I want to evaluate if an event is happening in my screen, every time it happens a particular box/image shows up in a screen region with very similar structure.
I have collected a bunch of 84x94 .png RGB images from that screen region and I'd like to build a classifier to tell me if the event is happening or not.
Therefore my idea was to create a pd.DataFrame (df) containing 2 columns, df['np_array'] contains every picture as a np.array and df['is_category'] contains boolean values telling if that image is indicating that the event is happening or not.
The structure looks like this (with != size):
I have resized the images to 10x10 for training and converted to greyscale
df = pd.DataFrame(
{'np_array': [np.random.random((10, 10,2)) for x in range(0,10)],
'is_category': [bool(random.getrandbits(1)) for x in range(0,10)]
})
My problem is that I can't fit a scikit learn classifier by doing clf.fit(df['np_array'],df['is_category'])
I've never tried image recognition before, thanks upfront for any help!
If its a 10x10 grayscale image, you can flatten it:
import numpy as np
from sklearn import ensemble
# generate random 2d arrays
image_data = np.random.rand(10,10, 100)
# generate random labels
labels = np.random.randint(0,2, 100)
X = image_data.reshape(100, -1)
# then use any scikit-learn classification model
clf = ensemble.RandomForestClassifier()
clf.fit(X, y)
By the way, for images the best performing algorithms are convolutional neural networks.