/PyImageSearch-CV-DL-CrashCourse

Repository for PyImageSearch Crash Course on Computer Vision and Deep Learning

Primary LanguagePythonMIT LicenseMIT

PyImageSearch CV/DL CrashCourse

Repository for PyImageSearch Crash Course on Computer Vision and Deep Learning

Day 1: Face detection with OpenCV and Deep Learning

Commands used:

  • Object detection with Images:

    $ python detect_faces.py --image images/rooster.jpg --prototxt model/deploy.prototxt.txt --model model/res10_300x300_ssd_iter_140000.caffemodel

  • Object detection with Webcam:

    $ python detect_faces_video.py --prototxt model/deploy.prototxt.txt --model model/res10_300x300_ssd_iter_140000.caffemodel

Day 2: OpenCV Tutorial: A Guide to Learn OpenCV

Commands used:

  • OpenCV tutorial:

$ python opencv_tutorial_01.py

  • Counting objects:

$ python opencv_tutorial_02.py --image images/tetris_blocks.png

Day 3: Document scanner

Commands used:

$ python scan.py --image images/page.jpg

Day 4: Bubble sheet multiple choice scanner and test grader using OMR

Commands used:

$ python test_grader.py --image images/test_01.png

Day 5: Ball Tracking with OpenCV

Commands used:

  • Using Video:

    $ python ball_tracking.py --video ball_tracking_example.mp4

  • Using Webcam:

    $ python ball_tracking.py (Note: To see any results, you will need a green object with the same HSV color range was used in this demo)

Day 6: Measuring size of objects in an image with OpenCV

Commands used:

$ python object_size.py --image images/example_01.png --width 0.955

$ python object_size.py --image images/example_02.png --width 0.955

$ python object_size.py --image images/example_03.png --width 3.5

Day 8: Facial landmarks with dlib, OpenCV, and Python

Commands used:

$ python facial_landmarks.py --shape-predictor model/shape_predictor_68_face_landmarks.dat --image images/example_01.jpg

$ python facial_landmarks.py --shape-predictor model/shape_predictor_68_face_landmarks.dat --image images/example_02.jpg

$ python facial_landmarks.py --shape-predictor model/shape_predictor_68_face_landmarks.dat --image images/example_03.jpg

Day 9: Eye blink detection with OpenCV, Python, and dlib

Commands used:

$ python detect_blinks.py --shape-predictor model/shape_predictor_68_face_landmarks.dat --video videos/blink_detection_demo.mp4

Day 10: Drowsiness detection with OpenCV

Commands used:

$ python detect_drowsiness.py --shape-predictor model/shape_predictor_68_face_landmarks.dat --alarm sounds/alarm.wav

Day 12: A simple neural network with Python and Keras

Note: Create a folder structure called /kaggle_dogs_vs_cats/train, download the training dataset Kaggle-Dogs vs. Cats and put the images into train folder.

Command used - Training:

$ python simple_neural_network.py --dataset kaggle_dogs_vs_cats --model output/simple_neural_network.hdf5

Command used - Test:

$ python test_network.py --model output/simple_neural_network.hdf5 --test-images test_images

Day 13: Deep Learning with OpenCV

Commands used:

$ python deep_learning_with_opencv.py --image images/jemma.png --prototxt model/bvlc_googlenet.prototxt --model model/bvlc_googlenet.caffemodel --labels model/synset_words.txt

$ python deep_learning_with_opencv.py --image images/traffic_light.png --prototxt model/bvlc_googlenet.prototxt --model model/bvlc_googlenet.caffemodel --labels model/synset_words.txt

$ python deep_learning_with_opencv.py --image images/eagle.png --prototxt model/bvlc_googlenet.prototxt --model model/bvlc_googlenet.caffemodel --labels model/synset_words.txt

$ python deep_learning_with_opencv.py --image images/vending_machine.png --prototxt model/bvlc_googlenet.prototxt --model model/bvlc_googlenet.caffemodel --labels model/synset_words.txt

Day 14: How to (quickly) build a deep learning image dataset

Commands used:

$ python search_bing_api.py --query "pokemon_class_to_search" --output dataset/pokemon_class_to_search

Day 15: Keras and Convolutional Neural Networks (CNNs)

Command used - Training:

$ python train.py --dataset dataset --model pokedex.model --labelbin lb.pickle

Command used - Testing:

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/charmander_counter.png

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/bulbasaur_plush.png

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/mewtwo_toy.png

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/pikachu_toy.png

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/squirtle_plush.png

$ python classify.py --model pokedex.model --labelbin lb.pickle --image examples/charmander_hidden.png

Day 16: Real-time object detection with deep learning and OpenCV

Commands used:

$ python real_time_object_detection.py --prototxt model/MobileNetSSD_deploy.prototxt.txt --model model/MobileNetSSD_deploy.caffemodel


Credits to Adrian Rosebrock on http://www.pyimagesearch.com