I want a code for double clicking with only these packages:
import cv2
import pyautogui
from HandTracker import HandDetector
import math
This is the code for handtracker:
import cv2
import mediapipe as mp
import time
import math
import numpy as np
class HandDetector():
def __init__(self, imageMode=False, maxHands=2, modelComplexity=1 , detectionConfidence=0.5, trackingConfidence=0.5 ):
self.imageMode = imageMode
self.maxHands = maxHands
self.modelComplexity = modelComplexity
self.detectionConfidence = detectionConfidence
self.trackingConfidence = trackingConfidence
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(self.imageMode, self.maxHands, self.modelComplexity, self.detectionConfidence, self.trackingConfidence)
self.mpDraw = mp.solutions.drawing_utils
self.tipIds = [4, 8, 12, 16, 20]
def findHandLandmarks(self, cap):
# X is the raw distance, Y is the value in cm.
x = [300, 245, 200, 170, 145, 130, 112, 103, 93, 87, 80, 75, 70, 67, 62, 59, 57]
y = [20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]
coff = np.polyfit(x, y, 2)
isImageRead, image = cap.read()
image = self.findHands(image)
landmarksList, boundingbox = self.findPosition(image)
try:
_, x1, y1 = landmarksList[5]
_, x2, y2 = landmarksList[17]
distance = int(math.sqrt((y2 - y1)**2 + (x2 - x1)**2))
A, B, C = coff
distanceCM = A * distance**2 + B * distance + C
if distanceCM < 40:
text = 'Move your hand away'
cv2.putText(image, text, (10, 70), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 255), 3)
if distanceCM > 90:
text = 'Bring your hand closer'
cv2.putText(image, text, (10, 70), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 255), 3)
if 90 >= distanceCM >= 40:
text = f'{int(distanceCM)} cm'
cv2.putText(image, text, (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3)
except IndexError:
pass
return image
def findHands(self, image, draw=True):
imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
self.results = self.hands.process(imgRGB)
if self.results.multi_hand_landmarks:
for handLandmarks in self.results.multi_hand_landmarks:
if draw:
self.mpDraw.draw_landmarks(image, handLandmarks, self.mpHands.HAND_CONNECTIONS)
return image
def findPosition(self, image, handNo=0, draw=True):
xList = []
yList = []
bbox = []
self.landmarksList = []
if self.results.multi_hand_landmarks:
myHand = self.results.multi_hand_landmarks[handNo]
for id, lm in enumerate(myHand.landmark):
h, w, c = image.shape
cx, cy = int(lm.x * w), int(lm.y * h)
xList.append(cx)
yList.append(cy)
self.landmarksList.append([id, cx, cy])
if draw:
cv2.circle(image, (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(image, (xmin - 20, ymin - 20), (xmax + 20, ymax + 20), (0, 255, 0), 2)
return self.landmarksList, bbox
def fingersMotion(self):
# Fingers[] represents which fingers are up where 1 = up and 0 = down
fingers = []
# Thumb
if self.landmarksList[self.tipIds[0]][1] > self.landmarksList[self.tipIds[0] - 1][1]:
fingers.append(1)
else:
fingers.append(0)
# Fingers
for id in range(1, 5):
if self.landmarksList[self.tipIds[id]][2] < self.landmarksList[self.tipIds[id] - 2][2]:
fingers.append(1)
else:
fingers.append(0)
return fingers
def findDistance(self, p1, p2, image, draw=True, r=15, t=3):
x1, y1 = self.landmarksList[p1][1:]
x2, y2 = self.landmarksList[p2][1:]
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
if draw:
cv2.line(image, (x1, y1), (x2, y2), (255, 0, 255), t)
cv2.circle(image, (x1, y1), r, (255, 0, 255), cv2.FILLED)
cv2.circle(image, (x2, y2), r, (255, 0, 255), cv2.FILLED)
cv2.circle(image, (cx, cy), r, (0, 0, 255), cv2.FILLED)
length = math.hypot(x2 - x1, y2 - y1)
return length, image, [x1, y1, x2, y2, cx, cy]
def main():
previousTime = 0
currentTime = 0
cap = cv2.VideoCapture(0)
detector = HandDetector()
while True:
image = detector.findHandLandmarks(cap)
# Frame rate
currentTime = time.time()
framesPerSecond = 1 / (currentTime - previousTime)
previousTime = currentTime
cv2.putText(image, str(int(framesPerSecond)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3)
# Display
cv2.imshow("Image", image)
if cv2.waitKey(1) == ord('q'):
break
if __name__ == "__main__":
main()
Related
so i'm working on a project that detect the hands move and use it to control the computer mouse , every thing went so good but now i need to convert it to exe file.
when i run my code in the pysharm or in the CMD console its working so good but when i convert i to exe file i cant open it and it keeps showing me some errors that i could not solve or find any solution online , i will be so greatfull for help cuz i really need it ( this code is not mine i took it online and modified it a little )
Sistem information :
python 3.7.8
pyinstaller 4.8
mediapipe 0.8.9.1
autopy 4.0.0
opencv 4.5.5.62
numpy 1.21.5
windows 10
My firts code:
import cv2
import mediapipe as mp
import math
class HandDetector:
def __init__(self,
mode=False,
max_num_hands=1,
complexity=1,
detection=0.5,
track=0.5):
self.mode = mode
self.maxHands = max_num_hands
self.complexity = complexity
self.detectionCon = detection
self.trackCon = track
self.mpHands = mp.solutions.hands
self.hands = self.mpHands.Hands(self.mode,
self.maxHands,
self.complexity,
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)
self.results = self.hands.process(imgRGB)
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 = []
if self.results.multi_hand_landmarks:
myHand = self.results.multi_hand_landmarks[handNo]
for id, lm in enumerate(myHand.landmark):
h, w, c = img.shape
cx, cy = int(lm.x * w), int(lm.y * h)
xlist.append(cx)
ylist.append(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
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 self.lmList[self.tipIds[id]][2] < self.lmList[self.tipIds[id] - 2][2]:
fingers.append(1)
else:
fingers.append(0)
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():
cam = cv2.VideoCapture(1, cv2.CAP_DSHOW)
detector = HandDetector()
while True:
success, img = cam.read()
img = detector.findHands(img)
lmlist, bbox = detector.findPosition(img)
if len(lmlist) != 0:
print(lmlist[4])
cv2.imshow("Image", img)
cv2.waitKey(1)
if __name__ == "__main__":
main()
And the second code :
import cv2
import first as htm
import numpy as np
import autopy
import mouse
wCam, hCam = 640, 480
frameR = 90
smoothening = 7
plocX, plocY = 0, 0
clocX, clocY = 0, 0
cam = cv2.VideoCapture(1, cv2.CAP_DSHOW)
cam.set(3, wCam)
cam.set(4, hCam)
detector = htm.HandDetector(max_num_hands=1)
wScr, hScr = autopy.screen.size()
while True:
# 1. Find hand Landmarks
success, img = cam.read()
img = detector.findHands(img)
lmList, bbox = detector.findPosition(img)
# 2. Get the tip of the index and thump
if len(lmList) != 0:
x1, y1 = lmList[8][1:]
x2, y2 = lmList[4][1:]
# 3. Check which fingers are up
fingers = detector.fingersUp()
cv2.rectangle(img, (frameR, frameR), (wCam - frameR, 250),
(255, 0, 255), 2)
# 4. Only Index Finger : Moving Mode
if fingers[1] == 1:
# 5. Convert Coordinates
x3 = np.interp(x1, (frameR, wCam - frameR), (0, wScr))
y3 = np.interp(y1, (frameR, 250), (0, hScr))
# 6. Smoothen Values
clocX = plocX + (x3 - plocX) / smoothening
clocY = plocY + (y3 - plocY) / smoothening
# 7. Move Mouse
autopy.mouse.move(wScr - clocX, clocY)
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
plocX, plocY = clocX, clocY
# 8. Find distance between fingers
length, img, lineInfo = detector.findDistance(4, 8, img)
length_2, img, lineInfo_2 = detector.findDistance(8, 20, img)
# 9. Click mouse if distance long / Short
if length > 125:
cv2.circle(img, (lineInfo[4], lineInfo[5]),
15, (0, 255, 0), cv2.FILLED)
mouse.click('left')
if length_2 < 115:
mouse.click('right')
# 10. Display
cv2.imshow("Image", img)
cv2.waitKey(1)
And the error :
Traceback (most recent call last):
File "Second.py", line 16, in <module>
detector = htm.HandDetector(max_num_hands=1)
File "first.py", line 23, in __init__
self.trackCon)
File "mediapipe\python\solutions\hands.py", line 129, in __init__
File "mediapipe\python\solution_base.py", line 238, in __init__
FileNotFoundError: The path does not exist.
I solved it this way on windows10.
sample *.spec:
block_cipher = None
def get_mediapipe_path():
import mediapipe
mediapipe_path = mediapipe.__path__[0]
return mediapipe_path
pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
mediapipe_tree = Tree(get_mediapipe_path(), prefix='mediapipe', excludes=["*.pyc"])
a.datas += mediapipe_tree
a.binaries = filter(lambda x: 'mediapipe' not in x[0], a.binaries)
1st the spec file is created and than according to this the application is buit.
So you 1st run the command :
pyinstaller --onefile main.py
This will buid the application file (not in working condition) and a spec file. Delete everything except the spec file and do changes in spec file according to https://python.tutorialink.com/issues-compiling-mediapipe-with-pyinstaller-on-macos/
.
Now we will build the application file according to this updated spec file using command : pyinstaller main.spec
(main is the name of the spec file). Now a working exe file is built.
I have been following some tutorials online on how to make a hand tracking program which can control the volume of my PC via OpenCV. I am on a macbook pro with the M1 chip, using Python 3.8.2, and MacOS Big Sur 11.6.
Whenever I run my program, I get 15 FPS instead of the usual 30 that most people get when following the tutorial. Further investigation showed that only 1 line of code is causing my fps to drop from 30 to 15.
self.results = self.hands.process(imgRGB)
Does anyone know any way to improve my FPS, and why this line causes such a massive drop in FPS? Full code is below
First part here is the hand detection script, where the problematic line of code is (under FindHands function):
import cv2
import mediapipe as mp
import time
class handDetector():
def __init__(self, mode=False, maxHands=2, modelComplexity=1, detectionCon=0.8, trackCon=0.8):
self.mode = mode
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.mpDraw = mp.solutions.drawing_utils
def findHands(self,img, draw = True):
imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
self.results = self.hands.process(imgRGB)
# print(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):
lmlist = []
if self.results.multi_hand_landmarks:
myHand = self.results.multi_hand_landmarks[handNo]
for id, lm in enumerate(myHand.landmark):
h, w, c = img.shape
cx, cy = int(lm.x * w), int(lm.y * h)
lmlist.append([id, cx, cy])
if draw:
cv2.circle(img, (cx, cy), 7, (255, 0, 255), cv2.FILLED)
return lmlist
def main():
pTime = 0
cTime = 0
cap = cv2.VideoCapture(0)
detector = handDetector()
while True:
success, img = cap.read()
img = detector.findHands(img)
lmlist = 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()
This next part is the script that controls the volume control, which on another note further reduces my FPS to 5 when a hand is in frame, which is apparently due to Osascript not being very good:
import cv2
import time
import numpy as np
import DoneHand as dh
import math
import osascript
minVol = 0
maxVol = 100
wCam, hCam = 640, 480
pTime = 0
cap = cv2.VideoCapture(0)
detector = dh.handDetector()
while True:
success, img = cap.read()
img = detector.findHands(img)
lmList = detector.findPosition(img, draw = False)
if len(lmList) != 0:
#print(lmList[4], lmList[8])
x1, y1 = lmList[4][1], lmList[4][2]
x2, y2 = lmList[8][1], lmList[8][2]
cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)
cv2.circle(img, (cx, cy), 15, (255, 0, 255), cv2.FILLED)
cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
length = math.hypot(x2 - x1, y2 - y1)
print(length)
#Hand range 55 - 350
#Volume range 0 - 100
vol = np.interp(length, [55, 350], [minVol, maxVol])
print(vol)
target_volume = vol
#osascript.osascript("set volume output volume {}".format(target_volume))
if length < 55:
cv2.circle(img, (cx, cy), 15, (0, 255, 0), cv2.FILLED)
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("Img", img)
cv2.waitKey(1)
Thank you in advance!
I'm new to this and I tried googling but
I can't seem to fix this error inside "fingersUp" function.
Basically, each part of a finger has indexes and this function checks if the thumb finger is up based on these indexes. Each finger has 4 points.
I'm using PyCharm and Python 3.8, if that helps.
if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0] - 1][1]: IndexError: list index out of range
Here's the full code:
import cv2
import mediapipe as mp
import time
import math
import numpy as np
class handDetector():
def __init__(self, mode=False, maxHands=2, detectionCon=0.5, trackCon=0.5):
self.mode = mode
self.maxHands = maxHands
self.detectionCon = detectionCon
self.trackCon = trackCon
self.mpHands = mp.solutions.hands
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)
self.results = self.hands.process(imgRGB)
# print(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 = []
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
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
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 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:
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()
def fingersUp(self):
fingers = []
if len(self.lmList):
if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0]-1][1]:
fingers.append(1)
else:
fingers.append(0)
for id in range(1,5):
if self.lmList[self.tipIds[id]][2]< self.lmList[self.tipIds[id]-2][2]:
fingers.append(1)
else:
fingers.append(0)
else:
fingers = [0,0,0,0,0]
return fingers
I had this same issue. Once adding Try: Except: around the if else statements in the fingersUp function I had no issue. The error is there because the fuction is being run even when your hands are off the screen.
Try:
if self.lmList[self.tipIds[0]][1] > self.lmList[self.tipIds[0] - 1][1]:
fingers.append(1)
else:
fingers.append(0)
Except:
fingers.append(0)
Try, this line:
the brackets aren't placed right that is why it shows the error.
if (self.lmList[self.tipIds[0][1]]
enter image description here
1.Indentation
2. Missing "_ "
When I run the code below, I get the following error:
Traceback (most recent call last):
File "path\gesture_mouse.py", line 63, in
cv2.circle(img, (cx, cy), (w+h)/4, (0, 0, 255), 2)
TypeError: integer argument expected, got float
[ WARN:0] terminating async callback
I tried assigning the center and the radius separately pre-defined.
import cv2
import numpy as np
from pynput.mouse import Button, Controller
import wx
mouse = Controller()
app = wx.App(False)
(sx, sy) = wx.GetDisplaySize()
(camx, camy) = (320, 240)
lowerBound = np.array([33, 80, 40])
upperBound = np.array([102, 255, 255])
cam = cv2.VideoCapture(0)
kernelOpen = np.ones((5, 5))
kernelClose = np.ones((20, 20))
pinchFlag = 0
while True:
ret, img = cam.read()
img=cv2.resize(img, (340, 220))
# convert BGR to HSV
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# create the Mask
mask = cv2.inRange(imgHSV, lowerBound, upperBound)
# morphology
maskOpen = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernelOpen)
maskClose = cv2.morphologyEx(maskOpen, cv2.MORPH_CLOSE, kernelClose)
maskFinal = maskClose
conts, h = cv2.findContours(maskFinal.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(conts) == 2:
if pinchFlag == 1:
pinchFlag = 0
mouse.release(Button.left)
x1, y1, w1, h1 = cv2.boundingRect(conts[0])
x2, y2, w2, h2 = cv2.boundingRect(conts[1])
cv2.rectangle(img, (x1, y1), (x1+w1, y1+h1), (255, 0, 0), 2)
cv2.rectangle(img, (x2, y2), (x2+w2, y2+h2), (255, 0, 0), 2)
cx1 = x1+w1/2
cy1 = y1+h1/2
cx2 = x2+w2/2
cy2 = y2+h2/2
cx = (cx1+cx2)/2
cy = (cy1+cy2)/2
cv2.line(img, (cx1, cy1), (cx2, cy2), (255, 0, 0), 2)
cv2.circle(img, (cx, cy), 2, (0, 0, 255), 2)
mouseLoc = (sx-(cx*sx/camx), cy*sy/camy)
mouse.position = mouseLoc
while mouse.position != mouseLoc:
pass
elif len(conts) == 1:
x, y, w, h = cv2.boundingRect(conts[0])
if pinchFlag == 0:
pinchFlag = 1
mouse.press(Button.left)
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cx = x+w/2
cy = y+h/2
cv2.circle(img, (cx, cy), (w+h)/4, (0, 0, 255), 2)
mouseLoc = (sx-(cx*sx/camx), cy*sy/camy)
mouse.position = mouseLoc
while mouse.position != mouseLoc:
pass
cv2.imshow("cam", img)
cv2.waitKey(5)
I was expecting that when two green color objects appear in the frame they will be scribed in a blue box, and a blue line from the center of the two box will appear where a red dot will be present signifying the location of the cursor(mouse).
Like the error says, the function needs int's as input, but you input floats. This is because the values are the result of division calculations.
print(type(10)) #<class 'int'>
print(type(5)) #<class 'int'>
print(type(10/5)) #<class 'float'>
print(10/5) #2.0
The solution is the convert the calculated values to integers:
cv2.circle(img, (int(cx), int(cy), int((w+h)/4), (0, 0, 255), 2)
def detect_video(image):
gray=image
blob = cv2.dnn.blobFromImage(gray, 1.0, (300, 300), [104, 117, 123], False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
gray=cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)
frameWidth=image.shape[1]
frameHeight=image.shape[0]
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > 0.7:
x1 = int(detections[0, 0, i, 3] * frameWidth)
y1 = int(detections[0, 0, i, 4] * frameHeight)
x2 = int(detections[0, 0, i, 5] * frameWidth)
y2 = int(detections[0, 0, i, 6] * frameHeight)
cv2.rectangle(image,(x1,y1),(x2,y2),(255,255,0),3)
try:
image1 = gray[y1:(y2), x1:(x2)]
img = cv2.resize(image1, (48,48), interpolation = cv2.INTER_CUBIC) / 255.
prediction=model1.predict_proba(img.reshape(1,48,48,1))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image,str(emotions[prediction[0].argmax()]),(x1,y1+10), font, 1,(255,255,255),2,cv2.LINE_AA)
result=prediction
if result is not None:
if result[0][6] < 0.6:
result[0][6] = result[0][6] - 0.12
result[0][:3] += 0.01
result[0][4:5] += 0.04
# write the different emotions and have a bar to indicate probabilities for each class
for index, emot in enumerate(emotion):
cv2.putText(image, emot, (10, index * 20 + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
cv2.rectangle(image, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4), (255, 0, 0), -1)
emt=[prediction[0][0],prediction[0][1],prediction[0][2],prediction[0][3],prediction[0][4],prediction[0][5],prediction[0][6]]
indx=np.arange(len(emotion))
plt.bar(indx,emt,color='blue')
plt.xticks(indx,emotion)
plt.savefig("ab.png")
cv2.imshow("graph",cv2.imread("ab.png"))
plt.clf()
#cv2.waitKey(5)
#plt.show()
#return indx,emt
except:
#print("----->Problem during resize .Probably Cant detect any face")
continue
return image
I have made my own model and trained on KDEF dataset.Now when I am giving the video as an input , it detects the face in the video but it makes two bounding boxes.Can anyone help me whats the mistake in the code.Its running successfully but just creating two bounding boxes.The input which the neural networks accepts is 48*48.
first select the detection which has the most significant confidence then draw it on image.
detection_index = 0
max_confidence = 0
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if max_confidence < confidence:
max_confidence = confidence
detection_index = i
i = detection_index
x1 = int(detections[0, 0, i, 3] * frameWidth)
y1 = int(detections[0, 0, i, 4] * frameHeight)
x2 = int(detections[0, 0, i, 5] * frameWidth)
y2 = int(detections[0, 0, i, 6] * frameHeight)
cv2.rectangle(image, (x1, y1), (x2, y2), (255, 255, 0), 3)
try:
image1 = gray[y1:(y2), x1:(x2)]
img = cv2.resize(image1, (48, 48), interpolation=cv2.INTER_CUBIC) / 255.
prediction = model1.predict_proba(img.reshape(1, 48, 48, 1))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(image, str(emotions[prediction[0].argmax()]), (x1, y1 + 10), font, 1, (255, 255, 255), 2, cv2.LINE_AA)
result = prediction
if result is not None:
if result[0][6] < 0.6:
result[0][6] = result[0][6] - 0.12
result[0][:3] += 0.01
result[0][4:5] += 0.04
# write the different emotions and have a bar to indicate probabilities for each class
for index, emot in enumerate(emotion):
cv2.putText(image, emot, (10, index * 20 + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
cv2.rectangle(image, (130, index * 20 + 10), (130 + int(result[0][index] * 100), (index + 1) * 20 + 4),
(255, 0, 0), -1)
emt = [prediction[0][0], prediction[0][1], prediction[0][2], prediction[0][3], prediction[0][4],
prediction[0][5], prediction[0][6]]
indx = np.arange(len(emotion))
plt.bar(indx, emt, color='blue')
plt.xticks(indx, emotion)
plt.savefig("ab.png")
cv2.imshow("graph", cv2.imread("ab.png"))
plt.clf()
# cv2.waitKey(5)
# plt.show()
# return indx,emt
except:
# print("----->Problem during resize .Probably Cant detect any face")
continue
return image