def getRigidBodies(label, working_dir):
sseq = scipy.io.mmread(path + '/../' + working_dir + '/shape_' + label +
'.mtx')
nbp, R, r, Rc, rc, Rw, rw = frames(sseq)
color, colorC, RB, RBC = {}, {}, {}, {}
for i in range(int(nbp)):
rrw, rrc, RRw, RRc = rw[i], rc[i], Rw[i], Rc[i]
fuf = open(path + '/../' + working_dir + '/complete_' + label + '.txt',
'r')
sus = fuf.read()
fuf.close()
base = sus[i]
if base == 'A':
baseC = 'T'
if base == 'T':
baseC = 'A'
if base == 'C':
baseC = 'G'
if base == 'G':
baseC = 'C'
#### The variable with C is complementary strand,c i.e. color and colorC ####
color[i], RB[i] = getRigidBodyConfig(
rrw, RRw, base) # call the above function for both the watson
colorC[i], RBC[i] = getRigidBodyConfig(rrc, RRc, baseC,
'C') # and crick strands
return nbp, RBC, colorC, RB, color
def main():
fullbody_cascade = cv2.CascadeClassifier(
'/Users/Aaron/Documents/College/Fourth_Year/Final_Year_Project/Datasets/body10/haarcascade_fullbody.xml'
)
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a",
"--min-area",
type=int,
default=500,
help="minimum area size")
args = vars(ap.parse_args())
if args.get("video", None) is None:
camera = cv2.VideoCapture(0)
# otherwise, we are reading from a video file
else:
print("[INFO] starting video file thread...")
camera = myqueue(args["video"]).start()
time.sleep(1.0)
fps = frames().start()
while camera.more():
frame = camera.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.putText(frame, "Queue Size: {}".format(camera.Q.qsize()), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
fullbody = fullbody_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in fullbody:
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = frame[y:y + h, x:x + w]
peds_found = "Found " + str(len(fullbody)) + " Pedestrians"
cv2.putText(frame, peds_found, (10, 55), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
(0, 255, 0), 2)
cv2.imshow("HaarCascade", frame)
cv2.waitKey(1)
fps.update()
k = cv2.waitKey(27) & 0xff
if k == 27:
break
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
cv2.destroyAllWindows()
camera.stop()
def cgDNA_MonteCarlo(*arg):
#####Sample input: cgDNA_MonteCarlo(seq_label(s),nbr_samples,working_dir)
n_arg = len(arg)
nbr_samples = int(arg[n_arg - 2])
l_p, l_d = {}, {}
for label in range(n_arg - 2):
fuf = open(
path + '/../' + arg[n_arg - 1] + '/complete_' + arg[label] +
'.txt', 'r')
sus = fuf.read()
fuf.close()
shape = scipy.io.mmread(path + '/../' + arg[n_arg - 1] + '/shape_' +
arg[label] + '.mtx')
stiff = scipy.io.mmread(path + '/../' + arg[n_arg - 1] + '/stiff_' +
arg[label] + '.mtx')
nbp = len(sus.strip())
dim = 12 * nbp - 6
MC = np.random.multivariate_normal(np.zeros(dim), np.eye(dim,
dtype=int),
nbr_samples)
ttc = np.zeros((nbp, 1))
L = scipy.linalg.cholesky(stiff.todense())
#### if only one seq is provided, it will plot the different MC configurations####
if n_arg == 3:
fig = plt.figure()
ax = plt.axes(projection='3d')
for jj in range(nbr_samples):
x = np.linalg.solve(L, np.transpose(MC[jj])) + shape
nbp, R, r, Rc, rc, Rw, rw = frames(x)
XYZ = np.zeros((nbp, 3))
for k in range(nbp):
XYZ[k] = r[k]
ttc[k][0] = np.divide(R[k][2][2], nbr_samples) + ttc[k][0]
if n_arg == 3:
XYZ = np.transpose(XYZ)
ax.plot3D(XYZ[0], XYZ[1], XYZ[2], 'blue')
XYZ = np.transpose(XYZ)
nbp, R, r, Rc, rc, Rw, rw = frames(shape)
ttc_int = np.zeros((nbp, 1))
for k in range(nbp):
XYZ[k] = r[k]
ttc_int[k][0] = R[k][2][2]
if n_arg == 3:
XYZ = np.transpose(XYZ)
ax.plot3D(XYZ[0], XYZ[1], XYZ[2], 'red')
l_p[label], l_d[label], not_imp = Compute_pl(nbp, ttc, ttc_int)
print("For ", arg[label], " l_p = ", l_p[label], " and l_d = ",
l_d[label])
if n_arg == 3:
plt.show()
plt.savefig(path + 'Mon.png', format='png', dpi=1200)
pred.append((int(tp[0]), int(tp[1])))
def paint(tp, xA, yA, xB, yB):
global frame, pred
cv2.circle(frame, ((tp[0]), (tp[1])), 3, (0, 0, 255), -1)
cv2.rectangle(frame, ((tp[0]) - ((xB - xA) / 2), (tp[1]) + (yB - yA) / 2),
(((tp[0]) + ((xB - xA) / 2)), ((tp[1]) - (yB - yA) / 2)),
(0, 0, 255), 2)
# created a *threaded* video stream, allow the camera sensor to warmup,
# and start the FPS counter
print("[INFO] sampling THREADED frames from webcam...")
vs = webcamThread(src=0).start()
fps = frames().start()
kalman = cv2.KalmanFilter(4, 2)
kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
kalman.transitionMatrix = np.array(
[[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
kalman.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0],
[0, 0, 0, 1]], np.float32) * 0.03
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
# loop over some frames...this time using the threaded stream
while (True):
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 600 pixels
def makepdb(*arg):
n_arg = len(arg)
for ii in range(n_arg - 1):
fuf = open(path + '/../' + arg[n_arg - 1] + '/complete_' + arg[ii] + '.txt', 'r')
sus = fuf.read()
fuf.close()
out = open(arg[ii] + '.pdb', 'w')
shape = scipy.io.mmread(path + '/../' + arg[n_arg - 1] + '/shape_' + arg[ii] + '.mtx')
nbp, R, r, Rc, rc, Rw, rw = frames(shape)
ideal = np.loadtxt(path + '/../functions/' + 'ideal_bases.txt',
skiprows=1,
delimiter=',',
usecols = (1,2,3))
atoms = np.loadtxt(path + '/../functions/' + 'ideal_bases.txt',
skiprows=1,
delimiter=',',
usecols=(0),
dtype=str)
kk=0
for i in range(nbp):
base = sus[i]
cord = {}
if base == 'A':
for j in range(11):
kk = kk + 1
cord[j] = rw[i] + np.matmul(Rw[i],np.transpose(ideal[j]))
cord[j] = rw[i] + np.matmul(Rw[i],ideal[j])
out.write("ATOM "+str(kk)+" "+str(atoms[j])+" "+base+" "+str(i+1)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'T':
for j in range(10):
kk = kk+1
cord[j] = rw[i] + np.matmul(Rw[i],np.transpose(ideal[j+11]))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11])+" "+base+" "+str(i+1)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'G':
for j in range(12):
kk = kk+1
cord[j] = rw[i] + np.matmul(Rw[i],np.transpose(ideal[j+11+10]))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11+10])+" "+base+" "+str(i+1)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'C':
for j in range(9):
kk = kk+1
cord[j] = rw[i] + np.matmul(Rw[i],np.transpose(ideal[j+11+10+12]))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11+10+12])+" "+base+" "+str(i+1)+" "+' '.join(map(str, cord[j]))+"\n")
out.write('TER \n')
F = [[1,0,0],[0,-1,0],[0,0,-1]]
for i in range(nbp-1,-1,-1):
base=comp(sus[i])
cord = {}
if base == 'A':
for j in range(11):
kk = kk+1
cord[j] = rc[i] + np.matmul(Rc[i],np.matmul(F,np.transpose(ideal[j])))
out.write("ATOM "+str(kk)+" "+str(atoms[j])+" "+base+" "+str(2*nbp-i)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'T':
for j in range(10):
kk = kk+1
cord[j] = rc[i] + np.matmul(Rc[i],np.matmul(F,np.transpose(ideal[j+11])))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11])+" "+base+" "+str(2*nbp-i)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'G':
for j in range(12):
kk = kk+1
cord[j] = rc[i] + np.matmul(Rc[i],np.matmul(F,np.transpose(ideal[j+11+10])))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11+10])+" "+base+" "+str(2*nbp-i)+" "+' '.join(map(str, cord[j]))+"\n")
if base == 'C':
for j in range(9):
kk = kk+1
cord[j] = rc[i] + np.matmul(Rc[i],np.matmul(F,np.transpose(ideal[j+11+10+12])))
out.write("ATOM "+str(kk)+" "+str(atoms[j+11+10+12])+" "+base+" "+str(2*nbp-i)+" "+' '.join(map(str, cord[j]))+"\n")
out.close()
return