])
self.imgurEntry.config(state='disabled')
rowIndex += 1
self.dropboxLabel = tk.Label(self, text='Dropbox Token(optional)')
self.dropboxLabel.grid(row=rowIndex, column=0, sticky=tk.W)
self.dropboxEntry = tk.Entry(self, width=60)
self.dropboxEntry.grid(row=rowIndex,
column=1,
columnspan=3,
sticky=tk.W)
if self.config['dropboxToken']:
self.dropboxEntry.insert(0, self.config['dropboxToken'])
self.dropboxEntry.config(state='disabled')
rowIndex += 1
self.basicsLabel = tk.Label(self, text='Basic lands')
self.basicsLabel.grid(row=rowIndex, column=0, sticky=tk.W)
basicsOptions = ('guru', 'unstable', 'alpha', 'core', 'guay')
self.basicsVar = tk.StringVar()
self.basicsVar.set(self.config['basicSet'])
self.basicsMenu = tk.OptionMenu(self, self.basicsVar, *basicsOptions)
self.basicsMenu.grid(row=rowIndex, column=1, columnspan=2, sticky=tk.W)
rowIndex += 1
self.hiresVar = tk.IntVar()
self.hiresVar.set(int(self.config['hires']))
self.hiresCheckbutton = tk.Checkbutton(self,
text='High Resolution',
variable=self.hiresVar)
self.hiresCheckbutton.grid(row=rowIndex, column=0, sticky=tk.W)
self.hiresVar.trace('w', self.hiresVarCallback)
self.reprintsVar = tk.IntVar()
self.reprintsVar.set(int(self.config['reprints']))
self.reprintsCheckbutton = tk.Checkbutton(self,
text='Reprints',
variable=self.reprintsVar)
self.reprintsCheckbutton.grid(row=rowIndex, column=1, sticky=tk.W)
self.nocacheVar = tk.IntVar()
self.nocacheVar.set(int(self.config['nocache']))
self.nocacheCheckbutton = tk.Checkbutton(self,
text='No cache',
variable=self.nocacheVar)
self.nocacheCheckbutton.grid(row=rowIndex, column=2, sticky=tk.W)
self.imgurVar = tk.IntVar()
self.imgurVar.set(int(self.config['imgur']))
self.imgurCheckbutton = tk.Checkbutton(self,
text='Imgur Upload',
variable=self.imgurVar)
self.imgurCheckbutton.grid(row=rowIndex, column=3, sticky=tk.W)
self.imgurVar.trace('w', self.imgurVarCallback)
self.updateImgurEntry()
self.dropboxVar = tk.IntVar()
self.dropboxVar.set(int(self.config['dropbox']))
self.dropboxCheckbutton = tk.Checkbutton(self,
text='Dropbox Upload',
variable=self.dropboxVar)
self.dropboxCheckbutton.grid(row=rowIndex, column=4, sticky=tk.W)
self.dropboxVar.trace('w', self.dropboxVarCallback)
self.updateDropboxEntry()
rowIndex += 1
self.progressLabel = tk.Label(self, text='Ready')
self.progressLabel.grid(row=rowIndex,
column=0,
columnspan=4,
sticky=tk.W)
self.generateButton = tk.Button(self,
text='Generate',
command=self.generate)
self.generateButton.grid(row=rowIndex, column=4, sticky=tk.E)
self.processQueue()
for file in files:
if file.endswith("png") or file.endswith("jpg"):
path = os.path.join(root, file)
label = os.path.basename(os.path.dirname(path)).replace(
" ", "-").lower()
#print(label, path)
if not label in label_ids:
label_ids[label] = current_id
current_id += 1
id_ = label_ids[label]
#print(label_ids)
#y_labels.append(label)
#x_train.append(path) #Verify this image, turn it into numpy array, GRAY
pil_image = Image.open(path).convert("L") #grayscale
size = (550, 550)
final_image = pil_image.resize(size, Image.ANTIALIAS)
image_array = np.array(final_image, "uint8")
#print(image_array)
faces = face_cascade.detectMultiScale(image_array,
scaleFactor=1.5,
minNeighbors=5)
for (x, y, w, h) in faces:
roi = image_array[y:y + h, x:x + w]
x_train.append(roi)
y_labels.append(id_)
def open_file_as_pil_image(source_file):
"""Return a new Image object from source file."""
return Image.open(source_file)
def create_empty_pil_image(pil_image):
"""Create an empty PIL Image."""
return Image.new('RGB', (pil_image.size[0], pil_image.size[1]))
# start button created
startb = Button(window1,
text="START",
command=start_fun,
font=("Arial", 25),
bg="orange",
fg="blue",
borderwidth=3,
relief="raised")
startb.place(x=130, y=590)
# image on the main window
path = "Images/front.png"
# Creates a Tkinter-compatible photo image, which can be used everywhere Tkinter expects an image object.
img1 = ImageTk.PhotoImage(Image.open(path))
# The Label widget is a standard Tkinter widget used to display a text or image on the screen.
panel = tk.Label(window1, image=img1)
panel.place(x=320, y=200)
# function created for exiting
def exit_win():
if mbox.askokcancel("Exit", "Do you want to exit?"):
window1.destroy()
# exit button created
exitb = Button(window1,
text="EXIT",
command=exit_win,
# -*- 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
# Tensorflow Hub shall give us pre trained model URLs
classifierUrl = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4"
imageUrl = "https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg"
labelsUrl = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
IMAGE_SHAPE = (224, 224)
INPUT_IMAGE_SHAPE = (224, 224, 3)
# Deep Learning model from TF HUB
model = tf.keras.Sequential(
[hub.KerasLayer(classifierUrl, input_shape=INPUT_IMAGE_SHAPE)])
print(model.summary())
# read the Image from URL
graceHopperImage = tf.keras.utils.get_file('image.jpg', imageUrl)
graceHopperImage = Image.open(graceHopperImage).resize(IMAGE_SHAPE)
print(graceHopperImage)
# Image preprocessing for deep learming model Input
graceHopperImage = np.array(graceHopperImage) / 255.0
print(graceHopperImage.shape)
# read the Labels and store them in a list
labelPath = tf.keras.utils.get_file("ImageNetLabels.txt", labelsUrl)
labels = np.array(open(labelPath).read().splitlines())
print(labels)
# Lets Use the Model for making predictions
result = model.predict(graceHopperImage[np.newaxis, :])
predictedClassLabelIdx = np.argmax(result[0], axis=-1)
y_labels = []
x_train = []
for root, dirs, files in os.walk(image_dir):
for file in files:
if file.endswith("jpg") or file.endswith("JPG"):
path = os.path.join(root, file)
label = os.path.basename(root).replace(" ", "-").lower()
#print(label, path)
if not label in label_ids:
label_ids[label] = current_id
current_id += 1
id_ = label_ids[label]
#print(label_ids)
pil_image = Image.open(path).convert("L")
size = (550, 550)
final_image = pil_image.resize(size, Image.ANTIALIAS)
image_array = np.array(pil_image, "uint8")
#print(image_array)
faces = face_casecade.detectMultiScale(image_array,
scaleFactor=1.5,
minNeighbors=5)
for (x, y, w, h) in faces:
#print(x, y, w, h)
roi = image_array[y:y + h, x:x + w]
x_train.append(roi)
y_labels.append(id_)
#print(y_labels)
def main():
pp = pprint.PrettyPrinter(indent=4)
img_path = args.img_path
label_path = args.label_path
img_type = args.img_type
datasets = args.datasets
cls_list = args.cls_list_file
result = None
data = None
if datasets == "COCO":
coco = COCO()
result, data = coco.parse(label_path)
elif datasets == "VOC":
voc = VOC()
result, data = voc.parse(label_path)
elif datasets == "UDACITY":
udacity = UDACITY()
result, data = udacity.parse(label_path, img_path)
elif datasets == "KITTI":
kitti = KITTI()
result, data = kitti.parse(label_path, img_path, img_type=img_type)
elif datasets == "YOLO":
yolo = YOLO(os.path.abspath(cls_list))
result, data = yolo.parse(label_path, img_path, img_type=img_type)
if result is True:
for key in data:
filepath = "".join([img_path, key, img_type])
im = Image.open(filepath)
draw = ImageDraw.Draw(im)
print("data['{}']: ".format(key), end="")
pp.pprint(data[key])
print("num_object : {}".format(data[key]["objects"]["num_obj"]))
for idx in range(0, int(data[key]["objects"]["num_obj"])):
print("idx {}, name : {}, bndbox :{}".format(
idx, data[key]["objects"][str(idx)]["name"],
data[key]["objects"][str(idx)]["bndbox"]))
x0 = data[key]["objects"][str(idx)]["bndbox"]["xmin"]
y0 = data[key]["objects"][str(idx)]["bndbox"]["ymin"]
x1 = data[key]["objects"][str(idx)]["bndbox"]["xmax"]
y1 = data[key]["objects"][str(idx)]["bndbox"]["ymax"]
draw.rectangle(((x0, y0), (x1, y1)), outline='#00ff88')
draw.text((x0, y0), data[key]["objects"][str(idx)]["name"])
del draw
print(
"===============================================================================================\n\n"
)
plt.imshow(im)
plt.show()
plt.clf()
im.close()
else:
print("return value : {}, msg : {}, args: {}".format(
result, data, args))
def create_empty_pil_image(pil_image):
"""Create an empty PIL Image."""
return Image.new('RGB', (pil_image.size[0], pil_image.size[1]))
import os
import numpy as np
from pil import Image
fname = 'galaga_sprite_sheet.png'
image_dir = 'images'
sprite_sheet = Image.open(os.path.join(image_dir, fname))
im_data = np.array(sprite_sheet)
assert im_data.shape == (383, 424, 3)
# Extract data for each sprite from sprite sheet
sprites = []
for yi in range(12):
y = 55 + yi * 24
for xi in range(8):
x = 16 + xi * 24
sprite = im_data[y:y + 16, x:x + 16, :]
sprites.append(sprite)
# Add a border and save as individual files
for i, sprite in enumerate(sprites):
img_data = np.zeros((19, 19, 3), dtype='uint8')
img_data[2:18, 2:18, :] = sprite
img = Image.fromarray(img_data)
if sprite.sum() > 0:
img.save(os.path.join(image_dir, f'galaga_{i:02d}.png'))
# Adding label into the frame
l0 = tkinter.Label(root, text="")
l0.pack()
# adding label with different font and formatting
l1 = tkinter.Label(root,
text="Hello...!",
fg="light green",
bg="dark green",
font="Helvetica 16 bold italic")
l1.pack()
# images
dice = ['die1.png', 'die2.png', 'die3.png', 'die4.png', 'die5.png', 'die6.png']
# simulating the dice with random numbers between 0 to 6 and generating image
image1 = ImageTk.PhotoImage(Image.open(random.choice(dice)))
# construct a label widget for image
label1 = tkinter.Label(root, image=image1)
label1.image = image1
# packing a widget in the parent widget
label1.pack(expand=True)
# function activated by button
def rolling_dice():
image1 = ImageTk.PhotoImage(Image.open(random.choice(dice)))
# update image
label1.configure(image=image1)
# keep a reference
# Create the ST7789 display:
disp = st7789.ST7789(spi,
cs=cs_pin,
dc=dc_pin,
rst=reset_pin,
baudrate=BAUDRATE,
width=135,
height=240,
x_offset=53,
y_offset=40)
# Create blank image for drawing.
# Make sure to create image with mode 'RGB' for full color.
height = disp.width # we swap height/width to rotate it to landscape!
width = disp.height
image = Image.new('RGB', (width, height))
rotation = 90
# Get drawing object to draw on image.
draw = ImageDraw.Draw(image)
# Draw a black filled box to clear the image.
draw.rectangle((0, 0, width, height), outline=0, fill=(0, 0, 0))
disp.image(image, rotation)
# Draw some shapes.
# First define some constants to allow easy resizing of shapes.
padding = -2
top = padding
bottom = height - padding
# Move left to right keeping track of the current x position for drawing shapes.
x = 0
from tkinter import *
from pil import Image, ImageTk
root = Tk()
root.option_add("*Font", "consolas 25")
images = ['phumin-1', 'phumin-2', 'phumin-3']
imag_list = []
for i, ing in enumerate(images):
imag_list.append(ImageTk.PhotoImage(Image.open(f'{ing}.jpg')))
lbl = Label(image=imag_list[i])
lbl.grid(row=0, column=i)
Label(root, text=f'{ing}.jpg').grid(row=1, column=i)
root.mainloop()
def rolling_dice():
image1 = ImageTk.PhotoImage(Image.open(random.choice(dice)))
# update image
label1.configure(image=image1)
# keep a reference
label1.image = image1
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)
