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gesture_detection_with_speaker_control.py
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gesture_detection_with_speaker_control.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import copy
import itertools
import pickle
import cv2 as cv
import numpy as np
import mediapipe as mp
from utils import CvFpsCalc
from utils.generate_commands import CommandGenerator
import vlc
def main():
cap_device = 0
cap_width = 960
cap_height = 540
use_static_image_mode = False
min_detection_confidence = 0.5
min_tracking_confidence = 0.5
use_brect = True
################################################################
cap = cv.VideoCapture(cap_device)
cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width)
cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height)
##############################################################
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(
static_image_mode=use_static_image_mode,
max_num_hands=1,
min_detection_confidence=min_detection_confidence,
min_tracking_confidence=min_tracking_confidence,
)
filename = 'models/logreg_complete.pkl'
model = pickle.load(open(filename, 'rb'))
commander = CommandGenerator()
# playlist of songs
plist = ['songs/howLong.mp3', 'songs/godIsAWoman.mp3', 'songs/tillTheWorldEnds.mp3', 'songs/thatsWhatILike.mp3', 'songs/24kMagic.mp3', 'songs/the_difference.mp3', 'songs/sunshine.mp3']
songIndex = 0
playlistLength = len(plist)
# play the first song
player = vlc.MediaPlayer(plist[0])
player.audio_set_volume(40)
player.play()
############################################################
with open('models/keypoint_classifier_label.csv',
encoding='utf-8-sig') as f:
keypoint_classifier_labels = csv.reader(f)
keypoint_classifier_labels = [
row[0] for row in keypoint_classifier_labels
]
#########################################################
cvFpsCalc = CvFpsCalc(buffer_len=10)
#########################################################################
mode = 0
while True:
fps = cvFpsCalc.get()
##################################################
key = cv.waitKey(10)
if key == 27: # ESC
break
number, mode = select_mode(key, mode)
######################################################
ret, image = cap.read()
if not ret:
break
image = cv.flip(image, 1)
debug_image = copy.deepcopy(image)
##############################################################
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image)
image.flags.writeable = True
#####################################################################
if results.multi_hand_landmarks is not None:
for hand_landmarks, handedness in zip(results.multi_hand_landmarks,
results.multi_handedness):
brect = calc_bounding_rect(debug_image, hand_landmarks)
landmark_list = calc_landmark_list(debug_image, hand_landmarks)
pre_processed_landmark_list = pre_process_landmark(
landmark_list)
hand_sign_id = model.predict(pre_processed_landmark_list.reshape(1,-1))[0]
commander.add_gestures(hand_sign_id)
command = commander.get_command()
if command == 2:
player.pause()
# raise volume
elif command == 3:
# time.sleep(5)
# if already at max volume, do nothing
if player.audio_get_volume() < 100:
player.audio_set_volume(player.audio_get_volume() + 20)
# lower volume
elif command == 4:
# time.sleep(5)
# if already at min volume, do nothing
if player.audio_get_volume() > 0:
player.audio_set_volume(player.audio_get_volume() - 20)
elif command == 5:
# if at last song in playlist, go to beginning
if songIndex == playlistLength - 1:
currentVol = player.audio_get_volume()
player.pause()
player = vlc.MediaPlayer(plist[0])
player.audio_set_volume(currentVol)
player.play()
songIndex = 0
else:
currentVol = player.audio_get_volume()
player.pause()
player = vlc.MediaPlayer(plist[songIndex + 1])
player.audio_set_volume(currentVol)
player.play()
songIndex = songIndex + 1
elif command == 6:
# if at first song in playlist, go to end
if (songIndex == 0):
currentVol = player.audio_get_volume()
player.pause()
player = vlc.MediaPlayer(plist[playlistLength - 1])
player.audio_set_volume(currentVol)
player.play()
songIndex = playlistLength - 1
else:
currentVol = player.audio_get_volume()
player.pause()
player = vlc.MediaPlayer(plist[songIndex - 1])
player.audio_set_volume(currentVol)
player.play()
songIndex = songIndex - 1
debug_image = draw_bounding_rect(use_brect, debug_image, brect)
debug_image = draw_landmarks(debug_image, landmark_list)
debug_image = draw_info_text(
debug_image,
brect,
handedness,
keypoint_classifier_labels[hand_sign_id]
)
debug_image = draw_info(debug_image, fps, mode, number)
##############################################################
cv.imshow('Hand Gesture Recognition', debug_image)
cap.release()
cv.destroyAllWindows()
def select_mode(key, mode):
number = -1
if 48 <= key <= 57: # 0 ~ 9
number = key - 48
if key == 110: # n
mode = 0
if key == 107: # k
mode = 1
if key == 104: # h
mode = 2
return number, mode
def calc_bounding_rect(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_array = np.empty((0, 2), int)
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
landmark_point = [np.array((landmark_x, landmark_y))]
landmark_array = np.append(landmark_array, landmark_point, axis=0)
x, y, w, h = cv.boundingRect(landmark_array)
return [x, y, x + w, y + h]
def calc_landmark_list(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_point = []
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
# landmark_z = landmark.z
landmark_point.append([landmark_x, landmark_y])
return landmark_point
def pre_process_landmark(landmark_list):
temp_landmark_list = copy.deepcopy(landmark_list)
base_x, base_y = 0, 0
for index, landmark_point in enumerate(temp_landmark_list):
if index == 0:
base_x, base_y = landmark_point[0], landmark_point[1]
temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x
temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y
temp_landmark_list = list(
itertools.chain.from_iterable(temp_landmark_list))
max_value = max(list(map(abs, temp_landmark_list)))
def normalize_(n):
return n / max_value
temp_landmark_list = list(map(normalize_, temp_landmark_list))
return np.array(temp_landmark_list)
def draw_landmarks(image, landmark_point):
if len(landmark_point) > 0:
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
(255, 255, 255), 2)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
(0, 0, 0), 6)
cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
(255, 255, 255), 2)
for index, landmark in enumerate(landmark_point):
if index == 0:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 1:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 2:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 3:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 4:
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 5:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 6:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 7:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 8:
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 9:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 10:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 11:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 12:
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 13:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 14:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 15:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 16:
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
if index == 17:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 18:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 19:
cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
if index == 20:
cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
-1)
cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
return image
def draw_bounding_rect(use_brect, image, brect):
if use_brect:
cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]),
(0, 0, 0), 1)
return image
def draw_info_text(image, brect, handedness, hand_sign_text):
cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[1] - 22),
(0, 0, 0), -1)
info_text = handedness.classification[0].label[0:]
if hand_sign_text != "":
info_text = info_text + ':' + hand_sign_text
cv.putText(image, info_text, (brect[0] + 5, brect[1] - 4),
cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv.LINE_AA)
return image
def draw_info(image, fps, mode, number):
cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX,
1.0, (0, 0, 0), 4, cv.LINE_AA)
cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX,
1.0, (255, 255, 255), 2, cv.LINE_AA)
mode_string = ['Logging Key Point', 'Logging Point History']
if 1 <= mode <= 2:
cv.putText(image, "MODE:" + mode_string[mode - 1], (10, 90),
cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1,
cv.LINE_AA)
if 0 <= number <= 9:
cv.putText(image, "NUM:" + str(number), (10, 110),
cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1,
cv.LINE_AA)
return image
if __name__ == '__main__':
main()