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Untriex Programming
2021-08-31 22:06:02 +02:00
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69
hand tracking/main.py Normal file
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import cv2
import mediapipe as mp
import time
import math
import pyautogui
cap = cv2.VideoCapture(0)
pyautogui.PAUSE = 0.01
mpHands = mp.solutions.hands
hands = mpHands.Hands(max_num_hands=1, min_tracking_confidence=0.80, min_detection_confidence=0.90)
mpDraw = mp.solutions.drawing_utils
testing = 23
click_dis = 35
sensitivity = 3.5
def get_distance(first, second, height, width):
dist_x = (results.multi_hand_landmarks[0].landmark[first].x - results.multi_hand_landmarks[0].landmark[
second].x) * width
dist_y = (results.multi_hand_landmarks[0].landmark[first].y - results.multi_hand_landmarks[0].landmark[
second].y) * height
return math.sqrt(abs(dist_x ** 2 + dist_y ** 2))
x, y = None, None
while True:
success, img = cap.read()
h, w, c = img.shape
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = hands.process(imgRGB)
if results.multi_hand_landmarks:
dist_palm = round(get_distance(0, 9, h, w) / 100, 3)
if not x and not y:
x, y = results.multi_hand_landmarks[0].landmark[4].x * w, results.multi_hand_landmarks[0].landmark[4].y * h
dis_1 = get_distance(4, 8, h, w)
cv2.putText(img, f"""dist 4-8: {round(dis_1)}/{round(testing * dist_palm, 2)}""", (0, 15),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img, f"""MOVE: {"True" if dis_1 < testing else "False"}""", (0, 40),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img, f"""dist 4-12: {round(get_distance(12, 4, h, w))}/{round(click_dis * dist_palm, 2)}""",
(0, 65),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img, f"""CLICK: {"True" if get_distance(12, 4, h, w) < click_dis * dist_palm else "False"}""",
(0, 90),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img, f"""dsit 0-9: {dist_palm}""", (0, 115),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
for handLms in results.multi_hand_landmarks:
mpDraw.draw_landmarks(img, handLms)
if dis_1 < testing * dist_palm and get_distance(12, 4, h, w) < click_dis * dist_palm:
pyautogui.dragRel(-(results.multi_hand_landmarks[0].landmark[3].x * w - x) * sensitivity,
(results.multi_hand_landmarks[0].landmark[3].y * h - y) * sensitivity, duration=0.001)
elif dis_1 < testing * dist_palm:
pyautogui.moveRel(-(results.multi_hand_landmarks[0].landmark[3].x * w - x) * sensitivity,
(results.multi_hand_landmarks[0].landmark[3].y * h - y) * sensitivity, duration=0.001)
elif get_distance(12, 4, h, w) < click_dis * dist_palm and not click:
pyautogui.click()
click = True
# mouse.click("left")
print("clicked")
else:
click = False
x, y = results.multi_hand_landmarks[0].landmark[3].x * w, results.multi_hand_landmarks[0].landmark[3].y * h
else:
x, y = None, None
cv2.imshow("Image", img)
cv2.waitKey(1)

84
hand tracking/new_demo.py Normal file
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import cv2
import mediapipe as mp
import time
import math
import threading
import pyautogui
cap = cv2.VideoCapture(0)
pyautogui.PAUSE = 0.01
mpHands = mp.solutions.hands
hands = mpHands.Hands(max_num_hands=1, min_tracking_confidence=0.95, min_detection_confidence=0.90)
mpDraw = mp.solutions.drawing_utils
mov_dis = 0.2379
click_dis = 0.2823
sensitivity = 3.5
testing8_12 = []
testing0_5 = []
testing3_5 = []
def get_distance(first, second, height, width):
dist_x = (results.multi_hand_landmarks[0].landmark[first].x - results.multi_hand_landmarks[0].landmark[
second].x) * width
dist_y = (results.multi_hand_landmarks[0].landmark[first].y - results.multi_hand_landmarks[0].landmark[
second].y) * height
return math.sqrt(abs(dist_x ** 2 + dist_y ** 2))
def dist(point1, point2, pointa, pointb, pointc, pointd):
dis12 = get_distance(point1, point2, h, w)
distab = get_distance(pointa, pointb, h, w)
distcd = get_distance(pointc, pointd, h, w)
testing8_12.append(dis12)
testing0_5.append(distab)
testing3_5.append(distcd)
print(f"8-12: {round(sum(testing8_12)/len(testing8_12), 2)} | 0-5 {round(sum(testing0_5)/len(testing0_5), 2)} | 3-5 {round(sum(testing3_5)/len(testing3_5), 2)}")
x, y = None, None
click = False
while True:
success, img = cap.read()
h, w, c = img.shape
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = hands.process(imgRGB)
if results.multi_hand_landmarks:
dist_palm = round(get_distance(0, 5, h, w))
if not x and not y:
x, y = results.multi_hand_landmarks[0].landmark[4].x * w, results.multi_hand_landmarks[0].landmark[4].y * h
dis_1 = get_distance(12, 8, h, w)
cv2.putText(img, f"""MOVE: {round(dis_1)}/{round(mov_dis * dist_palm, 2)} - {"true" if dis_1 < mov_dis * dist_palm else "false"}""", (0, 15),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img, f"""CLICK: {round(get_distance(5, 3, h, w))}/{round(click_dis * dist_palm, 2)} - {"true" if get_distance(5, 3, h, w) < click_dis * dist_palm else "false"}""", (0, 40),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
cv2.putText(img,
f"""DRAG: {"true" if dis_1 < mov_dis * dist_palm and get_distance(5, 3, h, w) < click_dis * dist_palm else "false"}""",
(0, 65),
cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
for handLms in results.multi_hand_landmarks:
mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)
if dis_1 < mov_dis * dist_palm and get_distance(5, 3, h, w) < click_dis * dist_palm:
pyautogui.mouseDown()
pyautogui.moveRel(-(results.multi_hand_landmarks[0].landmark[8].x * w - x) * sensitivity,
(results.multi_hand_landmarks[0].landmark[8].y * h - y) * sensitivity, duration=0.001)
elif dis_1 < mov_dis * dist_palm:
pyautogui.mouseUp()
pyautogui.moveRel(-(results.multi_hand_landmarks[0].landmark[8].x * w - x) * sensitivity,
(results.multi_hand_landmarks[0].landmark[8].y * h - y) * sensitivity, duration=0.001)
elif get_distance(5, 3, h, w) < click_dis * dist_palm and not click:
pyautogui.mouseUp()
pyautogui.click()
click = True
else:
pyautogui.mouseUp()
click = False
x, y = results.multi_hand_landmarks[0].landmark[8].x * w, results.multi_hand_landmarks[0].landmark[8].y * h
dist(8, 12, 0, 5, 3, 5)
else:
x, y = None, None
cv2.imshow("Image", img)
cv2.waitKey(1)

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hand tracking/smoothing.py Normal file
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import math
def smoothing_factor(t_e, cutoff):
r = 2 * math.pi * cutoff * t_e
return r / (r + 1)
def exponential_smoothing(a, x, x_prev):
return a * x + (1 - a) * x_prev
class OneEuroFilter:
def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0,
d_cutoff=1.0):
"""Initialize the one euro filter."""
# The parameters.
self.min_cutoff = float(min_cutoff)
self.beta = float(beta)
self.d_cutoff = float(d_cutoff)
# Previous values.
self.x_prev = float(x0)
self.dx_prev = float(dx0)
self.t_prev = float(t0)
def __call__(self, t, x):
"""Compute the filtered signal."""
t_e = t - self.t_prev
# The filtered derivative of the signal.
a_d = smoothing_factor(t_e, self.d_cutoff)
dx = (x - self.x_prev) / t_e
dx_hat = exponential_smoothing(a_d, dx, self.dx_prev)
# The filtered signal.
cutoff = self.min_cutoff + self.beta * abs(dx_hat)
a = smoothing_factor(t_e, cutoff)
x_hat = exponential_smoothing(a, x, self.x_prev)
# Memorize the previous values.
self.x_prev = x_hat
self.dx_prev = dx_hat
self.t_prev = t
return x_hat