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first_draft.py
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first_draft.py
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# Xing @ 2018.11.27
import cv2
import numpy as np
import time
# Initialize webcam input
cap = cv2.VideoCapture(1)
# Initialize video input
#cap = cv2.VideoCapture("C:/Users/liangx/Documents/Dunhill Project Data/Single Sign/Pronated Wrist/WATCH2.mp4")
#cap = cv2.VideoCapture("C:/Users/liangx/Documents/Dunhill Project Data/Single Sign/Stomach/FARM.mp4")
#cap = cv2.VideoCapture("C:/Users/liangx/Documents/Dunhill Project Data/Single Sign/Below Waist/LAP.mp4")
# Set Tracking Delay (i.e. delay in number of frames) to wait for KNN background subtraction work (Camera: 25; Video: 5)
DELAY= 5
# Set countour radius to denoise, only contours which is big enough will be tracked
RADIUS = 25
# Set frame count for tracking trails reset, if there is no hands being detected
frame_count = 0
# Get video/camera input details
"""
lengthVideo = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
widthVideo = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
heightVideo = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# fps for video input with a stream header (need nb_frames field)
fpsVideo = int(cap.get(cv2.CAP_PROP_FPS))
# fps for camera input without a stream header
# Number of frames to capture
num_frames = 120;
print ("Capturing {0} frames".format(num_frames))
# Start time
start = time.time()
# Grab a few frames
for i in range(0, num_frames):
ret, frame = cap.read()
# End time
end = time.time()
# Time elapsed
seconds = end - start
print ("Time taken : {0} seconds".format(seconds))
# Calculate frames per second
fpsCamera = int(num_frames / seconds)
print ("Estimated frames per second : {0}".format(fpsCamera))
"""
# define range of skin color in HSV
#lower_thresh = np.array([0, 50, 0])
#upper_thresh = np.array([120, 150, 255])
# define range of skin color in HSV (works better with brown skin)
lower_thresh = np.array([0, 48, 80], dtype = "uint8")
upper_thresh = np.array([20, 255, 255], dtype = "uint8")
# Create empty points array for hand trajectories tracking
points_left = []
points_right = []
# Initlaize KNN background subtractor
kernel_bgsub = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fgbg = cv2.createBackgroundSubtractorKNN()
# Sorting contour by area
def get_contour_areas(contours):
# returns the areas of all contours as list
all_areas = []
for cnt in contours:
area = cv2.contourArea(cnt)
all_areas.append(area)
return all_areas
# Sorting contour by position
def x_cord_contour(contours):
#Returns the X cordinate for the contour centroid
M = cv2.moments(contours)
return (int(M['m10']/M['m00']))
# CAMshift first frame window initialize
"""
# take first frame of the video
ret, frame = cap.read()
# setup default location of window
r, h, c, w = 240, 100, 400, 160
track_window = (c, r, w, h)
# Crop region of interest for tracking
roi = frame[r:r+h, c:c+w]
# Convert cropped window to HSV color space
hsv_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_roi, lower_thresh, upper_thresh)
# Obtain the color histogram of the ROI
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0,180])
# Normalize values to lie between the range 0, 255
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
# Setup the termination criteria
# We stop calculating the centroid shift after ten iterations
# or if the centroid has moved at least 1 pixel
term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
"""
# Loop video capture until break statement is exectured
while cap.isOpened():
# Read webcam/video image
ret, frame = cap.read()
# when there is a video input
if ret == True:
# Get default camera/video window size
Height, Width = frame.shape[:2]
# Convert image from RBG/BGR to HSV
hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Face Detection Using HAAR CASCADE
hc_face = cv2.CascadeClassifier("C:/Users/liangx/source/repos/Skin Detection/haarcascade_frontalface_alt/haarcascade_frontalface_alt.xml")
faces = hc_face.detectMultiScale(hsv_img)
for (x,y,w,h) in faces:
cv2.rectangle(hsv_img, (x,y), (x+w,y+h), 255, thickness=2)
crop_img = frame[y+2:y+w, x+2:x+h]
cv2.imshow('Face Detection', crop_img)
# Use inRange to capture only the values between lower & upper_thresh for skin detection
mask = cv2.inRange(hsv_img, lower_thresh, upper_thresh)
# Adding morphology effects to denoise
kernel_morphology =np.ones((5, 5), np.uint8)
mask = cv2.erode(mask, kernel_morphology, iterations=1)
#mask=cv2.morphologyEx(mask,cv2.MORPH_OPEN, kernel_morphology)
mask=cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel_morphology)
mask = cv2.dilate(mask, kernel_morphology, iterations=1)
cv2.imshow('Skin colour Mask', mask)
# Perform Bitwise AND on mask and original frame
# rest1 is the results after applying morphology effects
rest1 = cv2.bitwise_and(frame, frame, mask= mask)
# Apply KKN background subtraction to refine skin filtering result, i.e. to further remove static skin coulor related background (face will be fading out, if it does not move)
fgmask = fgbg.apply(rest1)
fgmask = cv2.morphologyEx(fgmask, cv2.MORPH_OPEN, kernel_bgsub)
cv2.imshow('Background subtraction Mask',fgmask)
# Perform Bitwise AND on fgmask and rest1 frame
# rest2 is results after applying background subtraction and morphology effects
rest2 = cv2.bitwise_and(rest1, rest1, mask= fgmask)
# Find contours
# cv2.RETR_EXTERNAL finds external contours only; cv2.CHAIN_APPROX_SIMPLE only provides start and end points of bounding contours, thus resulting in much more efficent storage of contour information.
_, contours, _ = cv2.findContours(fgmask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print ("Number of contours1 found = ", len(contours))
#print(type(contours)) #The variable 'contours' are stored as a numpy array of (x,y) points that form the contour
# Find Canny Edges (not helpful!)
"""
edged = cv2.Canny(rest2, 30, 240)
cv2.imshow('Canny Edges', edged)
# Find contours
# cv2.RETR_EXTERNAL finds external contours only
# cv2.CHAIN_APPROX_SIMPLE only provides start and end points of bounding contours, thus resulting in much more efficent storage of contour information.
_, contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print ("Number of contours2 found = ", len(contours))
#print(type(contours)) #The variable 'contours' are stored as a numpy array of (x,y) points that form the contour
#Draw all contours
"""
# Draw all Contours found
#cv2.drawContours(rest2, contours, -1, (0,255,0), 3)
#cv2.imshow('All Contours filtered by skin color and background subtraction', rest2)
#cv2.imshow('Original', frame)
# CAMshift
"""
hsv= cv2.cvtColor(rest2, cv2.COLOR_BGR2HSV)
# Calculate the histogram back projection
# Each pixel's value is it's probability
dst = cv2.calcBackProject([hsv],[0],roi_hist,[0,180],1)
# apply Camshift to get the new location
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
# Draw it on image
# We use polylines to represent Adaptive box
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
img2 = cv2.polylines(frame,[pts],True, 255,2)
cv2.imshow('Camshift Tracking', img2)
"""
# Create empty centre array to store centroid center of mass
center = int(Height*2/3), int(Width*2/3)
# When both hands are detected
if len(contours) >=2:
# Get the largest two contours and its center (i.e. two hands)
sorted_contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2]
# Sort by left to right using our x_cord_contour function (i.e. hands tracking from left to right)
contours_left_to_right = sorted(sorted_contours, key = x_cord_contour, reverse = False)
# Iterate over two contours and draw one at a time
for (i,c) in enumerate(contours_left_to_right):
# Draw Convex Hull Contour
hull=cv2.convexHull(c)
cv2.drawContours(rest2, [hull], -1, (0,0,255), 3)
# Draw Normal Contour
cv2.drawContours(rest2, [c], -1, (255,0,0), 3)
# Show hands Contour
cv2.imshow('Contours by area', rest2)
# Tracking Left hand
if i == 0:
(x, y), radius = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
# Draw cirlce and leave the last center creating a trail
cv2.circle(frame, (int(x), int(y)), int(radius),(0, 0, 255), 2)
# Only contours with radius > RADIUS are tracked (de-noise)
if radius > RADIUS:
points_left.append(center)
# loop over the set of tracked points to draw tracking lines (starts with frames delay- approximately 1 seconds delay to make KNN background subtraction work)
for l in range(DELAY, len(points_left)):
try:
cv2.line(frame, points_left[l - 1], points_left[l], (0, 0, 255), 2)
except:
pass
frame_count = 0
else:
# If there is no hand detected, count frames
frame_count += 1
print("frame_count",frame_count)
# when frame_count reaches 20, clear our trails
if frame_count == 5:
points_left = []
points_right = []
frame_count = 0
# Tracking Right hand
elif i == 1:
(x, y), radius = cv2.minEnclosingCircle(c)
M = cv2.moments(c)
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
# Draw cirlce and leave the last center creating a trail
cv2.circle(frame, (int(x), int(y)), int(radius),(0, 255, 0), 2)
# loop over the set of tracked points
if radius > RADIUS:
points_right.append(center)
for l in range(DELAY, len(points_right)):
try:
cv2.line(frame, points_right[l - 1], points_right[l], (0, 255, 0), 2)
except:
pass
frame_count = 0
else:
# If there is no hand detected, count frames
frame_count += 1
# when frame_count reaches 50, clear our trails
if frame_count == 50:
points_left = []
points_right = []
frame_count = 0
else:
pass
else:
pass
# Display our object tracker
frame = cv2.flip(frame, 1)
cv2.imshow("Object Tracker", frame)
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
else:
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
cap.release()
cv2.destroyAllWindows()