_

hand tracking/main.py

@@ -1,69 +1,87 @@
+import math
+
 import cv2
 import mediapipe as mp
-import time
-import math
 import pyautogui
 
-cap = cv2.VideoCapture(0)
+
-pyautogui.PAUSE = 0.01
+class Hands_AI:
-mpHands = mp.solutions.hands
+    def __init__(self):
-hands = mpHands.Hands(max_num_hands=1, min_tracking_confidence=0.80, min_detection_confidence=0.90)
+        self.cap = cv2.VideoCapture(0)
-mpDraw = mp.solutions.drawing_utils
+        pyautogui.PAUSE = 0.01
-testing = 23
+        self.mpHands = mp.solutions.hands
-click_dis = 35
+        self.hands = self.mpHands.Hands(max_num_hands=1, min_tracking_confidence=0.95, min_detection_confidence=0.90)
-sensitivity = 3.5
+        self.mpDraw = mp.solutions.drawing_utils
+        self.mov_dis = 0.2479
+        self.click_dis = 0.2823
+        self.sensitivity = 3.5
+        self.x, self.y = None, None
+        self.click = False
+
+    def get_distance(self, first, second, height, width):
+        dist_x = (self.results.multi_hand_landmarks[0].landmark[first].x -
+                  self.results.multi_hand_landmarks[0].landmark[
+                      second].x) * width
+        dist_y = (self.results.multi_hand_landmarks[0].landmark[first].y -
+                  self.results.multi_hand_landmarks[0].landmark[
+                      second].y) * height
+        return math.sqrt(abs(dist_x ** 2 + dist_y ** 2))
+
+    def run(self, q):
+        while True:
+            success, img = self.cap.read()
+            self.h, self.w, self.c = img.shape
+            imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            self.results = self.hands.process(imgRGB)
+            if self.results.multi_hand_landmarks:
+                dist_palm = round(self.get_distance(0, 5, self.h, self.w))
+                if not self.x and not self.y:
+                    self.x, self.y = self.results.multi_hand_landmarks[0].landmark[4].x * self.w, \
+                                     self.results.multi_hand_landmarks[0].landmark[4].y * self.h
+                dis_1 = self.get_distance(12, 8, self.h, self.w)
+                cv2.putText(img,
+                            f"""MOVE: {round(dis_1)}/{round(self.mov_dis * dist_palm, 2)} - {"true" if dis_1 < self.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(self.get_distance(5, 3, self.h, self.w))}/{round(self.click_dis * dist_palm, 2)} - {"true" if self.get_distance(5, 3, self.h, self.w) < self.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 < self.mov_dis * dist_palm and self.get_distance(5, 3, self.h, self.w) < self.click_dis * dist_palm else "false"}""",
+                            (0, 65),
+                            cv2.FONT_HERSHEY_PLAIN, 1.5, (255, 0, 255), thickness=2)
+                for handLms in self.results.multi_hand_landmarks:
+                    self.mpDraw.draw_landmarks(img, handLms, self.mpHands.HAND_CONNECTIONS)
+                if dis_1 < self.mov_dis * dist_palm and self.get_distance(5, 3, self.h,
+                                                                          self.w) < self.click_dis * dist_palm:
+                    pyautogui.mouseDown()
+                    pyautogui.moveRel(
+                        -(self.results.multi_hand_landmarks[0].landmark[8].x * self.w - self.x) * self.sensitivity,
+                        (self.results.multi_hand_landmarks[0].landmark[8].y * self.h - self.y) * self.sensitivity,
+                        duration=0.001)
+                elif dis_1 < self.mov_dis * dist_palm:
+                    pyautogui.mouseUp()
+                    pyautogui.moveRel(
+                        -(self.results.multi_hand_landmarks[0].landmark[8].x * self.w - self.x) * self.sensitivity,
+                        (self.results.multi_hand_landmarks[0].landmark[8].y * self.h - self.y) * self.sensitivity,
+                        duration=0.001)
+
+                elif self.get_distance(5, 3, self.h, self.w) < self.click_dis * dist_palm and not self.click:
+                    pyautogui.mouseUp()
+                    pyautogui.click()
+                    self.click = True
+                else:
+                    pyautogui.mouseUp()
+                    self.click = False
+                self.x, self.y = self.results.multi_hand_landmarks[0].landmark[8].x * self.w, \
+                                 self.results.multi_hand_landmarks[0].landmark[8].y * self.h
+            else:
+                self.x, self.y = None, None
+            cv2.imshow("Image", img)
+            cv2.waitKey(1)
 
 
-def get_distance(first, second, height, width):
+if __name__ == '__main__':
-    dist_x = (results.multi_hand_landmarks[0].landmark[first].x - results.multi_hand_landmarks[0].landmark[
+    ai = Hands_AI()
-        second].x) * width
+    ai.run("X")
-    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)