Gain insights into using Python for scientific and engineering applications. Delve into real-world scenarios, learning NumPy, Matplotlib, audio processing, and more through practical, hands-on projects.

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jupter.tar.gz

notebook

obesity_plot_gender

print_gender_pandas

create_sine_wave

notebook_exercise

range

print_gender_pandas_remove_NaN

print_gender_year

print_data_age

print_data_age_with_year

print_data_age_remove_NaN

obesity_plot_age_simple

obesity_plot_age_clear

print_data_age_under_16

print_data_age_25_34

obesity_plot_children_vs_adults

print_movie_top_rated

print_movies_oldie_teen

print_movies_by_gender

print_movies_for_women

print_movies_by_gender_loc

print_movies_diff_gender

movies_diff_gender_plot

print_u_data

print_movies_oldie_index

print_movies_oldies

print_movies_teens

display_image_nouse

grey_image

blur_image

edge_detection_low

edge_detection_high

plot_sin_wave

create_wave_file

display_again

print_frequencies

count_objects

detect_faces

detect_faces_little_mix

count_exact_lines

webcam_detection

single_thread

find_correlation

webcam_face_detection

motion_detection

motion_detection_live

In this course, you will learn to use Python for real-world scientific/engineering applications. For each topic, there will be a real case scenario where you will build a quick solution in Python to solve the problem.

More specifically, you will cover topics such as creating a word counter, NumPy and Matplotlib, audio processing, and a lot more. Throughout each chapter, you will get hands-on experience in building solutions for complex problems that engineers and scientists face every day.

The Practical Guide to Python for Scientists and Engineers

# How to detect motion using frames

In this lesson, we’ll detect motion in a video using two consecutive frames and finding the differences between them. If the difference is minor, that means no motion occurred. If we find a significant difference between frames, then a motion must have occurred.

Look at lines 1–10 of the code block below. In this code, we’re creating a video capture instance. Then we’ll read the last and the current frames and convert them to grayscale in lines 13–15.

Here we’ll read two frames and convert them to grayscale. It might seem like we’re doing this outside the while loop. This is because we want two consecutive frames captured before the main loop starts. We’ll call them `last_frame` and `current_frame`. Why do we need two to see the difference between them.

In line 18 of the code block below, we’ll enter our “while” loop and store the `current_frame` as the `last_frame`. That’s because for each loop iteration, the `current_frame` from the previous iteration will become the `last_frame` of this iteration.

Now in line 26, we read a new frame and convert it to grayscale.

Next in line 28, we use the inbuilt `absdiff()` to find the absolute difference between consecutive frames.

## Difference between two consecutive frames

Now, we have a code that will show us what the difference is. Before that, we must understand that OpenCv video and image frames are NumPy arrays that contain the values of all the pixels in the image or video. If we want, we can print the whole array as shown in the code below.





Learn how to detect motion using a video in Python.

Motion Detection

Introduction

Create a Word Counter in Python

An Introduction to NumPy and Matplotlib

Python pandas

Audio Processing

Image and Video Processing

MultiThreading vs. Multiprocessing in Python

Machine Learning with an Amazon-like Recommendation Engine

Conclusion

Motion Detection

How to detect motion using frames