def main(algorithm: str, selected_mti: str, policy: str, train: bool = True,
tb_log: bool = False, csv_log: bool = True, model_id: str = None):
"""
:param algorithm: (str) 'A5C' or 'EA4C'
:param selected_mti: (str) Name of the multi-task instance
['SpaceInvaders-v0', 'CrazyClimber-v0', 'Seaquest-v0', 'DemonAttack-v0', 'StarGunner-v0']'
:param policy: (str) 'lstm' or 'ff' (feed forward)
:param train: (bool) Whether to train or play
:param tb_log: (bool) Whether to create tensorboard log or not. WARNING: IT LEAKS MEMORY IF IT IS TRUE
:param csv_log: (bool) Whether or not to save results in csv files
:param model_id: (str) or None Name of the model's directory which trying to load either for transfer learning or playing.
"""
if isinstance(selected_mti, str):
if selected_mti.lower() == 'mti1':
selected_mti = config.MTI1
elif selected_mti.lower() == 'mti2':
selected_mti = config.MTI2
elif selected_mti.lower() == 'mti3':
selected_mti = config.MTI3
elif selected_mti.lower() == 'mti4':
selected_mti = config.MTI4
elif selected_mti.lower() == 'mti5':
selected_mti = config.MTI5
elif selected_mti.lower() == 'mti6':
selected_mti = config.MTI6
elif selected_mti.lower() == 'mti7':
selected_mti = config.MTI7
elif selected_mti.lower() == 'mtic1':
selected_mti = config.MTIC1
elif selected_mti.lower() == 'mtic2':
selected_mti = config.MTIC2
elif selected_mti.lower() == 'mtic3':
selected_mti = config.MTIC3
else:
selected_mti = config.MTIC1
if train:
n_cpus = cpu_count()
dir_check()
if tb_log:
print("WARNING: TENSORBOARD LOGGING ACTIVE -> IT LEAKS MEMORY")
repo = git.Repo(search_parent_directories=True)
sha = repo.head.object.hexsha
json_params = {
'git_sha': sha,
}
mt = MultiTaskLearning(selected_mti, algorithm, policy, n_cpus, json_params,
csv_log, model_id, tensorboard_logging=tb_log)
mt.train()
else:
if model_id == '':
raise ValueError("If you want to play, you should provide a model")
MultiTaskAgent.play(model_id, n_games=1, display=True)
def dense_flow(fm):
# initialize variables
count = 0
x = y = w = h = 0
magnitude_histogram = []
direction_histogram = []
magnitude_histogram1 = []
direction_histogram1 = []
magnitude_histogram2 = []
direction_histogram2 = []
magnitude_histogram3 = []
direction_histogram3 = []
magnitude_histogram4 = []
direction_histogram4 = []
# start reading the video
cap = cv2.VideoCapture(fm)
# take the first frame and convert it to gray
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# create the HSV color image
hsvImg = np.zeros_like(frame)
hsvImg[..., 1] = 255
# play until the user decides to stop
while True:
# save the previous frame data
previousGray = gray
# get the next frame
ret , frame = cap.read()
if ret:
# background-subtraction
fgmask = fgbg.apply(frame)
# median-blur
seg_mask = cv2.medianBlur(fgmask, 5)
# dilation
seg_mask = cv2.dilate(seg_mask, kernel, iterations = 1)
# for drawing bounding box over the entire body
body = body_cascade.detectMultiScale(gray, 1.05, 3)
if(len(body)!=0):
for (x_t,y_t,w_t,h_t) in body:
x, y, w, h = x_t, y_t, w_t, h_t
# convert the frame to gray scale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# exception-handling
if((x, y, w, h) == (0 ,0, 0, 0)):
continue
# calculate the dense optical flow
flow = cv2.calcOpticalFlowFarneback(previousGray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
# obtain the flow magnitude and direction angle
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
mag = cv2.bitwise_and(mag, mag, mask = seg_mask)
ang = cv2.bitwise_and(ang, ang, mask = seg_mask)
# scaling
ang=((ang*180)/(np.pi/2))%180
# find the intersection points to draw the 2x2 grid
k=1
if(w%2==0):
k=0
c_x = x+(w//2)+k
k=1
if(h%2==0):
k=0
c_y = y+(h//2)+k
flag1=flag2=flag3=flag4=0
if(x-5>=0):
x-=5
flag1=1
if(x+w+10<ang.shape[1]):
w+=10
flag2=1
if(y-5>=0):
y-=5
flag3=1
if(y+h+10<ang.shape[0]):
h+=10
flag4=1
# extract the region-of-interests corresponding to the 2x2 grids
roi_mag1 = mag[y:c_y, x:c_x]
roi_mag2 = mag[y:c_y, c_x:x+w]
roi_mag3 = mag[c_y:y+h, x:c_x]
roi_mag4 = mag[c_y:y+h, c_x:x+w]
roi_dir1 = ang[y:c_y, x:c_x]
roi_dir2 = ang[y:c_y, c_x:x+w]
roi_dir3 = ang[c_y:y+h, x:c_x]
roi_dir4 = ang[c_y:y+h, c_x:x+w]
magnitude = np.array(mag).flatten()
direction = np.array(ang).flatten()
magnitude1 = np.array(roi_mag1).flatten()
direction1 = np.array(roi_dir1).flatten()
magnitude2 = np.array(roi_mag2).flatten()
direction2 = np.array(roi_dir2).flatten()
magnitude3 = np.array(roi_mag3).flatten()
direction3 = np.array(roi_dir3).flatten()
magnitude4 = np.array(roi_mag4).flatten()
direction4 = np.array(roi_dir4).flatten()
# create magnitude and direction optical flow histogram per frame for each grid
magnitude_histogram, direction_histogram = create_hist(magnitude, direction, magnitude_histogram, direction_histogram)
magnitude_histogram1, direction_histogram1 = create_hist(magnitude1, direction1, magnitude_histogram1, direction_histogram1)
magnitude_histogram2, direction_histogram2 = create_hist(magnitude2, direction2, magnitude_histogram2, direction_histogram2)
magnitude_histogram3, direction_histogram3 = create_hist(magnitude3, direction3, magnitude_histogram3, direction_histogram3)
magnitude_histogram4, direction_histogram4 = create_hist(magnitude4, direction4, magnitude_histogram4, direction_histogram4)
#---------------------------------------------------------#
# if you wish to see the optical flow frames uncomment the next 3 paragraphs
'''
# update the color image
hsvImg[..., 0] = 0.5 * ang * 180 / np.pi
hsvImg[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
rgbImg = cv2.cvtColor(hsvImg, cv2.COLOR_HSV2BGR)
# drawing the bounding box
cv2.rectangle(rgbImg, (x,y), (c_x,c_y), (255,0,0), 2)
cv2.rectangle(rgbImg, (c_x,y), (x+w,c_y), (0,255,0), 2)
cv2.rectangle(rgbImg, (x,c_y), (c_x,y+h), (0,0,255), 2)
cv2.rectangle(rgbImg, (c_x,c_y), (x+w,y+h), (255,255,0), 2)
# Display the resulting frame
cv2.imshow('dense optical flow', np.hstack((frame, rgbImg)))
'''
#---------------------------------------------------------#
# adjusting the bounding box over the POI to facilitate outward motion of the human body
if(flag1==1):
x+=5
if(flag2==1):
w-=10
if(flag3==1):
y+=5
if(flag4==1):
h-=10
k = cv2.waitKey(30) & 0xff
if k == 27:
break
else:
break
# check the magnitude and direction histograms to have expected shapes
magnitude_histogram = mag_check(magnitude_histogram)
magnitude_histogram1 = mag_check(magnitude_histogram1)
magnitude_histogram2 = mag_check(magnitude_histogram2)
magnitude_histogram3 = mag_check(magnitude_histogram3)
magnitude_histogram4 = mag_check(magnitude_histogram4)
direction_histogram = dir_check(direction_histogram)
direction_histogram1 = dir_check(direction_histogram1)
direction_histogram2 = dir_check(direction_histogram2)
direction_histogram3 = dir_check(direction_histogram3)
direction_histogram4 = dir_check(direction_histogram4)
# calculate the mean of the magnitude and direction histograms for each 2x2 grids
mag_avg_hist = np.mean(magnitude_histogram, axis=0)
dir_avg_hist = np.mean(direction_histogram, axis=0)
mag_avg_hist1 = np.mean(magnitude_histogram1, axis=0)
dir_avg_hist1 = np.mean(direction_histogram1, axis=0)
mag_avg_hist2 = np.mean(magnitude_histogram2, axis=0)
dir_avg_hist2 = np.mean(direction_histogram2, axis=0)
mag_avg_hist3 = np.mean(magnitude_histogram3, axis=0)
dir_avg_hist3 = np.mean(direction_histogram3, axis=0)
mag_avg_hist4 = np.mean(magnitude_histogram4, axis=0)
dir_avg_hist4 = np.mean(direction_histogram4, axis=0)
# calculate the standard deviation of the magnitude and direction histograms for each 2x2 grids
mag_std_hist = np.std(magnitude_histogram, axis=0)
dir_std_hist = np.std(direction_histogram, axis=0)
mag_std_hist1 = np.std(magnitude_histogram1, axis=0)
dir_std_hist1 = np.std(direction_histogram1, axis=0)
mag_std_hist2 = np.std(magnitude_histogram2, axis=0)
dir_std_hist2 = np.std(direction_histogram2, axis=0)
mag_std_hist3 = np.std(magnitude_histogram3, axis=0)
dir_std_hist3 = np.std(direction_histogram3, axis=0)
mag_std_hist4 = np.std(magnitude_histogram4, axis=0)
dir_std_hist4 = np.std(direction_histogram4, axis=0)
# concatenate all the histogram features to get the motion descriptor for 2x2 grids
histogram = mag_avg_hist
histogram = np.hstack((histogram, mag_std_hist))
histogram = np.hstack((histogram, dir_avg_hist))
histogram = np.hstack((histogram, dir_std_hist))
histogram = np.hstack((histogram, mag_avg_hist1))
histogram = np.hstack((histogram, mag_std_hist1))
histogram = np.hstack((histogram, dir_avg_hist1))
histogram = np.hstack((histogram, dir_std_hist1))
histogram = np.hstack((histogram, mag_avg_hist2))
histogram = np.hstack((histogram, mag_std_hist2))
histogram = np.hstack((histogram, dir_avg_hist2))
histogram = np.hstack((histogram, dir_std_hist2))
histogram = np.hstack((histogram, mag_avg_hist3))
histogram = np.hstack((histogram, mag_std_hist3))
histogram = np.hstack((histogram, dir_avg_hist3))
histogram = np.hstack((histogram, dir_std_hist3))
histogram = np.hstack((histogram, mag_avg_hist4))
histogram = np.hstack((histogram, mag_std_hist4))
histogram = np.hstack((histogram, dir_avg_hist4))
histogram = np.hstack((histogram, dir_std_hist4))
cv2.destroyAllWindows()
cap.release()
return histogram
def dense_flow(fm):
# initialize variables
count = 0
x = y = w = h = 0
magnitude_histogram = []
direction_histogram = []
magnitude_histogram1 = []
direction_histogram1 = []
magnitude_histogram2 = []
direction_histogram2 = []
magnitude_histogram3 = []
direction_histogram3 = []
magnitude_histogram4 = []
direction_histogram4 = []
magnitude_histogram5 = []
direction_histogram5 = []
magnitude_histogram6 = []
direction_histogram6 = []
magnitude_histogram7 = []
direction_histogram7 = []
magnitude_histogram8 = []
direction_histogram8 = []
magnitude_histogram9 = []
direction_histogram9 = []
magnitude_histogram10 = []
direction_histogram10 = []
magnitude_histogram11 = []
direction_histogram11 = []
magnitude_histogram12 = []
direction_histogram12 = []
magnitude_histogram13 = []
direction_histogram13 = []
magnitude_histogram14 = []
direction_histogram14 = []
magnitude_histogram15 = []
direction_histogram15 = []
magnitude_histogram16 = []
direction_histogram16 = []
# start reading the video
cap = cv2.VideoCapture(fm)
# take the first frame and convert it to gray
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# create the HSV color image
hsvImg = np.zeros_like(frame)
hsvImg[..., 1] = 255
# play until the user decides to stop
frame_no = 0
while True:
# save the previous frame data
previousGray = gray
# get the next frame
ret, frame = cap.read()
if ret:
# background-subtraction
fgmask = fgbg.apply(frame)
# median-blur
seg_mask = cv2.medianBlur(fgmask, 5)
# dilation
seg_mask = cv2.dilate(seg_mask, kernel, iterations=1)
#for drawing bounding box over the entire body
body = body_cascade.detectMultiScale(gray, 1.05, 3)
if (len(body) != 0):
for (x_t, y_t, w_t, h_t) in body:
x, y, w, h = x_t, y_t, w_t, h_t
# convert the frame to gray scale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# exception-handling
if ((x, y, w, h) == (0, 0, 0, 0)):
continue
# calculate the dense optical flow
flow = cv2.calcOpticalFlowFarneback(previousGray, gray, None, 0.5,
3, 15, 3, 5, 1.2, 0)
# obtain the flow magnitude and direction angle
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
mag = cv2.bitwise_and(mag, mag, mask=seg_mask)
ang = cv2.bitwise_and(ang, ang, mask=seg_mask)
# scaling
ang = ((ang * 180) / (np.pi / 2)) % 180
# find the intersection points to draw the 4x4 grid
k = 1
if (w % 4 == 0):
k = 0
c_x1 = x + (w // 4) + k
c_x2 = (x + 2 * (w // 4)) + k
c_x3 = (x + 3 * (w // 4))
k = 1
if (h % 4 == 0):
k = 0
c_y1 = y + (h // 4) + k
c_y2 = (y + 2 * (h // 4)) + k
c_y3 = (y + 3 * (h // 4))
flag1 = flag2 = flag3 = flag4 = 0
if (x - 5 >= 0):
x -= 5
flag1 = 1
if (x + w + 10 < ang.shape[1]):
w += 10
flag2 = 1
if (y - 5 >= 0):
y -= 5
flag3 = 1
if (y + h + 10 < ang.shape[0]):
h += 10
flag4 = 1
# extract the region-of-interests corresponding to the 4x4 grids
roi_mag1 = mag[y:c_y1, x:c_x1]
roi_mag2 = mag[y:c_y1, c_x1:c_x2]
roi_mag3 = mag[y:c_y1, c_x2:c_x3]
roi_mag4 = mag[y:c_y1, c_x3:x + w]
roi_mag5 = mag[c_y1:c_y2, x:c_x1]
roi_mag6 = mag[c_y1:c_y2, c_x1:c_x2]
roi_mag7 = mag[c_y1:c_y2, c_x2:c_x3]
roi_mag8 = mag[c_y1:c_y2, c_x3:x + w]
roi_mag9 = mag[c_y2:c_y3, x:c_x1]
roi_mag10 = mag[c_y2:c_y3, c_x1:c_x2]
roi_mag11 = mag[c_y2:c_y3, c_x2:c_x3]
roi_mag12 = mag[c_y2:c_y3, c_x3:x + w]
roi_mag13 = mag[c_y3:y + h, x:c_x1]
roi_mag14 = mag[c_y3:y + h, c_x1:c_x2]
roi_mag15 = mag[c_y3:y + h, c_x2:c_x3]
roi_mag16 = mag[c_y3:y + h, c_x3:x + w]
roi_dir1 = ang[y:c_y1, x:c_x1]
roi_dir2 = ang[y:c_y1, c_x1:c_x2]
roi_dir3 = ang[y:c_y1, c_x2:c_x3]
roi_dir4 = ang[y:c_y1, c_x3:x + w]
roi_dir5 = ang[c_y1:c_y2, x:c_x1]
roi_dir6 = ang[c_y1:c_y2, c_x1:c_x2]
roi_dir7 = ang[c_y1:c_y2, c_x2:c_x3]
roi_dir8 = ang[c_y1:c_y2, c_x3:x + w]
roi_dir9 = ang[c_y2:c_y3, x:c_x1]
roi_dir10 = ang[c_y2:c_y3, c_x1:c_x2]
roi_dir11 = ang[c_y2:c_y3, c_x2:c_x3]
roi_dir12 = ang[c_y2:c_y3, c_x3:x + w]
roi_dir13 = ang[c_y3:y + h, x:c_x1]
roi_dir14 = ang[c_y3:y + h, c_x1:c_x2]
roi_dir15 = ang[c_y3:y + h, c_x2:c_x3]
roi_dir16 = ang[c_y3:y + h, c_x3:x + w]
magnitude = np.array(mag).flatten()
direction = np.array(ang).flatten()
magnitude1 = np.array(roi_mag1).flatten()
direction1 = np.array(roi_dir1).flatten()
magnitude2 = np.array(roi_mag2).flatten()
direction2 = np.array(roi_dir2).flatten()
magnitude3 = np.array(roi_mag3).flatten()
direction3 = np.array(roi_dir3).flatten()
magnitude4 = np.array(roi_mag4).flatten()
direction4 = np.array(roi_dir4).flatten()
magnitude5 = np.array(roi_mag5).flatten()
direction5 = np.array(roi_dir5).flatten()
magnitude6 = np.array(roi_mag6).flatten()
direction6 = np.array(roi_dir6).flatten()
magnitude7 = np.array(roi_mag7).flatten()
direction7 = np.array(roi_dir7).flatten()
magnitude8 = np.array(roi_mag8).flatten()
direction8 = np.array(roi_dir8).flatten()
magnitude9 = np.array(roi_mag9).flatten()
direction9 = np.array(roi_dir9).flatten()
magnitude10 = np.array(roi_mag10).flatten()
direction10 = np.array(roi_dir10).flatten()
magnitude11 = np.array(roi_mag11).flatten()
direction11 = np.array(roi_dir11).flatten()
magnitude12 = np.array(roi_mag12).flatten()
direction12 = np.array(roi_dir12).flatten()
magnitude13 = np.array(roi_mag13).flatten()
direction13 = np.array(roi_dir13).flatten()
magnitude14 = np.array(roi_mag14).flatten()
direction14 = np.array(roi_dir14).flatten()
magnitude15 = np.array(roi_mag15).flatten()
direction15 = np.array(roi_dir15).flatten()
magnitude16 = np.array(roi_mag16).flatten()
direction16 = np.array(roi_dir16).flatten()
# create magnitude and direction optical flow histogram per frame for each grid
magnitude_histogram, direction_histogram = create_hist_context(
magnitude, direction, magnitude_histogram, direction_histogram)
magnitude_histogram1, direction_histogram1 = create_hist_context(
magnitude1, direction1, magnitude_histogram1,
direction_histogram1)
magnitude_histogram2, direction_histogram2 = create_hist_context(
magnitude2, direction2, magnitude_histogram2,
direction_histogram2)
magnitude_histogram3, direction_histogram3 = create_hist_context(
magnitude3, direction3, magnitude_histogram3,
direction_histogram3)
magnitude_histogram4, direction_histogram4 = create_hist_context(
magnitude4, direction4, magnitude_histogram4,
direction_histogram4)
magnitude_histogram5, direction_histogram5 = create_hist_context(
magnitude5, direction5, magnitude_histogram5,
direction_histogram5)
magnitude_histogram6, direction_histogram6 = create_hist_context(
magnitude6, direction6, magnitude_histogram6,
direction_histogram6)
magnitude_histogram7, direction_histogram7 = create_hist_context(
magnitude7, direction7, magnitude_histogram7,
direction_histogram7)
magnitude_histogram8, direction_histogram8 = create_hist_context(
magnitude8, direction8, magnitude_histogram8,
direction_histogram8)
magnitude_histogram9, direction_histogram9 = create_hist_context(
magnitude9, direction9, magnitude_histogram9,
direction_histogram9)
magnitude_histogram10, direction_histogram10 = create_hist_context(
magnitude10, direction10, magnitude_histogram10,
direction_histogram10)
magnitude_histogram11, direction_histogram11 = create_hist_context(
magnitude11, direction11, magnitude_histogram11,
direction_histogram11)
magnitude_histogram12, direction_histogram12 = create_hist_context(
magnitude12, direction12, magnitude_histogram12,
direction_histogram12)
magnitude_histogram13, direction_histogram13 = create_hist_context(
magnitude13, direction13, magnitude_histogram13,
direction_histogram13)
magnitude_histogram14, direction_histogram14 = create_hist_context(
magnitude14, direction14, magnitude_histogram14,
direction_histogram14)
magnitude_histogram15, direction_histogram15 = create_hist_context(
magnitude15, direction15, magnitude_histogram15,
direction_histogram15)
magnitude_histogram16, direction_histogram16 = create_hist_context(
magnitude16, direction16, magnitude_histogram16,
direction_histogram16)
#---------------------------------------------------------#
# if you wish to see the optical flow frames uncomment the next 3 paragraphs
'''
# update the color image
hsvImg[..., 0] = 0.5 * ang * 180 / np.pi
hsvImg[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
rgbImg = cv2.cvtColor(hsvImg, cv2.COLOR_HSV2BGR)
#drawing the bounding box
cv2.rectangle(rgbImg, (x,y), (c_x1,c_y1), (255,0,0), 2)
cv2.rectangle(rgbImg, (c_x1,y), (c_x2,c_y1), (0,255,0), 2)
cv2.rectangle(rgbImg, (c_x2,y), (c_x3,c_y1), (0,0,255), 2)
cv2.rectangle(rgbImg, (c_x3,y), (x+w,c_y1), (167,0,255), 2)
cv2.rectangle(rgbImg, (x,c_y1), (c_x1,c_y2), (128,0,0), 2)
cv2.rectangle(rgbImg, (c_x1, c_y1), (c_x2,c_y2), (0,128,0), 2)
cv2.rectangle(rgbImg, (c_x2, c_y1), (c_x3,c_y2), (0,0,128), 2)
cv2.rectangle(rgbImg, (c_x3, c_y1), (x+w,c_y2), (0,128,255), 2)
cv2.rectangle(rgbImg, (x,c_y2), (c_x1,c_y3), (255,128,0), 2)
cv2.rectangle(rgbImg, (c_x1, c_y2), (c_x2,c_y3), (0,255,128), 2)
cv2.rectangle(rgbImg, (c_x2, c_y2), (c_x3,c_y3), (128,0,255), 2)
cv2.rectangle(rgbImg, (c_x3, c_y2), (x+w,c_y3), (0,100,255), 2)
cv2.rectangle(rgbImg, (x,c_y3), (c_x1,y+h), (255,0,128), 2)
cv2.rectangle(rgbImg, (c_x1, c_y3), (c_x2,y+h), (128,255,128), 2)
cv2.rectangle(rgbImg, (c_x2, c_y3), (c_x3,y+h), (128,128,255), 2)
cv2.rectangle(rgbImg, (c_x3, c_y3), (x+w,y+h), (167,167,255), 2)
#Display the resulting frame
cv2.imshow('dense optical flow', np.hstack((frame, rgbImg)))
'''
#---------------------------------------------------------#
# adjusting the bounding box over the POI to facilitate outward motion of the human body
frame_no += 1
if (flag1 == 1):
x += 5
if (flag2 == 1):
w -= 10
if (flag3 == 1):
y += 5
if (flag4 == 1):
h -= 10
k = cv2.waitKey(30) & 0xff
if k == 27:
break
else:
break
# check the magnitude and direction histograms to have expected shapes
magnitude_histogram = mag_check(magnitude_histogram)
magnitude_histogram1 = mag_check(magnitude_histogram1)
magnitude_histogram2 = mag_check(magnitude_histogram2)
magnitude_histogram3 = mag_check(magnitude_histogram3)
magnitude_histogram4 = mag_check(magnitude_histogram4)
magnitude_histogram5 = mag_check(magnitude_histogram5)
magnitude_histogram6 = mag_check(magnitude_histogram6)
magnitude_histogram7 = mag_check(magnitude_histogram7)
magnitude_histogram8 = mag_check(magnitude_histogram8)
magnitude_histogram9 = mag_check(magnitude_histogram9)
magnitude_histogram10 = mag_check(magnitude_histogram10)
magnitude_histogram11 = mag_check(magnitude_histogram11)
magnitude_histogram12 = mag_check(magnitude_histogram12)
magnitude_histogram13 = mag_check(magnitude_histogram13)
magnitude_histogram14 = mag_check(magnitude_histogram14)
magnitude_histogram15 = mag_check(magnitude_histogram15)
magnitude_histogram16 = mag_check(magnitude_histogram16)
direction_histogram = dir_check(direction_histogram)
direction_histogram1 = dir_check(direction_histogram1)
direction_histogram2 = dir_check(direction_histogram2)
direction_histogram3 = dir_check(direction_histogram3)
direction_histogram4 = dir_check(direction_histogram4)
direction_histogram5 = dir_check(direction_histogram5)
direction_histogram6 = dir_check(direction_histogram6)
direction_histogram7 = dir_check(direction_histogram7)
direction_histogram8 = dir_check(direction_histogram8)
direction_histogram9 = dir_check(direction_histogram9)
direction_histogram10 = dir_check(direction_histogram10)
direction_histogram11 = dir_check(direction_histogram11)
direction_histogram12 = dir_check(direction_histogram12)
direction_histogram13 = dir_check(direction_histogram13)
direction_histogram14 = dir_check(direction_histogram14)
direction_histogram15 = dir_check(direction_histogram15)
direction_histogram16 = dir_check(direction_histogram16)
# apply windowing to extract contextual information
mag_hist = window(magnitude_histogram)
dir_hist = window(direction_histogram)
mag_hist1 = window(magnitude_histogram1)
dir_hist1 = window(direction_histogram1)
mag_hist2 = window(magnitude_histogram2)
dir_hist2 = window(direction_histogram2)
mag_hist3 = window(magnitude_histogram3)
dir_hist3 = window(direction_histogram3)
mag_hist4 = window(magnitude_histogram4)
dir_hist4 = window(direction_histogram4)
mag_hist5 = window(magnitude_histogram5)
dir_hist5 = window(direction_histogram5)
mag_hist6 = window(magnitude_histogram6)
dir_hist6 = window(direction_histogram6)
mag_hist7 = window(magnitude_histogram7)
dir_hist7 = window(direction_histogram7)
mag_hist8 = window(magnitude_histogram8)
dir_hist8 = window(direction_histogram8)
mag_hist9 = window(magnitude_histogram9)
dir_hist9 = window(direction_histogram9)
mag_hist10 = window(magnitude_histogram10)
dir_hist10 = window(direction_histogram10)
mag_hist11 = window(magnitude_histogram11)
dir_hist11 = window(direction_histogram11)
mag_hist12 = window(magnitude_histogram12)
dir_hist12 = window(direction_histogram12)
mag_hist13 = window(magnitude_histogram13)
dir_hist13 = window(direction_histogram13)
mag_hist14 = window(magnitude_histogram14)
dir_hist14 = window(direction_histogram14)
mag_hist15 = window(magnitude_histogram15)
dir_hist15 = window(direction_histogram15)
mag_hist16 = window(magnitude_histogram16)
dir_hist16 = window(direction_histogram16)
# calculate the mean of the magnitude and direction histograms for each 4x4 grids
mag_avg_hist = np.mean(mag_hist, axis=0)
dir_avg_hist = np.mean(dir_hist, axis=0)
mag_avg_hist1 = np.mean(mag_hist1, axis=0)
dir_avg_hist1 = np.mean(dir_hist1, axis=0)
mag_avg_hist2 = np.mean(mag_hist2, axis=0)
dir_avg_hist2 = np.mean(dir_hist2, axis=0)
mag_avg_hist3 = np.mean(mag_hist3, axis=0)
dir_avg_hist3 = np.mean(dir_hist3, axis=0)
mag_avg_hist4 = np.mean(mag_hist4, axis=0)
dir_avg_hist4 = np.mean(dir_hist4, axis=0)
mag_avg_hist5 = np.mean(mag_hist5, axis=0)
dir_avg_hist5 = np.mean(dir_hist5, axis=0)
mag_avg_hist6 = np.mean(mag_hist6, axis=0)
dir_avg_hist6 = np.mean(dir_hist6, axis=0)
mag_avg_hist7 = np.mean(mag_hist7, axis=0)
dir_avg_hist7 = np.mean(dir_hist7, axis=0)
mag_avg_hist8 = np.mean(mag_hist8, axis=0)
dir_avg_hist8 = np.mean(dir_hist8, axis=0)
mag_avg_hist9 = np.mean(mag_hist9, axis=0)
dir_avg_hist9 = np.mean(dir_hist9, axis=0)
mag_avg_hist10 = np.mean(mag_hist10, axis=0)
dir_avg_hist10 = np.mean(dir_hist10, axis=0)
mag_avg_hist11 = np.mean(mag_hist11, axis=0)
dir_avg_hist11 = np.mean(dir_hist11, axis=0)
mag_avg_hist12 = np.mean(mag_hist12, axis=0)
dir_avg_hist12 = np.mean(dir_hist12, axis=0)
mag_avg_hist13 = np.mean(mag_hist13, axis=0)
dir_avg_hist13 = np.mean(dir_hist13, axis=0)
mag_avg_hist14 = np.mean(mag_hist14, axis=0)
dir_avg_hist14 = np.mean(dir_hist14, axis=0)
mag_avg_hist15 = np.mean(mag_hist15, axis=0)
dir_avg_hist15 = np.mean(dir_hist15, axis=0)
mag_avg_hist16 = np.mean(mag_hist16, axis=0)
dir_avg_hist16 = np.mean(dir_hist16, axis=0)
# calculate the standard deviation of the magnitude and direction histograms for each 4x4 grids
mag_std_hist = np.std(mag_hist, axis=0)
dir_std_hist = np.std(dir_hist, axis=0)
mag_std_hist1 = np.std(mag_hist1, axis=0)
dir_std_hist1 = np.std(dir_hist1, axis=0)
mag_std_hist2 = np.std(mag_hist2, axis=0)
dir_std_hist2 = np.std(dir_hist2, axis=0)
mag_std_hist3 = np.std(mag_hist3, axis=0)
dir_std_hist3 = np.std(dir_hist3, axis=0)
mag_std_hist4 = np.std(mag_hist4, axis=0)
dir_std_hist4 = np.std(dir_hist4, axis=0)
mag_std_hist5 = np.std(mag_hist5, axis=0)
dir_std_hist5 = np.std(dir_hist5, axis=0)
mag_std_hist6 = np.std(mag_hist6, axis=0)
dir_std_hist6 = np.std(dir_hist6, axis=0)
mag_std_hist7 = np.std(mag_hist7, axis=0)
dir_std_hist7 = np.std(dir_hist7, axis=0)
mag_std_hist8 = np.std(mag_hist8, axis=0)
dir_std_hist8 = np.std(dir_hist8, axis=0)
mag_std_hist9 = np.std(mag_hist9, axis=0)
dir_std_hist9 = np.std(dir_hist9, axis=0)
mag_std_hist10 = np.std(mag_hist10, axis=0)
dir_std_hist10 = np.std(dir_hist10, axis=0)
mag_std_hist11 = np.std(mag_hist11, axis=0)
dir_std_hist11 = np.std(dir_hist11, axis=0)
mag_std_hist12 = np.std(mag_hist12, axis=0)
dir_std_hist12 = np.std(dir_hist12, axis=0)
mag_std_hist13 = np.std(mag_hist13, axis=0)
dir_std_hist13 = np.std(dir_hist13, axis=0)
mag_std_hist14 = np.std(mag_hist14, axis=0)
dir_std_hist14 = np.std(dir_hist14, axis=0)
mag_std_hist15 = np.std(mag_hist15, axis=0)
dir_std_hist15 = np.std(dir_hist15, axis=0)
mag_std_hist16 = np.std(mag_hist16, axis=0)
dir_std_hist16 = np.std(dir_hist16, axis=0)
# concatenate all the histogram features to get the contextual descriptor for 4x4 grids
histogram = mag_avg_hist
histogram = np.hstack((histogram, mag_std_hist))
histogram = np.hstack((histogram, dir_avg_hist))
histogram = np.hstack((histogram, dir_std_hist))
histogram = np.hstack((histogram, mag_avg_hist1))
histogram = np.hstack((histogram, mag_std_hist1))
histogram = np.hstack((histogram, dir_avg_hist1))
histogram = np.hstack((histogram, dir_std_hist1))
histogram = np.hstack((histogram, mag_avg_hist2))
histogram = np.hstack((histogram, mag_std_hist2))
histogram = np.hstack((histogram, dir_avg_hist2))
histogram = np.hstack((histogram, dir_std_hist2))
histogram = np.hstack((histogram, mag_avg_hist3))
histogram = np.hstack((histogram, mag_std_hist3))
histogram = np.hstack((histogram, dir_avg_hist3))
histogram = np.hstack((histogram, dir_std_hist3))
histogram = np.hstack((histogram, mag_avg_hist4))
histogram = np.hstack((histogram, mag_std_hist4))
histogram = np.hstack((histogram, dir_avg_hist4))
histogram = np.hstack((histogram, dir_std_hist4))
histogram = np.hstack((histogram, mag_avg_hist5))
histogram = np.hstack((histogram, mag_std_hist5))
histogram = np.hstack((histogram, dir_avg_hist5))
histogram = np.hstack((histogram, dir_std_hist5))
histogram = np.hstack((histogram, mag_avg_hist6))
histogram = np.hstack((histogram, mag_std_hist6))
histogram = np.hstack((histogram, dir_avg_hist6))
histogram = np.hstack((histogram, dir_std_hist6))
histogram = np.hstack((histogram, mag_avg_hist7))
histogram = np.hstack((histogram, mag_std_hist7))
histogram = np.hstack((histogram, dir_avg_hist7))
histogram = np.hstack((histogram, dir_std_hist7))
histogram = np.hstack((histogram, mag_avg_hist8))
histogram = np.hstack((histogram, mag_std_hist8))
histogram = np.hstack((histogram, dir_avg_hist8))
histogram = np.hstack((histogram, dir_std_hist8))
histogram = np.hstack((histogram, mag_avg_hist9))
histogram = np.hstack((histogram, mag_std_hist9))
histogram = np.hstack((histogram, dir_avg_hist9))
histogram = np.hstack((histogram, dir_std_hist9))
histogram = np.hstack((histogram, mag_avg_hist10))
histogram = np.hstack((histogram, mag_std_hist10))
histogram = np.hstack((histogram, dir_avg_hist10))
histogram = np.hstack((histogram, dir_std_hist10))
histogram = np.hstack((histogram, mag_avg_hist11))
histogram = np.hstack((histogram, mag_std_hist11))
histogram = np.hstack((histogram, dir_avg_hist11))
histogram = np.hstack((histogram, dir_std_hist11))
histogram = np.hstack((histogram, mag_avg_hist12))
histogram = np.hstack((histogram, mag_std_hist12))
histogram = np.hstack((histogram, dir_avg_hist12))
histogram = np.hstack((histogram, dir_std_hist12))
histogram = np.hstack((histogram, mag_avg_hist13))
histogram = np.hstack((histogram, mag_std_hist13))
histogram = np.hstack((histogram, dir_avg_hist13))
histogram = np.hstack((histogram, dir_std_hist13))
histogram = np.hstack((histogram, mag_avg_hist14))
histogram = np.hstack((histogram, mag_std_hist14))
histogram = np.hstack((histogram, dir_avg_hist14))
histogram = np.hstack((histogram, dir_std_hist14))
histogram = np.hstack((histogram, mag_avg_hist15))
histogram = np.hstack((histogram, mag_std_hist15))
histogram = np.hstack((histogram, dir_avg_hist15))
histogram = np.hstack((histogram, dir_std_hist15))
histogram = np.hstack((histogram, mag_avg_hist16))
histogram = np.hstack((histogram, mag_std_hist16))
histogram = np.hstack((histogram, dir_avg_hist16))
histogram = np.hstack((histogram, dir_std_hist16))
cv2.destroyAllWindows()
cap.release()
return histogram, frame_no
# 1. Contstruct the path to the text file in the data directory using the `pathlib` module [2P]
cars_dir = Path(data_dir, "cars.txt")
# 2. Read the text file [2P]
cars = open(str(cars_dir), "r").read()
# 3. Count the occurences of each item in the text file [2P]
clist = list(cars.split("\n"))
occur = ut.countelem(clist)
# 4. Using `pathlib` check if a directory with name `solution` exists and if not create it [2P]
ut.dir_check(output_dir)
# 5. Write the counts to the file `counts.csv` in the `solution` directory in the format (first line is the header): [2P]
# item, count
# item_name_1, item_count_1
# item_name_2, item_count_2
# ...
counts = output_dir / "counts.csv"
with open(counts, "w") as file:
writer = csv.DictWriter(file, fieldnames=["item", "count"])
writer.writeheader()
for key in occur.keys():
file.write("%s,%s\n" % (key, occur[key]))