Repository for PyImageSearch Crash Course on Computer Vision and Deep Learning

• URL to course: https://www.pyimagesearch.com/welcome-crash-course/

• Link: https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/
• Folder: 01-deep-learning-face-detection

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

• Link: https://www.pyimagesearch.com/2018/07/19/opencv-tutorial-a-guide-to-learn-opencv/
• Folder: 02-opencv-tutorial

Commands used:

• OpenCV tutorial:

$ python opencv_tutorial_01.py

• Counting objects:

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

• Link: https://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/
• Folder: 03-document-scanner

Commands used:

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

• Link: https://www.pyimagesearch.com/2016/10/03/bubble-sheet-multiple-choice-scanner-and-test-grader-using-omr-python-and-opencv/
• Folder: 04-omr-test-grader

Commands used:

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

• Link: https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/
• Folder: 05-ball-tracking

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)

• Link: https://www.pyimagesearch.com/2016/03/28/measuring-size-of-objects-in-an-image-with-opencv/
• Folder: 06-size-of-objects

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

• Link: https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/
• Folder: 08-facial_landmarks

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

• Link: https://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/
• Folder: 09-blink-detection

Commands used:

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

• Link: https://www.pyimagesearch.com/2017/05/08/drowsiness-detection-opencv/
• Folder: 10-detect_drowsiness

Commands used:

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

• Link: https://www.pyimagesearch.com/2016/09/26/a-simple-neural-network-with-python-and-keras/
• Folder: 12-simple-neural-network

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

• Link: https://www.pyimagesearch.com/2017/08/21/deep-learning-with-opencv/
• Folder: 13-deep-learning-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

• Link: https://www.pyimagesearch.com/2018/04/09/how-to-quickly-build-a-deep-learning-image-dataset/
• Folder: 14-search_bing_api

Commands used:

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

• Link: https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/
• Folder: 15-cnn-keras

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

• Link: https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/
• Folder: 16-real-time-object-detection

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