Merge pull request #4 from prathamTailor/main

Some Changes Done

AiVirtualMouseProject.py

@@ -0,0 +1,133 @@
+import sys
+sys.path.append('Users\prath\AppData\Local\Programs\Python\Python38\Lib\site-packages')
+import cv2
+from pynput.keyboard import Key, Listener
+import numpy as np
+from pynput.keyboard import Key
+import HandTrackingModule as htm
+import time
+import autopy
+import keyboard
+import pyautogui as py
+
+##########################
+wCam, hCam = 640, 480
+frameR = 100  # Frame Reduction
+smoothening = 7
+#########################
+
+pTime = 0
+plocX, plocY = 0, 0
+clocX, clocY = 0, 0
+
+# cv2.VideoCapture(video_path or device index )
+# device index: It is just the number to specify the camera. Its possible values ie either 0 or -1.
+cap = cv2.VideoCapture(0)
+cap.set(3, wCam) #set width of cam
+cap.set(4, hCam) #set height of cam
+detector = htm.handDetector(maxHands=1)
+wScr, hScr = autopy.screen.size() #screen size of device in which program is open
+# print(wScr, hScr)
+
+
+def show(key):
+    if key == Key.esc:
+        return True
+    else :
+        return False
+while True:
+
+    # 1. Find hand Landmarks
+    success, img = cap.read() #cap.read() returns a bool (True/False) saved in success. If the frame is read correctly, 
+    # it will be true and store in img
+    img = detector.findHands(img)
+    lmList, bbox = detector.findPosition(img)
+    # 2. Get the tip of the index and middle fingers
+    if len(lmList) != 0:
+        x1, y1 = lmList[8][1:]
+        x2, y2 = lmList[12][1:]
+        # print(x1, y1, x2, y2)
+
+    # 3. Check which fingers are up
+    fingers = detector.fingersUp()
+    if len(fingers) > 4 and fingers[1] == 1 and fingers[2] == 1 and fingers[3] == 1 and fingers[4] == 1:
+        py.mouseUp(button='left')
+    if (len(fingers)>3 and fingers[3] == 0) or (len(fingers)>4 and fingers[4] == 0):
+
+        # 4. Only Index Finger : Moving Mode
+        if len(fingers)>2 and fingers[1] == 1 and fingers[2] == 1:
+            length, img, lineInfo = detector.findDistance(8, 12, img)
+
+            # 5. Convert Coordinates
+            x3 = np.interp(x1, (frameR, wCam - frameR), (0, wScr))
+            y3 = np.interp(y1, (frameR, hCam - frameR), (0, hScr))
+            # 6. Smoothen Values
+            clocX = plocX + (x3 - plocX) / smoothening
+            clocY = plocY + (y3 - plocY) / smoothening
+
+            # 7. Move Mouse
+            if length > 40:
+                autopy.mouse.move(wScr - clocX, clocY)
+                cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
+                cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
+                plocX, plocY = clocX, clocY
+
+        # 8. Both Index and middle fingers are up : Clicking Mode Right CLick
+        if len(fingers) > 2 and fingers[1] == 0 and fingers[2] == 1:
+            # 9. Find distance between fingers
+            length, img, lineInfo = detector.findDistance(8, 12, img)
+            # print(length)
+            # 10. Click mouse if distance short
+            if length > 30:
+                cv2.circle(img, (lineInfo[4], lineInfo[5]),15, (0, 255, 0), cv2.FILLED)
+                py.click(button = 'left')
+
+        # 8. Both Index and middle fingers are up : Clicking Mode Left CLick
+        if len(fingers) > 2 and fingers[1] == 1 and fingers[2] == 0:
+            # 9. Find distance between fingers
+            length, img, lineInfo = detector.findDistance(8, 12, img)
+            # print(length)
+            # 10. Click mouse if distance short
+            if length > 30:
+                cv2.circle(img, (lineInfo[4], lineInfo[5]),15, (0, 255, 0), cv2.FILLED)
+                py.click(button = 'right')
+
+        if len(fingers) > 4 and fingers[1] == 1 and fingers[2] == 1 and fingers[0]==0 and fingers[3]==0 and fingers[4]==0:
+            # 9. Find distance between fingers
+            length, img, lineInfo = detector.findDistance(8, 12, img)
+            # print(length)
+            # 10. Click mouse if distance short
+            if length < 25:
+                cv2.circle(img, (lineInfo[4], lineInfo[5]),15, (0, 255, 0), cv2.FILLED)
+                py.doubleClick()
+
+        # Drag and Drop
+        if len(fingers) > 4 and fingers[0] == 0 and fingers[1] == 0 and fingers[2] == 0 and fingers[3] == 0 and fingers[4] == 0:
+            length, img, lineInfo = detector.findDistance(8, 12, img)
+
+            # 5. Convert Coordinates
+            x3 = np.interp(x1, (frameR, wCam - frameR), (0, wScr))
+            y3 = np.interp(y1, (frameR, hCam - frameR), (0, hScr))
+            # 6. Smoothen Values
+            clocX = plocX + (x3 - plocX) / smoothening
+            clocY = plocY + (y3 - plocY) / smoothening
+
+            # 7. Move Mouse
+            py.mouseDown(button='left')
+            autopy.mouse.move(wScr - clocX, clocY)
+            cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
+            cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
+            plocX, plocY = clocX, clocY
+
+    # 11. Frame Rate
+    cTime = time.time()
+    fps = 1 / (cTime - pTime)
+    pTime = cTime
+    img = cv2.flip(img,1)
+    cv2.putText(img, str(int(fps)), (20, 50), cv2.FONT_HERSHEY_PLAIN, 3,
+                (255, 0, 0), 3)
+    # 12. Display
+    cv2.imshow("Image", img)
+    cv2.waitKey(1)
+    if keyboard.is_pressed('esc'):
+        break

HandTrackingModule.py

@@ -0,0 +1,125 @@
+import mediapipe as mp
+import time
+import math
+import numpy as np
+import cv2
+
+class handDetector():
+    def __init__(self, mode=False, maxHands=2, modelComplexity=1,detectionCon=0.5, trackCon=0.5):
+        self.mode = mode
+        self.lmList = []
+        self.maxHands = maxHands
+        self.modelComplex = modelComplexity
+        self.detectionCon = detectionCon
+        self.trackCon = trackCon
+        self.mpHands = mp.solutions.hands
+        self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.modelComplex, self.detectionCon, self.trackCon)
+        # self.hands = self.mpHands.Hands(self.mode, self.maxHands, self.detectionCon, self.trackCon)
+        self.mpDraw = mp.solutions.drawing_utils
+        self.tipIds = [4, 8, 12, 16, 20]
+
+    def findHands(self, img, draw=True):
+        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2.cvtColor() method is used to convert an image from one color space to another.
+        self.results = self.hands.process(imgRGB) #It then processes the RGB image to identify the hands in the image:
+        # print(self.results.multi_hand_landmarks)
+
+        if self.results.multi_hand_landmarks:
+            for handLms in self.results.multi_hand_landmarks:
+                if draw:
+                    self.mpDraw.draw_landmarks(img, handLms,self.mpHands.HAND_CONNECTIONS)
+
+        return img
+
+    def findPosition(self, img, handNo=0, draw=True):
+        xList = []
+        yList = []
+        bbox = []
+        # self.lmList = []
+        self.lmList = []
+        # print(type(self.results.multi_hand_landmarks))
+        if self.results.multi_hand_landmarks:
+            myHand = self.results.multi_hand_landmarks[handNo]
+            for id, lm in enumerate(myHand.landmark):
+                # print(id, lm)
+                h, w, c = img.shape # height, width, channel
+                cx, cy = int(lm.x * w), int(lm.y * h)
+                xList.append(cx)
+                yList.append(cy)
+                # print(id, cx, cy)
+                self.lmList.append([id, cx, cy])
+                if draw:
+                    cv2.circle(img, (cx, cy), 5, (255, 0, 255), cv2.FILLED)
+
+            xmin, xmax = min(xList), max(xList)
+            ymin, ymax = min(yList), max(yList)
+            bbox = xmin, ymin, xmax, ymax
+
+            if draw:
+                cv2.rectangle(img, (xmin - 20, ymin - 20), (xmax + 20, ymax + 20),
+                              (0, 255, 0), 2)
+
+        return self.lmList, bbox
+
+    def fingersUp(self):
+        fingers = []
+        # Thumb
+        # print(len(self.lmList))
+        # print(self.lmList[self.tipIds[0]][1])
+        # print(self.lmList[self.tipIds[0] - 1][1])
+        if len(self.lmList) > 1:
+            if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0] - 1][1]:
+                fingers.append(1)
+            else:
+                fingers.append(0)
+
+        # Fingers
+        for id in range(1, 5):
+            if len(self.lmList) > 2:
+                if self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
+                    fingers.append(1)
+                else:
+                    fingers.append(0)
+
+        # totalFingers = fingers.count(1)
+
+        return fingers
+
+    def findDistance(self, p1, p2, img, draw=True, r=15, t=3):
+        x1, y1 = self.lmList[p1][1:]
+        x2, y2 = self.lmList[p2][1:]
+        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
+
+        if draw:
+            cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), t)
+            cv2.circle(img, (x1, y1), r, (255, 0, 255), cv2.FILLED)
+            cv2.circle(img, (x2, y2), r, (255, 0, 255), cv2.FILLED)
+            cv2.circle(img, (cx, cy), r, (0, 0, 255), cv2.FILLED)
+        length = math.hypot(x2 - x1, y2 - y1)
+
+        return length, img, [x1, y1, x2, y2, cx, cy]
+
+
+def main():
+    pTime = 0
+    cTime = 0
+    cap = cv2.VideoCapture(0)
+    detector = handDetector()
+    while True:
+        success, img = cap.read()
+        img = detector.findHands(img)
+        lmList, bbox = detector.findPosition(img)
+        if len(lmList) != 0:
+            print(lmList[4])
+
+        cTime = time.time()
+        fps = 1 / (cTime - pTime)
+        pTime = cTime
+
+        cv2.putText(img, str(int(fps)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3,(255, 0, 255), 3)
+
+        cv2.imshow("Image", img)
+        cv2.waitKey(1)
+
+
+if __name__ == "__main__":
+    main()

open.py

@@ -0,0 +1,16 @@
+import sys
+import os
+import tkinter as tk
+from tkinter import *
+
+window=Tk()
+
+window.title("Running Python Script")
+window.geometry('250x250')
+def callback():
+    with open("AiVirtualMouseProject.py", "r", encoding="utf-8") as file:
+        exec(file.read())
+b = tk.Button(window,text="Run the Face Detection",command=callback)
+b.pack()
+
+window.mainloop()