Modify hand tracking, add right click and scrolling
This commit is contained in:
Piotr Radzewicz
parent
b8809db927
commit
b09e5fc6f9
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@ -1,66 +1,294 @@
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import cv2
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import numpy
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import math
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import numpy as np
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import pyautogui
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import pynput.mouse
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def openCamera(input):
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cv2.imshow("frame", input)
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key = cv2.waitKey(1)
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return key
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class FingersNumberDetector:
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def drawBox(finder, frame, b, g, r):
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for x, y, w, h in finder:
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cv2.rectangle(frame, (x, y), (x + w, y + h) ,(b, g, r), 2)
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def __init__(self):
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pyautogui.PAUSE = 0
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def exit(video):
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video.release()
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cv2.destroyAllWindows()
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self.isHandHistCreated = False
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self.isBgCaptured = False
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self.bgSubThreshold = 30
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self.dx = 0
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self.dy = 0
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self.mouse = pynput.mouse.Controller()
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# Background subtractor learning rate
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self.bgSubtractorLr = 0
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self.xs = [6.0/20.0, 9.0/20.0, 12.0/20.0]
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self.ys = [9.0/20.0, 10.0/20.0, 11.0/20.0]
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# Gamma correction lookUpTable
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# Increase the contrast
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gamma = 3
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self.lookUpTable = np.empty((1, 256), np.uint8)
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for i in range(256):
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self.lookUpTable[0, i] = np.clip(
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pow(i / 255.0, gamma) * 255.0, 0, 255)
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def createHandHistogram(self, frame):
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rows, cols, _ = frame.shape
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hsvFrame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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roi = np.zeros([180, 20, 3], dtype=hsvFrame.dtype)
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i = 0
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for x in self.xs:
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for y in self.ys:
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x0, y0 = int(x*rows), int(y*cols)
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roi[i*20:i*20 + 20, :, :] = hsvFrame[x0:x0 + 20, y0:y0 + 20, :]
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i += 1
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handHist = cv2.calcHist([roi], [0, 1], None, [
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180, 256], [0, 180, 0, 256])
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return cv2.normalize(handHist, handHist, 0, 255, cv2.NORM_MINMAX)
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def drawRect(self, frame):
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rows, cols, _ = frame.shape
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for x in self.xs:
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for y in self.ys:
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x0, y0 = int(x*rows), int(y*cols)
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cv2.rectangle(frame, (y0, x0),
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(y0 + 20, x0 + 20), (0, 255, 0), 1)
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def histMasking(self, frame, handHist):
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"""Create the HSV masking"""
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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dst = cv2.calcBackProject([hsv], [0, 1], handHist, [0, 180, 0, 256], 1)
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disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (21, 21))
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cv2.filter2D(dst, -1, disc, dst)
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ret, thresh = cv2.threshold(dst, 150, 255, cv2.THRESH_BINARY)
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kernel = np.ones((5, 5), np.uint8)
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thresh = cv2.morphologyEx(
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thresh, cv2.MORPH_CLOSE, kernel, iterations=7)
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# thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=5)
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# thresh = cv2.dilate(thresh, kernel, iterations=5)
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# thresh = cv2.erode(thresh, kernel, iterations=5)
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thresh = cv2.merge((thresh, thresh, thresh))
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return cv2.bitwise_and(frame, thresh)
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def getCentroid(self, contour):
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moment = cv2.moments(contour)
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if moment['m00'] != 0:
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cx = int(moment['m10']/moment['m00'])
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cy = int(moment['m01']/moment['m00'])
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return [cx, cy]
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else:
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return None
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def getMaxContours(self, contours):
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maxIndex = 0
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maxArea = 0
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for i in range(len(contours)):
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cnt = contours[i]
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area = cv2.contourArea(cnt)
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if area > maxArea:
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maxArea = area
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maxIndex = i
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return contours[maxIndex]
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def threshold(self, mask):
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grayMask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(grayMask, 0, 255, 0)
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return thresh
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def bgSubMasking(self, frame):
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"""Create a foreground (hand) mask
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@param frame: The video frame
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@return: A masked frame
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"""
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fgmask = self.bgSubtractor.apply(
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frame, learningRate=self.bgSubtractorLr)
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kernel = np.ones((4, 4), np.uint8)
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# MORPH_OPEN removes noise
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# MORPH_CLOSE closes the holes in the object
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fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel, iterations=2)
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fgmask = cv2.morphologyEx(
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fgmask, cv2.MORPH_CLOSE, kernel, iterations=2)
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return cv2.bitwise_and(frame, frame, mask=fgmask)
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def getMaskAreaRatio(self, mask):
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grayMask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
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ret, thresh = cv2.threshold(grayMask, 0, 255, 0)
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return np.sum(thresh)/(self.height*self.width*255)
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def setupFrame(self, frame_width, frame_height):
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"""self.x0 and self.y0 are top left corner coordinates
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self.width and self.height are the width and height the ROI
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"""
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x, y = 0.0, 0.4
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self.x0 = int(frame_width*x)
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self.y0 = int(frame_height*y)
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self.width = 450
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self.height = 450
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def countFingers(self, contour, contourAndHull):
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hull = cv2.convexHull(contour, returnPoints=False)
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if len(hull) > 3:
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defects = cv2.convexityDefects(contour, hull)
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cnt = 0
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if type(defects) != type(None):
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for i in range(defects.shape[0]):
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s, e, f, d = defects[i, 0]
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start = tuple(contour[s, 0])
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end = tuple(contour[e, 0])
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far = tuple(contour[f, 0])
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angle = self.calculateAngle(far, start, end)
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# Ignore the defects which are small and wide
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# Probably not fingers
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if d > 10000 and angle <= math.pi/2:
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cnt += 1
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cv2.circle(contourAndHull, far, 8, [255, 0, 0], -1)
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return True, cnt
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return False, 0
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def calculateAngle(self, far, start, end):
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"""Cosine rule"""
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a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
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b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
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c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
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angle = math.acos((b**2 + c**2 - a**2) / (2*b*c))
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return angle
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def execute(self, cnt):
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if cnt == 1:
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self.mouse.click(pynput.mouse.Button.left, 1)
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if cnt == 2:
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self.mouse.click(pynput.mouse.Button.right, 1)
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if cnt == 3:
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pyautogui.press("down")
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elif cnt == 4:
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pyautogui.press("up")
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def detectHand(self, frame, handHist):
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roi = frame[self.y0:self.y0 + self.height,
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self.x0:self.x0 + self.width,
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:]
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roi = cv2.bilateralFilter(roi, 5, 50, 100)
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# Color masking
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histMask = self.histMasking(roi, handHist)
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cv2.imshow("histMask", histMask)
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# Background substraction
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bgSubMask = self.bgSubMasking(roi)
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cv2.imshow("bgSubMask", bgSubMask)
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# Attempt to learn the background automatically
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"""
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areaRatio = self.getMaskAreaRatio(bgSubMask)
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if areaRatio > 0.6:
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self.bgSubtractorLr = 1
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elif areaRatio < 0.001:
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self.bgSubtractorLr = 0
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"""
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# Overall mask
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mask = cv2.bitwise_and(histMask, bgSubMask)
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thresh = self.threshold(mask)
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cv2.imshow("Overall thresh", thresh)
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contours, hierarchy = cv2.findContours(
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thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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if len(contours) > 0:
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maxContour = self.getMaxContours(contours)
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M = cv2.moments(maxContour)
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if M["m00"] != 0:
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cX = int(M["m10"] / M["m00"])
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cY = int(M["m01"] / M["m00"])
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if self.dx != 0:
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self.mouse.move( -1 * 2 * (cX -self.dx), 2 * (cY - self.dy))
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self.dx = cX
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self.dy = cY
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# print(cX, cY)
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# Draw contour and hull
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contourAndHull = np.zeros(roi.shape, np.uint8)
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hull = cv2.convexHull(maxContour)
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# print(type(hull))
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cv2.drawContours(contourAndHull, [maxContour], 0, (0, 255, 0), 2)
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cv2.drawContours(contourAndHull, [hull], 0, (0, 0, 255), 3)
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found, cnt = self.countFingers(maxContour, contourAndHull)
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cv2.imshow("Contour and Hull", contourAndHull)
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if found:
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self.execute(cnt)
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centroid = self.getCentroid(maxContour)
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if centroid is not None:
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centroid[0] += self.x0
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centroid[1] += self.y0
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cv2.circle(frame, tuple(centroid), 5, [255, 0, 0], -1)
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def startDetecting(self):
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cap = cv2.VideoCapture(0)
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frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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self.setupFrame(frame_width, frame_height)
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while cap.isOpened():
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ret, frame = cap.read()
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# Increase the contrast
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# frame = cv2.convertScaleAbs(frame, alpha=3, beta=-500)
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# Gamma corection
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# Increase the contrast
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frame = cv2.LUT(frame, self.lookUpTable)
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cv2.rectangle(frame, (self.x0, self.y0), (self.x0 +
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self.width - 1, self.y0 + self.height - 1), (255, 0, 0), 2)
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k = cv2.waitKey(1) & 0xFF
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if k == ord("z"):
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self.isHandHistCreated = True
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handHist = self.createHandHistogram(frame)
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elif k == ord('b'):
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self.bgSubtractor = cv2.createBackgroundSubtractorMOG2(
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30, self.bgSubThreshold)
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self.isBgCaptured = True
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elif k == ord("r"):
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self.bgSubtractor = None
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self.isBgCaptured = False
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if self.isHandHistCreated and self.isBgCaptured:
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self.detectHand(frame, handHist)
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elif not self.isHandHistCreated:
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self.drawRect(frame)
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cv2.imshow("Output", frame)
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if k == ord("q"):
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break
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elif k == ord("j"):
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self.y0 = min(self.y0 + 20, frame_height - self.height)
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elif k == ord("k"):
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self.y0 = max(self.y0 - 20, 0)
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elif k == ord("h"):
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self.x0 = max(self.x0 - 20, 0)
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elif k == ord("l"):
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self.x0 = min(self.x0 + 20, frame_width - self.width)
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cap.release()
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cv2.destroyAllWindows()
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mouse = pynput.mouse.Controller()
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video = cv2.VideoCapture(0)
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frame = video.read()
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findFistInit = None
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centerX = 0
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centerY = 0
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while True:
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cascadeHand = cv2.CascadeClassifier("hand.xml")
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cascadeFist = cv2.CascadeClassifier("fist.xml")
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frame = video.read()[1]
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fmask = cv2.GaussianBlur(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (21,21), 0)
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cv2.imshow("fmask", fmask)
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findFist = cascadeFist.detectMultiScale(fmask, scaleFactor = 1.1, minNeighbors = 75)
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if findFistInit is None:
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findFistInit = findFist
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if findFist == ():
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findHand = cascadeHand.detectMultiScale(fmask, scaleFactor = 1.1, minNeighbors = 10)
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if findHand != ():
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mouse.click(pynput.mouse.Button.left, 1)
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if findFist != () and findFistInit != ():
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dx, dy, dw, dh = findFist[0][0] - findFistInit[0][0], findFist[0][1] - findFistInit[0][1], findFist[0][2] - findFistInit[0][2], findFist[0][3] - findFistInit[0][3]
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centerX = (dx + dx + dw) / 2
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centerY = (dy + dy + dh) / 2
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mouse.move( -1 * 7 * dx, 7 * dy)
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findFistInit = findFist
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drawBox(findFist, frame, 0, 0, 255)
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drawBox(findHand, frame, 0, 255, 0)
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frame = cv2.flip(frame, 1)
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key = openCamera(frame)
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if(key == ord('q')):
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break
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exit(video)
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if __name__ == "__main__":
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detector = FingersNumberDetector()
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detector.startDetecting()
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