This program is specialized for automatically detecting ONE hand per frame in a MP4 video using OpenCV API.
During the execution of the program, you have to choose whether you would like to save the frame or not by pressing one of the hotkeys. When the program terminates, a csv file will be generated. Run other consecutive programs to generate other csv files and TFRecord files which can be used for training and testing a TensorFlow hand detection model.
This program has been successfully run using python2.7.
Remember to pip install cv2 numpy csv pandas scikit-learn to get all the dependencies ready before the execution of the program.
Currently only one label - hand is supported. See the line rowdata = [filename, final_w, final_h, 'hand', xmin, ymin, xmax, ymax]
hand_images/ --> holding all the images to be split into training/testing images
hand_labels.csv --> holding all the information of the images and coordinates of the hand bounding boxes in the images
hand_my_dataset/
|
|--- hand_test/
| L hand_test_labels.csv
| L all the images for testing
|
|--- hand_train/
| L hand_train_labels.csv
| L all the images for training
|
--- hand_eval.record
|
--- hand_train.record
This program detects hand in frames from a video source using OpenCV. If a hand is detected in a frame, the frame can be saved in JPEG format under the image directory and the coordinates of the bounding box for the hand in the frame will be recorded. After the detection of hand in the video frames finished, a csv file hand_label.csv will be generated.
Please change ?.mp4 in the line hand_detector = HandDetector('resources/?.mp4') to your video file in MP4 format. You are advised to put all the video sources under the resources directory.
After a hand is detected in the frame, you can save the frame by activating any one of the debug window and pressing the s hotkey twice. You can also press the x hotkey once to discard the frame. Otherwise, after 8 seconds, the frame will be discarded automatically. You may change the time period for making a decision by modifying the number in ms i.e. 8000 in the lines if cv2.waitKey(8000) & 0xFF == ord('x'): and elif cv2.waitKey(8000) & 0xFF == ord('s'):.
Before the frame is saved, it will be automatically resized to a size of 1280 * 720. You may change your desired frame width and height by changing the number of the variables self.desiredFrameWidth and self.desiredFrameHeight.
You may change the variable self.image_dir to create another directory with a different name for holding all the JPEG images which are going to be saved during the execution of the program. The default name of the directory is image.
You may change the variable self.csvFilename to create another csv file for recording all the information about the image and the coordinates of the bounding box indicating the position of a hand in the image. The default name of the csv file is hand_label.csv.
This program splits the images in the image directory into 2 separated directories and corresponding image data in the hand_label.csv csv file into 2 separated csv files. By default a dataset directory called hand_my_dataset will be created. Under this dataset directory, there are two subdirectories. hand_train is intended to be used for training while hand_test is intended to be used for testing during the hand detection model training process.
You may change the names of the images, training, testing directories and the csv files by modifying the variables located at the beginning of the program.
This program generates a TFRecord file either based on the train or test csv file, i.e. hand_train_labels.csv or hand_test_labels.csv.
Run the program twice to generate 2 TFRecord files, one for training and another one for testing. Change the name of the files and directories by modifying values of the variables located at the beginning of the program.
Run visualize_image_bbox.py seperately to visualize the bounding box for each image saved under the image, hand_train and hand_test directories.
At present, only ONE hand can be detected per frame.
The bounding box indicating the position of the hand (cropped area) cannot perfectly fit in the size of the hand i.e. the bounding box is bigger than the size of the hand and includes other objects surrounding a hand.
If a hand appears to be too big in the frame, it can hardly be detected or only a few fingers can be detected. Same problem happends if the background of the video or the objects surrounding the hands has a very similar color to skin.
A hand which appears to be smaller in size in each frame can be detected with a higher accuracy i.e. the bounding box can fit in the size of the hand.
Avoid choosing a video in which hands appeared to be too big as well as the color of the background and the objects surrounding the hands has a similar color to skin.
For Mac users, be aware of the .DS_Store file which might appear in the dataset directory i.e. hand_my_dataset as well as hand_train and hand_test directories. Please run ls -la in the terminal to check if the .DS_Store file exists in the mentioned directories. If any, delete the file before generating the TFRecord files.