new_dataset.py

import numpy as np
import cv2
import os
from utils import detector_utils as detector_utils
import tensorflow as tf

def main():
    currentPath = ''
    currentExample = ''

    print('Do you want to :  Add examples to existing pose (Y/N)' )

    menu_choice = input()


    if(menu_choice == 'Y'):
        # Display current poses
        dirs = os.listdir('F:/Github/Hand_pose_DKE/Poses/')
        dirs_choice = ''
        possible_choices = []
        i = 1
        for _dir in dirs:
            dirs_choice += str(i) + ' - ' + str(_dir) + ' / '
            possible_choices.append(str(i))
            i+=1
        
        # Ask user to choose to which pose to add examples
        print('Choose one of the following pose:')
        print(dirs_choice)       
        choice = input()

        # Count number of files to increment new example directory
        subdirs = os.listdir('F:/Github/Hand_pose_DKE/Poses/' + dirs[int(choice)-1] + '/')
        index = len(subdirs) + 1

        # Create new example directory
        if not os.path.exists('Poses/' + dirs[int(choice)-1] + '/' + dirs[int(choice)-1] + '_' + str(index) + '/'):
            os.makedirs('Poses/' + dirs[int(choice)-1] + '/' + dirs[int(choice)-1] + '_' + str(index) + '/')

            #Update current path
            currentPath = 'Poses/' + dirs[int(choice)-1] + '/' + dirs[int(choice)-1] + '_' + str(index) + '/'
            currentExample = dirs[int(choice)-1] + '_' + str(index) + '_'
            name_pose = dirs[int(choice) - 1]

    elif menu_choice == "N":
        print('Enter a name for the pose you want to add :')
        name_pose = input()

        # Create folder for pose
        if not os.path.exists('Poses/' + name_pose):
            os.makedirs('Poses/' + name_pose + '/' + name_pose + '_1/')
            currentPath = 'Poses/' + name_pose + '/' + name_pose + '_1/'
            currentExample = name_pose + '_1_'
        

    print('You\'ll now be prompted to record the pose you want to add. \n \
                Please place your hand beforehand facing the camera, and press any key when ready. \n \
                When finished press \'q\'.')
    input()

    # Begin capturing
    cap = cv2.VideoCapture(0)

    # Define the codec and create VideoWriter object
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    out = cv2.VideoWriter(currentPath + name_pose + '.avi', fourcc, 25.0, (640, 480))

    while(cap.isOpened()):
        ret, frame = cap.read()
        if ret:
            # write the frame
            out.write(frame)

            cv2.imshow('frame', frame)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        else:
            break

    # Release everything if job is finished
    cap.release()
    out.release()
    cv2.destroyAllWindows()

    vid = cv2.VideoCapture(currentPath + name_pose + '.avi')

    # Check if the video
    if (not vid.isOpened()):
        print('Error opening video stream or file')
        return

    print('>> loading frozen model..')
    detection_graph, sess = detector_utils.load_inference_graph()
    sess = tf.Session(graph=detection_graph)
    print('>> model loaded!')
    
    _iter = 1
    # Read until video is completed
    while(vid.isOpened()):
        # Capture frame-by-frame
        ret, frame = vid.read()
        if ret:
            print('   Processing frame: ' + str(_iter))
            # Resize and convert to RGB for NN to work with
            frame = cv2.resize(frame, (320, 180), interpolation=cv2.INTER_AREA)

            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

            # Detect object
            boxes, scores = detector_utils.detect_objects(frame, detection_graph, sess)

            # get region of interest
            res = detector_utils.get_box_image(1, 0.2, scores, boxes, 320, 180, frame)

            # Save cropped image 
            if(res is not None):       
                cv2.imwrite(currentPath + currentExample + str(_iter) + '.png', cv2.cvtColor(res, cv2.COLOR_RGB2BGR))

            _iter += 1
        # Break the loop
        else:
            break

    print('   Processed ' + str(_iter) + ' frames!')

    vid.release()


if __name__ == '__main__':
    main()