I write a dog-classifier in a Jupyter notebook that, every time a dog is detected in an image, should show the image and print some text describing it. Somehow, the images are always displayed after all the text was printed, no matter in which order I put plt.imshow() and print(). Does anybody know why this is the case?
Thank you!
Here is my code-snippet:
for i in range (0, 1,1):
all_counter+=1
if dog_detector(dog_files_short[i]):
img = image.load_img(dog_files_short[i], target_size=(224, 224))
plt.show()
plt.imshow(img)
time.sleep(5)
print("That's a dog!!!!")
dog_counter+=1
print("______________")
else:
print("______________")
img = image.load_img(dog_files_short[i], target_size=(224, 224))
plt.show()
plt.imshow(img)
print("No Doggo up here :(")
print(ResNet50_predict_labels(dog_files_short[i]))
print("______________")
print((dog_counter/all_counter)*100, "% of the dog pictures are classified as dogs")
The output is like this:
It seems you are using Juypter notebook. This always shows any autogenerated output (like the matplotlib figures) last in the output.
You may use IPython.display.display to display the figures at the position of the output where they belong.
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import display
images = [np.random.rayleigh((i+1)/8., size=(180, 200, 3)) for i in range(4)]
dog_detector = lambda x: np.random.choice([True,False])
dog_counter = 0
for i in range(len(images)):
if dog_detector(images[i]):
dog_counter+=1
fig, ax = plt.subplots(figsize=(3,2))
ax.imshow(images[i])
display(fig)
display("That's a dog!!!!")
display("______________")
else:
display("______________")
fig, ax = plt.subplots(figsize=(3,2))
ax.imshow(images[i])
display(fig)
display("No Doggo up here :(")
display("______________")
perc = (dog_counter/float(len(images)))*100
display("{}% of the dog pictures are classified as dogs".format(perc))
plt.close()
Output:
I tried this in my ipython notebook, if I first call plt.imshow(img) and plt.show() right after I get the image first and the text after.
Related
I found an example that can run normally on my laptop, but there is a problem. When the drawing is finished, a repeated result graph will be drawn again. I want to know how to not display the last repeated image.
import numpy as np
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
def plot_durations(y):
plt.figure(2)
plt.clf()
plt.subplot(211)
plt.plot(y[:,0])
plt.subplot(212)
plt.plot(y[:,1])
if is_ipython:
display.clear_output(wait=True)
display.display(plt.gcf())
x = np.linspace(-10,10,10)
y = []
for i in range(len(x)):
y1 = np.cos(i/(3*3.14))
y2 = np.sin(i/(3*3.14))
y.append(np.array([y1,y2]))
plot_durations(np.array(y))
plt.ioff()
plt.show()
Replacing plt.show() with plt.close() at the end of your code will prevent jupyter notebook from displaying the final plot twice. An explanation is included here.
For example, this code works fine and saves the plt only when the plt.show() is present at the end. or else it just runs without saving any output.
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
def animate(i):
line.set_ydata(np.sin(2*np.pi*i / 50)*np.sin(x))
#fig.canvas.draw() not needed see comment by #tacaswell
plt.savefig(str(i)+".png")
return line,
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1,1)
x = np.linspace(0, 2*np.pi, 200)
line, = ax.plot(x, np.zeros_like(x))
plt.draw()
ani = matplotlib.animation.FuncAnimation(fig, animate, frames=5, repeat=False)
plt.show()
PS - I tried using ani.save(writer="ffmpeg",dpi=200) and it works fine, but later on as I am using ffmpeg commands to convert the image sequence to animation, it shows invalid PNG signature.
Also I am new in this field so my appoligies if i did not follow any proper method.
Thanks in advance.
I try to arrange multiple figures in a jupyter notebook using ipywidget and matplotlib but it's not working as I expect to... When I run cells invidually its working well but when I rerun the whole notebook nothing shows.
You can have a glimpse of what I try to do and help me by finding what's wrong in my whole notebook.
Cell 1
import ipywidgets as widgets
import numpy as np
import matplotlib.pyplot as plt
%matplotlib widget
pi = np.pi
t = np.linspace(0,2*pi,100)
fig1 = plt.figure(1,figsize=(6,2))
ax1 = fig1.add_subplot(1, 1, 1, aspect=1)
def update_plot(phase,amplitude):
ax1.clear()
ax1.plot(t,amplitude*np.sin(t+phase), color='r', label='Sinus')
ax1.legend(loc=0)
phase = widgets.FloatSlider(min=-2,max=2,value=0,description='Phase:')
amplitude = widgets.FloatSlider(min=0,max=8,value=1.5,description='Amplitude:')
widgets.interactive(update_plot, phase=phase, amplitude=amplitude)
Cell2
fig2 = plt.figure(2,figsize=(6,2))
ax2 = fig2.add_subplot(1, 1, 1, aspect=1)
t2 = np.linspace(0,2*pi,100)
def update_plot(phase,amplitude):
ax2.clear()
ax2.plot(t2,amplitude*np.cos(t2+phase), color='b', label='Cosinus')
ax2.legend(loc=0)
phase = widgets.FloatSlider(min=-2,max=2,value=0,description='Phase:')
amplitude = widgets.FloatSlider(min=0,max=8,value=1,description='Amplitude:')
widgets.interactive(update_plot, phase=phase, amplitude=amplitude)
Whole notebook
I don't know how to share notebook so I pasted the ipynb file into pastebin.
https://pastebin.com/UdK4jzHK
To see plots you can just add
fig1
fig2 etc at the end of each figure cell. notebook example
PS: you can always share your notebook on Collab: https://colab.research.google.com/
I've plotted a confusion matrix with scikit-learn / matplotlib thanks to different code examples I found on the web, but I'm stuck at finding how to add space between the xticklabels and the main title. As you can see on the image below, the plot title and the xticklabels are overlapping (+ the ylabel 'True' is cut out).
Link to my confusion matrix image
Here is the function I use:
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
PLOTS = '/plots/' # Output folder
def plt_confusion_matrix(y_test, y_pred, normalize=False, title="Confusion matrix"):
"""
Plots a nice confusion matrix.
:param y_test: list of predicted labels
:param y_pred: list of labels that should have been predicted.
:param normalize: boolean. If False, the plots shows the number of sentences predicted.
If True, shows the percentage of sentences predicted.
:param title: string. Title of the plot.
:return: Nothing but saves the plot as a PNG file and shows it.
"""
labels = list(set(y_pred))
cm = confusion_matrix(y_test, y_pred, labels)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm, cmap=plt.cm.binary, interpolation='nearest')
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
fig.suptitle(title, fontsize=14, wrap=True)
fig.colorbar(cax)
ax.set_xticklabels([''] + labels, rotation=45)
ax.set_yticklabels([''] + labels)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.subplots_adjust(hspace=0.6)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 1.5 if normalize else cm.max() / 2
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
plt.savefig(PLOTS + title)
plt.show()
I had to rotate the xticklabels because they are too long and otherwise overlapping each other horizontally, and I had to wrap the title because it is also too long and could not otherwise be displayed entirely in the image.
I've seen in another post that xticklabels can also be placed underneath the figure (like in this stackoverflow post), so maybe it could be a solution, however I haven't understood how to make it.
How do I solve the problem?
either to add some space between the title and the xticklabels
(making them appear entirely btw);
or to make the ylabel 'True' visible
or to move the xticklabels under the figure.
Edit : I tried both of geekzeus solutions, without success...
Result with geekzeus' 1st solution : See confusion matrix
Result with geekzeus' 2nd solution : See confusion matrix
Do it like this
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix')
#xaxisticks
ax.xaxis.set_ticklabels(['A', 'B'])
#yaxis ticks
ax.yaxis.set_ticklabels(['B', 'A'])
OR
use seaborn with matplotlib,you can also directly provide list variable to ticks
import seaborn as sns
import matplotlib.pyplot as plt
cm = confusion_matrix(true_classes, predicted_classes)
ax= plt.subplot()
sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells
# labels, title and ticks
ax.set_xlabel('Predicted labels')
ax.set_ylabel('True labels')
ax.set_title('Confusion Matrix')
ax.xaxis.set_ticklabels(['A', 'B'])
ax.yaxis.set_ticklabels(['B', 'A'])
You can specify the location of the title using parameters x and y. If you tweak the values of y, the desired plot can be generated.
fig.suptitle(title, fontsize=14, wrap=True, y=1.2)
let say I have this code:
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
plt.show()
the result figure has too much info and now I want to pick 1 of the axes and draw it alone in a new figure
I tried doing something like this
def on_click(event):
axes = event.inaxes.get_axes()
fig2 = plt.figure(15)
fig2.axes.append(axes)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
but it didn't quite work. what would be the correct way to do it? searching through the docs and throw SE gave hardly any useful result
edit:
I don't mind redrawing the chosen axes, but I'm not sure how can I tell which of the axes was chosen so if that information is available somehow then it is a valid solution for me
edit #2:
so I've managed to do something like this:
def on_click(event):
fig2 = plt.figure(15)
fig2.clf()
for line in event.inaxes.axes.get_lines():
xydata = line.get_xydata()
plt.plot(xydata[:, 0], xydata[:, 1])
fig2.show()
which seems to be "working" (all the other information is lost - labels, lines colors, lines style, lines width, xlim, ylim, etc...)
but I feel like there must be a nicer way to do it
thanks
Copying the axes
The inital answer here does not work, we keep it for future reference and also to see why a more sophisticated approach is needed.
#There are some pitfalls on the way with the initial approach.
#Adding an `axes` to a figure can be done via `fig.add_axes(axes)`. However, at this point,
#the axes' figure needs to be the figure the axes should be added to.
#This may sound a bit like running in circles but we can actually set the axes'
#figure as `axes.figure = fig2` and hence break out of this.
#One might then also position the axes in the new figure to take the usual dimensions.
#For this a dummy axes can be added first, the axes can change its position to the position
#of the dummy axes and then the dummy axes is removed again. In total, this would look as follows.
import matplotlib.pyplot as plt
import numpy as np
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
def on_click(event):
axes = event.inaxes
if not axes: return
fig2 = plt.figure()
axes.figure=fig2
fig2.axes.append(axes)
fig2.add_axes(axes)
dummy = fig2.add_subplot(111)
axes.set_position(dummy.get_position())
dummy.remove()
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
#So far so good, however, be aware that now after a click the axes is somehow
#residing in both figures, which can cause all sorts of problems, e.g. if you
# want to resize or save the initial figure.
Instead, the following will work:
Pickling the figure
The problem is that axes cannot be copied (even deepcopy will fail). Hence to obtain a true copy of an axes, you may need to use pickle. The following will work. It pickles the complete figure and removes all but the one axes to show.
import matplotlib.pyplot as plt
import numpy as np
import pickle
import io
num_rows = 10
num_cols = 1
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in range(num_rows):
ax = axs[i]
ax.plot(np.arange(10), np.arange(10)**i)
def on_click(event):
if not event.inaxes: return
inx = list(fig.axes).index(event.inaxes)
buf = io.BytesIO()
pickle.dump(fig, buf)
buf.seek(0)
fig2 = pickle.load(buf)
for i, ax in enumerate(fig2.axes):
if i != inx:
fig2.delaxes(ax)
else:
axes=ax
axes.change_geometry(1,1,1)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
Recreate plots
The alternative to the above is of course to recreate the plot in a new figure each time the axes is clicked. To this end one may use a function that creates a plot on a specified axes and with a specified index as input. Using this function during figure creation as well as later for replicating the plot in another figure ensures to have the same plot in all cases.
import matplotlib.pyplot as plt
import numpy as np
num_rows = 10
num_cols = 1
colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
labels = ["Label {}".format(i+1) for i in range(num_rows)]
def myplot(i, ax):
ax.plot(np.arange(10), np.arange(10)**i, color=colors[i])
ax.set_ylabel(labels[i])
fig, axs = plt.subplots(num_rows, num_cols, sharex=True)
for i in xrange(num_rows):
myplot(i, axs[i])
def on_click(event):
axes = event.inaxes
if not axes: return
inx = list(fig.axes).index(axes)
fig2 = plt.figure()
ax = fig2.add_subplot(111)
myplot(inx, ax)
fig2.show()
fig.canvas.mpl_connect('button_press_event', on_click)
plt.show()
If you have, for example, a plot with three lines generated by the function plot_something, you can do something like this:
fig, axs = plot_something()
ax = axs[2]
l = list(ax.get_lines())[0]
l2 = list(ax.get_lines())[1]
l3 = list(ax.get_lines())[2]
plot(l.get_data()[0], l.get_data()[1])
plot(l2.get_data()[0], l2.get_data()[1])
plot(l3.get_data()[0], l3.get_data()[1])
ylim(0,1)