Overlaying a box on label image using Tkinter - image

I am using Tkinter and the grid() layout manager to create a GUI. I am showing the image in my GUI using a label, on a tabbed window:
label2 = ttk.Label(tab2)
image2 = PhotoImage(file="lizard.gif")
label2['image'] = image2
label2.grid(column=0, row=0, columnspan=3)
For illustration, let's say the image is 300 x 900. If I know a set of coordinates within the image, how can I overlay a shaded box on the image, defined by the known (A,B,C,D which are shown just for the illustration purpose) coordinates?

Let me give you a step by step solution.
You can use a tkinter.Label() to display your image as you did, you can also choose other widgets. But for situation, let's choose tkinter.Canvas() widget instead (but same reasoning is valid if you choose to use tkinter.Label())
Technical issues:
Your problem contains 2 main sub-problems to resolve:
How to overlay 2 images the way you want.
How to display an image using tkinter.Canvas()
To be able to read an image of jpg format , you need to use a specific PIL (or its Pillow fork) method and a class:
PIL.Image.open()
PIL.ImageTk.PhotoImage()
This is done by 3 lines in the below program:
self.im = Image.open(self.saved_image)
self.photo = ImageTk.PhotoImage(self.im)
And then display self.photo in the self.canvas widget we opted for:
self.canvas.create_image(0,0, anchor=tk.N+tk.W, image = self.photo)
Second, to reproduce the effect you desire, use cv2.addWeighted() OpenCV method. But I feel you have already done that. So I just show you the portion of code of the program that does it:
self.img = cv2.imread(self.image_to_read)
self.overlay = self.img.copy()
cv2.rectangle(self.overlay, (500,50), (400,100), (0, 255, 0), -1)
self.opacity = 0.4
cv2.addWeighted(self.overlay, self.opacity, self.img, 1 - self.opacity, 0, self.img)
cv2.imwrite( self.saved_image, self.img)
Program design:
I use 2 methods:
- __init__(): Prepare the frame and call the GUI initialization method.
- initialize_user_interface(): Draw the GUI and perform the previous operations.
But for scalability reasons, it is better to create a separate method to handle the different operations of the image.
Full program (OpenCV + tkinter)
Here is the source code (I used Python 3.4):
'''
Created on Apr 05, 2016
#author: Bill Begueradj
'''
import tkinter as tk
from PIL import Image, ImageTk
import cv2
import numpy as np
import PIL
class Begueradj(tk.Frame):
'''
classdocs
'''
def __init__(self, parent):
'''
Prepare the frame and call the GUI initialization method.
'''
tk.Frame.__init__(self, parent)
self.parent=parent
self.initialize_user_interface()
def initialize_user_interface(self):
"""Draw a user interface allowing the user to type
"""
self.parent.title("Bill BEGUERADJ: Image overlay with OpenCV + Tkinter")
self.parent.grid_rowconfigure(0,weight=1)
self.parent.grid_columnconfigure(0,weight=1)
self.image_to_read = 'begueradj.jpg'
self.saved_image = 'bill_begueradj.jpg'
self.img = cv2.imread(self.image_to_read)
self.overlay = self.img.copy()
cv2.rectangle(self.overlay, (500,50), (400,100), (0, 255, 0), -1)
self.opacity = 0.4
cv2.addWeighted(self.overlay, self.opacity, self.img, 1 - self.opacity, 0, self.img)
cv2.imwrite( self.saved_image, self.img)
self.im = Image.open(self.saved_image)
self.photo = ImageTk.PhotoImage(self.im)
self.canvas = tk.Canvas(self.parent, width = 580, height = 360)
self.canvas.grid(row = 0, column = 0)
self.canvas.create_image(0,0, anchor=tk.N+tk.W, image = self.photo)
def main():
root=tk.Tk()
d=Begueradj(root)
root.mainloop()
if __name__=="__main__":
main()
Demo:
This is a screenshot of the running program:

You will need to use a canvas widget. That will allow you to draw an image, and then overlay a rectangle on it.

Although the above answers were wonderfully in depth, they did not fit my exact situation (Specifically use of Python 2.7, etc.). However, this solution gave me exactly what I was looking for:
canvas = Canvas(tab2, width=875, height=400)
image2=PhotoImage(file='lizard.gif')
canvas.create_image(440,180,image=image2)
canvas.grid(column=0, row=0, columnspan=3)
The rectangle is added over the canvas using:
x1 = 3, y1 = 10, x2 = 30, y2 = 20
canvas.create_rectangle(x1, y1, x2, y2, fill="blue", stipple="gray12")
stipple comes from this example, to help add transparency to the rectangle.

Related

How can I classify different images with various sizes and formats in scikit-learn?

I'm trying to build a simple image classifier using scikit-learn. I'm hoping to avoid having to resize and convert each image before training.
Question
Given two different images that are different formats and sizes (1.jpg and 2.png), how can I avoid a ValueError while fitting the model?
I have one example where I train using only 1.jpg, which fits successfully.
I have another example where I train using both 1.jpg and 2.png and a ValueError is produced.
This example will fit successfully:
import numpy as np
from sklearn import svm
import matplotlib.image as mpimg
target = [1, 2]
images = np.array([
# target 1
[mpimg.imread('./1.jpg'), mpimg.imread('./1.jpg')],
# target 2
[mpimg.imread('./1.jpg'), mpimg.imread('./1.jpg')],
])
n_samples = len(images)
data = images.reshape((n_samples, -1))
model = svm.SVC()
model.fit(data, target)
This example will raise a Value error.
Observe the different 2.png image in target 2.
import numpy as np
from sklearn import svm
import matplotlib.image as mpimg
target = [1, 2]
images = np.array([
# target 1
[mpimg.imread('./1.jpg'), mpimg.imread('./1.jpg')],
# target 2
[mpimg.imread('./2.png'), mpimg.imread('./1.jpg')],
])
n_samples = len(images)
data = images.reshape((n_samples, -1))
model = svm.SVC()
model.fit(data, target)
# ValueError: setting an array element with a sequence.
1.jpg
2.png
For this, I would really recommend using the tools in Keras that are specifically designed to preprocess images in a highly scalable and efficient way.
from keras.preprocessing.image import ImageDataGenerator
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
1 Determine the target size of your new pictures
h,w = 150,150 # desired height and width
batch_size = 32
N_images = 100 #total number of images
Keras works in batches, so batch_size just determines how many pictures at once will be processed (this does not impact your end result, just the speed).
2 Create your Image Generator
train_datagen = ImageDataGenerator(
rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'Pictures_dir',
target_size=(h, w),
batch_size=batch_size,
class_mode = 'binary')
The object that is going to do the image extraction is ImageDataGenerator. It has the method flow_from_directory which I believe might be useful for you here. It will read the content of the folder Pictures_dir and expect your images to be in folders by class (eg: Pictures_dir/class0 and Pictures_dir/class1). The generator, when called, will then create images from these folders and also import their label (in this example, 'class0' and 'class1').
There are plenty of other arguments to this generator, you can check them out in the Keras documentation (especially if you want to do data augmentation).
Note: this will take any image, be it PNG or JPG, as you requested
If you want to get the mapping from class names to label indices, do:
train_generator.class_indices
# {'class0': 0, 'class1': 1}
You can check what is going on with
plt.imshow(train_generator[0][0][0])
3 Extract all resized images from the Generator
Now you are ready to extract the images from the ImageGenerator:
def extract_images(generator, sample_count):
images = np.zeros(shape=(sample_count, h, w, 3))
labels = np.zeros(shape=(sample_count))
i = 0
for images_batch, labels_batch in generator: # we are looping over batches
images[i*batch_size : (i+1)*batch_size] = images_batch
labels[i*batch_size : (i+1)*batch_size] = labels_batch
i += 1
if i*batch_size >= sample_count:
# we must break after every image has been seen once, because generators yield indifinitely in a loop
break
return images, labels
images, labels = extract_images(train_generator, N_images)
print(labels[0])
plt.imshow(images[0])
Now you have your images all at the same size in images, and their corresponding labels in labels, which you can then feed into any scikit-learn classifier of your choice.
Its difficult because of the math operations behind the scene, (the details are out of scope) if you manage do so, lets say you build your own algorithm, still you would not get the desired result.
i had this issue once with faces with different sizes. maybe this piece of code give you starting point.
from PIL import Image
import face_recognition
def face_detected(file_address = None , prefix = 'detect_'):
if file_address is None:
raise FileNotFoundError('File address required')
image = face_recognition.load_image_file(file_address)
face_location = face_recognition.face_locations(image)
if face_location:
face_location = face_location[0]
UP = int(face_location[0] - (face_location[2] - face_location[0]) / 2)
DOWN = int(face_location[2] + (face_location[2] - face_location[0]) / 2)
LEFT = int(face_location[3] - (face_location[3] - face_location[2]) / 2)
RIGHT = int(face_location[1] + (face_location[3] - face_location[2]) / 2)
if UP - DOWN is not LEFT - RIGHT:
height = UP - DOWN
width = LEFT - RIGHT
delta = width - height
LEFT -= int(delta / 2)
RIGHT += int(delta / 2)
pil_image = Image.fromarray(image[UP:DOWN, LEFT:RIGHT, :])
pil_image.thumbnail((50, 50), Image.ANTIALIAS)
pil_image.save(prefix + file_address)
return True
pil_image = Image.fromarray(image)
pil_image.thumbnail((200, 200), Image.ANTIALIAS)
pil_image.save(prefix + file_address)
return False
Note : i wrote this long time ago maybe not a good practice

setting contrast of image in PyQtGraph GraphicsWindow

I'm building a GUI and I want to allow the user to change the contrast of an image displayed through a PyQtGraph GraphicsWindow (say using a scroll bar or something). Any ideas?
My code looks something like this:
gw = pg.GraphicsWindow(size=(OCT_WIDTH, OCT_HEIGHT), border=True)
gw_layout = gw.addLayout(row=0, col=0)
gw_view = gw_layout.addViewBox(row=1, col=0, lockAspect=True)
img = imread(imgPath)
imgItem = pg.ImageItem(img)
gw_view.addItem(imgItem)
The ImageView widget contains a color bar that can be used to modify the image contrast or intensity. See the ImageView example.
For convenience and future reference I copied the ImageView.py example here below.
# -*- coding: utf-8 -*-
"""
This example demonstrates the use of ImageView, which is a high-level widget for
displaying and analyzing 2D and 3D data. ImageView provides:
1. A zoomable region (ViewBox) for displaying the image
2. A combination histogram and gradient editor (HistogramLUTItem) for
controlling the visual appearance of the image
3. A timeline for selecting the currently displayed frame (for 3D data only).
4. Tools for very basic analysis of image data (see ROI and Norm buttons)
"""
## Add path to library (just for examples; you do not need this)
import initExample
import numpy as np
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph as pg
# Interpret image data as row-major instead of col-major
pg.setConfigOptions(imageAxisOrder='row-major')
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QMainWindow()
win.resize(800,800)
imv = pg.ImageView()
win.setCentralWidget(imv)
win.show()
win.setWindowTitle('pyqtgraph example: ImageView')
## Create random 3D data set with noisy signals
img = pg.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
img = img[np.newaxis,:,:]
decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
data = np.random.normal(size=(100, 200, 200))
data += img * decay
data += 2
## Add time-varying signal
sig = np.zeros(data.shape[0])
sig[30:] += np.exp(-np.linspace(1,10, 70))
sig[40:] += np.exp(-np.linspace(1,10, 60))
sig[70:] += np.exp(-np.linspace(1,10, 30))
sig = sig[:,np.newaxis,np.newaxis] * 3
data[:,50:60,30:40] += sig
## Display the data and assign each frame a time value from 1.0 to 3.0
imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]))
## Set a custom color map
colors = [
(0, 0, 0),
(45, 5, 61),
(84, 42, 55),
(150, 87, 60),
(208, 171, 141),
(255, 255, 255)
]
cmap = pg.ColorMap(pos=np.linspace(0.0, 1.0, 6), color=colors)
imv.setColorMap(cmap)
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
QtGui.QApplication.instance().exec_()

Matplotlib Subplot into Tkinter GUI window

I'm working on a Tkinter GUI and I was wondering if it was possible to put a Subplot into a Tkinter GUI. Any help would be appreciated as I currently have no idea.
import pandas.io.data as web
import matplotlib.pyplot as plt
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2014, 8, 3)
google = web.DataReader("GOOG", 'yahoo', start, end )
ax1 = plt.subplot2grid((4,4), (0,0), colspan=4)
ax2 = plt.subplot2grid((4,4), (1,0), colspan=2)
top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4)
top.plot(google.index, google["Close"])
plt.title('Google Stock Price from 2007 - 2012')
bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)
bottom.bar(google.index, google['Volume'])
plt.title('Google Trading Volume in Millions')
plt.gcf().set_size_inches(15,8)
plt.show()
I'm working with something around this, but I haven't been able to place it into the GUI without it being an entirely separate window.
self.root2= Tk()
self.root2.geometry("600x400")
self.root2.title("Stock Visualization")
frame = Frame(self.root2)
frame.grid(row=2,column=0, sticky="s")
frame2 = Frame(self.root2)
frame2.grid(row=0,column=0, sticky = "n")
## self.canvas=Canvas(self.root2, width=300, height=300, background='white')
## self.canvas.grid(row=1,column=0, columnspan = 4)
This is part of the frame, without all the labels and such around. I have that Canvas commented out where I would want the Subplot to go.
This might be a little late, but you should try something along the lines of this
fig = Figure(figsize=(5,4), dpi = 100)
ax = fig.add_subplot(111)
figcanvas = FigureCanvasTkAgg(fig, master = root)
plotthis(figcanvas, ax)#in this example plotthis is a function that plots the figure, ax being a graph, and figcanvas being the canvas of which the graph is being plotted to.
figcanvas.get_tk_widget().grid()
Just as a little heads up, I don't think this method of doing this works in 3 yet, so as of now, I would just do it in 2 if at all possible. No clue as to why it doesn't work in 3. Hope this helps :)

images in layers with movement and transparency - wxpython

It might sound unproper 'images in layers' like in Photoshop but in fact that is what I have and would like to make working.
I use a set of wx.boxsizer's to have a nice&organized screen after launching my program.
In one row of Horizontal wx.BoxSizer's I have 3 columns done with different wx.Panels and each of that is containing moving wx.StaticBitmaps done by a Timer function.
The second wx.Panel which is in the middle with 0 proportion to mantain original panel size contains 2 images actually, one PNG with transparency and the moving wx.StaticBitmap which should be the background for this PNG.
This is not working out for me. I simply want that the PNG to be over the other image which is moved by the timer. 2 layers if you like.
It is creating a nice very simple and basic action effect (like the object in the PNG is running)
Now I thought of a few ways to go about this but none of them worked:
Figure out how python decides which image to bring in front and which would stay behind. Manipulate that
Keep the moving image in the sizer and take out the PNG and place it over the moving image (than I need to dynamically determine where is that moving image)
Make the moving image the wx.panel background and then probably I can simply call the transparent PNG over it.
I will paste here a part of my script for the brave eyes:
class AnimationPanel(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent)
self.loc = wx.Image("intro/runner.png",wx.BITMAP_TYPE_PNG).ConvertToBitmap()
self.locpic = wx.StaticBitmap(self, -1, self.loc, (0, 0), (self.loc.GetWidth(), self.loc.GetHeight()))
self.xer = 3080
self.xer2 = 2310
self.xer3 = 1540
self.env = wx.Image("intro/environ1.png",wx.BITMAP_TYPE_PNG).ConvertToBitmap()
self.env2 = wx.Image("intro/environ2.png",wx.BITMAP_TYPE_PNG).ConvertToBitmap()
self.env3 = wx.Image("intro/environ3.png",wx.BITMAP_TYPE_PNG).ConvertToBitmap()
self.picture = wx.StaticBitmap(self, -1, self.env, (0, 0), (self.env.GetWidth(), self.env.GetHeight()))
self.picture2 = wx.StaticBitmap(self, -1, self.env2, (770, 0), (self.env2.GetWidth(), self.env2.GetHeight()))
self.picture3 = wx.StaticBitmap(self, -1, self.env3, (1540, 0), (self.env3.GetWidth(), self.env3.GetHeight()))
self.timer = wx.Timer(self)
self.Bind(wx.EVT_TIMER, self.OnTimer, self.timer)
self.timer.Start(5)
def OnTimer(self, event):
if self.xer <= 3080:
self.xer += 1
self.picture.Move((self.xer,0))
else:
self.xer = -770
if self.xer2 <= 3080:
self.xer2 += 1
self.picture2.Move((self.xer2,0))
else:
self.xer2 = -770
if self.xer3 <= 3080:
self.xer3 += 1
self.picture3.Move((self.xer3,0))
else:
self.xer3 = -770
This is in the main frame:
ap = AnimationPanel(self)
v2box = wx.BoxSizer(wx.HORIZONTAL)
v2box.Add(someother, 1, wx.EXPAND)
v2box.Add(ap, 0, wx.EXPAND)
v2box.Add(someother, 1, wx.EXPAND)
I did put research in this but I'm quite of a beginner so please help me out with some simple tips or suggestions if it's possible.
Thanks.
In the wxpython demo install there is a samples folder that has a pySketch demo.
In this code it creates dc drawn objects that can be moved in front and behind other objects.
A quick look over this code it looks like the drawn items are stored in a list and then drawn in the list order.
I guess that's how you would implement your layering, you would have a list of layers and in each layer you would store your items for that layer.

Setting correct limits with imshow if image data shape changes

I have a 3D array, of which the first two dimensions are spatial, so say (x,y). The third dimension contains point-specific information.
print H.shape # --> (200, 480, 640) spatial extents (200,480)
Now, by selecting a certain plane in the third dimension, I can display an image with
imdat = H[:,:,100] # shape (200, 480)
img = ax.imshow(imdat, cmap='jet',vmin=imdat.min(),vmax=imdat.max(), animated=True, aspect='equal')
I want to now rotate the cube, so that I switch from (x,y) to (y,x).
H = np.rot90(H) # could also use H.swapaxes(0,1) or H.transpose((1,0,2))
print H.shape # --> (480, 200, 640)
Now, when I call:
imdat = H[:,:,100] # shape (480,200)
img.set_data(imdat)
ax.relim()
ax.autoscale_view(tight=True)
I get weird behavior. The image along the rows displays the data till 200th row, and then it is black until the end of the y-axis (480). The x-axis extends from 0 to 200 and shows the rotated data. Now on, another rotation by 90-degrees, the image displays correctly (just rotated 180 degrees of course)
It seems to me like after rotating the data, the axis limits, (or image extents?) or something is not refreshing correctly. Can somebody help?
PS: to indulge in bad hacking, I also tried to regenerate a new image (by calling ax.imshow) after each rotation, but I still get the same behavior.
Below I include a solution to your problem. The method resetExtent uses the data and the image to explicitly set the extent to the desired values. Hopefully I correctly emulated the intended outcome.
import matplotlib.pyplot as plt
import numpy as np
def resetExtent(data,im):
"""
Using the data and axes from an AxesImage, im, force the extent and
axis values to match shape of data.
"""
ax = im.get_axes()
dataShape = data.shape
if im.origin == 'upper':
im.set_extent((-0.5,dataShape[0]-.5,dataShape[1]-.5,-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((dataShape[1]-.5,-.5))
else:
im.set_extent((-0.5,dataShape[0]-.5,-.5,dataShape[1]-.5))
ax.set_xlim((-0.5,dataShape[0]-.5))
ax.set_ylim((-.5,dataShape[1]-.5))
def main():
fig = plt.gcf()
ax = fig.gca()
H = np.zeros((200,480,10))
# make distinguishing corner of data
H[100:,...] = 1
H[100:,240:,:] = 2
imdat = H[:,:,5]
datShape = imdat.shape
im = ax.imshow(imdat,cmap='jet',vmin=imdat.min(),
vmax=imdat.max(),animated=True,
aspect='equal',
# origin='lower'
)
resetExtent(imdat,im)
fig.savefig("img1.png")
H = np.rot90(H)
imdat = H[:,:,0]
im.set_data(imdat)
resetExtent(imdat,im)
fig.savefig("img2.png")
if __name__ == '__main__':
main()
This script produces two images:
First un-rotated:
Then rotated:
I thought just explicitly calling set_extent would do everything resetExtent does, because it should adjust the axes limits if 'autoscle' is True. But for some unknown reason, calling set_extent alone does not do the job.

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