จากภาพจะพบว่าการทำงานจะเป็นการตรวจสอบเฉพาะบริเวณกรอบสี่เหลี่ยมที่เรากำหนดไว้ โดยภาพที่ได้มาก็จะมีลักษณะตามกรอบด้านซ้ายซึ่งเห็นเป็นรูปมือได้ค่อนข้างชัดเจน
ปัญหาที่พบคือ เนื่องจากตัวกล้องนั้นไม่ใช่ Depth Camera การกำหนดระยะ Threshold ในโค้ดจึงช่วยให้การตรวจจับยังไม่ดีเท่าที่ควร อีกทั้งหากพื้นหลังมีแสงรบกวนมากหรือไม่ใช่พื้นที่ราบเรียบ การตรวจจับจะเพี้ยนไปดังรูปด้านบน
ตัวอย่างโค้ดที่นำมาทดลอง
# organize imports
import cv2
import imutils
import numpy as np
# global variables
bg = None
#-------------------------------------------------------------------------------
# Function - To find the running average over the background
#-------------------------------------------------------------------------------
def run_avg(image, aWeight):
global bg
# initialize the background
if bg is None:
bg = image.copy().astype("float")
return
# compute weighted average, accumulate it and update the background
cv2.accumulateWeighted(image, bg, aWeight)
#-------------------------------------------------------------------------------
# Function - To segment the region of hand in the image
#-------------------------------------------------------------------------------
def segment(image, threshold=25):
global bg
# find the absolute difference between background and current frame
diff = cv2.absdiff(bg.astype("uint8"), image)
# threshold the diff image so that we get the foreground
thresholded = cv2.threshold(diff,
threshold,
255,
cv2.THRESH_BINARY)[1]
# get the contours in the thresholded image
(_, cnts, _) = cv2.findContours(thresholded.copy(),
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# return None, if no contours detected
if len(cnts) == 0:
return
else:
# based on contour area, get the maximum contour which is the hand
segmented = max(cnts, key=cv2.contourArea)
return (thresholded, segmented)
#-------------------------------------------------------------------------------
# Main function
#-------------------------------------------------------------------------------
if __name__ == "__main__":
# initialize weight for running average
aWeight = 0.5
# get the reference to the webcam
camera = cv2.VideoCapture(0)
# region of interest (ROI) coordinates
top, right, bottom, left = 10, 350, 225, 590
# initialize num of frames
num_frames = 0
# keep looping, until interrupted
while(True):
# get the current frame
(grabbed, frame) = camera.read()
# resize the frame
frame = imutils.resize(frame, width=700)
# flip the frame so that it is not the mirror view
frame = cv2.flip(frame, 1)
# clone the frame
clone = frame.copy()
# get the height and width of the frame
(height, width) = frame.shape[:2]
# get the ROI
roi = frame[top:bottom, right:left]
# convert the roi to grayscale and blur it
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# to get the background, keep looking till a threshold is reached
# so that our running average model gets calibrated
if num_frames < 30:
run_avg(gray, aWeight)
else:
# segment the hand region
hand = segment(gray)
# check whether hand region is segmented
if hand is not None:
# if yes, unpack the thresholded image and
# segmented region
(thresholded, segmented) = hand
# draw the segmented region and display the frame
cv2.drawContours(clone, [segmented + (right, top)], -1, (0, 0, 255))
cv2.imshow("Thesholded", thresholded)
# draw the segmented hand
cv2.rectangle(clone, (left, top), (right, bottom), (0,255,0), 2)
# increment the number of frames
num_frames += 1
# display the frame with segmented hand
cv2.imshow("Video Feed", clone)
# observe the keypress by the user
keypress = cv2.waitKey(1) & 0xFF
# if the user pressed "q", then stop looping
if keypress == ord("q"):
break
# free up memory
camera.release()
cv2.destroyAllWindows()
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