Intro to OpenCV with Python

Installation To work with OpenCV from python, you need to install it first: pip install opencv-python Reading Images from file After we import cv2 we can directly work with images like so: import cv2 img = cv2.imread(“doc_brown.png”) For showing the image, it is recommended to use matplotlib import matplotlib.pyplot as plt img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)…

Python data classes

A cool new feature made its way into Python 3.7: Data classes. When You’ve already read my article about Lombok the concept isn’t so new at all: With the new decorator @dataclass You can save a huge amount of time because the methods __init__() __repr__() __eq__() are created for you! from dataclasses import dataclass @dataclass…

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Confusion Matrix

Too confused of the confusion matrix? Let me bring some clarity into this topic! Let’s take the example from Precision and Recall: y_true = [“dog”, “dog”, “non-dog”, “non-dog”, “dog”, “dog”] y_pred = [“dog”, “non-dog”, “dog”, “non-dog”, “dog”, “non-dog”] When we look at the prediction we can count the correct and incorrect classifications: dog correctly classified…

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numpy random choice

With numpy you can easily create test data with random_integers and randint. numpy.random.randint(low, high=None, size=None, dtype=’l’) numpy.random.random_integers(low, high=None, size=None) random_integers includes the high boundary while randint does not. >>> import numpy as np >>> np.random.random_integers(5) 4 >>> np.random.random_integers(5, size=(5)) array([5, 3, 4, 1, 4]) >>>np.random.random_integers(5, size=(5, 4)) array([[2, 3, 3, 5], [1, 3, 1, 3],…

Classification: Precision and Recall

In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean? Clarification Living together with little kids You very often run into classification issues: My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a…

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UD120 – Intro to Machine Learning

One part of my bucket list for 2018 is finishing the Udacity Course UD120: Intro to Machine Learning. The host of this course are Sebastian Thrun, ex-google-X and founder of Udacity and Katie Malone, creator of the Linear digressions podcast. The course consists of 17 lessons. Every lesson has a couple of hours of video…