def draw_result(fp_label,
fp_res,
fp_massmap,
fp_posres,
setups,
masses_gt=[1, 1, 1, 1],
iters=[
-1,
0,
1,
2,
]):
cmap = matplotlib.cm.coolwarm
#cnorm = matplotlib.colors.Normalize(vmin=0.1,vmax=4.9)
obj = os.path.split(fp_label)[1].split('_')[0]
sim = SimBullet(gui=False)
#sim._p.setGravity(0,0,-9.8)
sim.addObject('table_rect', 'table_rect', [1, 1, 1, 0])
sim.setupRendering(0.75, -90, -60, 500, 500)
img_label = cv2.imread(fp_label, cv2.IMREAD_UNCHANGED)
labels = np.unique(img_label)
cells = CellPhysicsBase.createCells(img_label, masses_gt, 0.53 / 640.0)
sim.addObject(obj, cells, [0, 0, 1, 1])
sim.setObjectPose(obj, 0, 0, 0)
cellinfos = sim.getCellBodyProperties(obj)
vmax = max([info['mass'] for info in cellinfos])
vmin = min([info['mass'] for info in cellinfos])
cnorm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)
for info in cellinfos:
info['color'] = cmap(cnorm(info['mass']))
sim.setCellBodyProperties(obj, cellinfos)
img = sim.draw()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(fp_massmap % -3, img)
f = open(fp_res, 'rb')
res = pickle.load(f)
f.close()
# estimation example
cellinfos = res['history'][-1]['cellinfos']
#cnorm = matplotlib.colors.Normalize(vmin=0.1,vmax=5)
for info in cellinfos[obj]:
info['color'] = cmap(cnorm((vmax - vmin) * 0.5))
sim.setCellBodyProperties(obj, cellinfos[obj])
img = sim.draw()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(fp_massmap % -2, img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for it in iters:
if it >= len(res['history']):
continue
cellinfos = res['history'][it]['cellinfos']
#vmax = max([info['mass'] for info in cellinfos[obj]])
#vmin = min([info['mass'] for info in cellinfos[obj]])
#cnorm = matplotlib.colors.Normalize(vmin=vmin,vmax=vmax)
for info in cellinfos[obj]:
info['color'] = cmap(cnorm(info['mass']))
sim.setCellBodyProperties(obj, cellinfos[obj])
img = sim.draw()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(fp_massmap % it, img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return
# debug image
sim.setupRendering(0.75, 90, -90, 500, 500)
sim.addObject('finger1', 'finger', [1, 1, 0.9, 1])
sim.addObject('finger2', 'finger', [1, 1, 0.6, 1])
sim.addObject('finger3', 'finger', [1, 1, 0.3, 1])
sim.addObject('init', cells, [0.7, 0.7, 0.7, 1])
pos, rot = sim.getPosRot(sim.objects['init']['shape'], 0, 0, 0)
pos[2] = pos[2] - 0.002
sim._p.resetBasePositionAndOrientation(sim.objects['init']['bodyId'], pos,
rot)
sim.addObject('gt', cells, [0, 1, 0, 1])
for i in range(len(res['history'][-1]['test_poses'])):
if 'real' not in setups:
pixel2meter(cells, setups['test'][i])
sim.resetObject('finger1',setups['test'][i]['finger']['pos'][0] - 0.03,\
setups['test'][i]['finger']['pos'][1],0)
sim.resetObject('finger2',setups['test'][i]['finger']['pos'][0] + setups['test'][i]['finger']['velocity'][0]*0.40 - 0.03,\
setups['test'][i]['finger']['pos'][1] + setups['test'][i]['finger']['velocity'][1]*0.40,0)
sim.resetObject('finger3',setups['test'][i]['finger']['pos'][0] + setups['test'][i]['finger']['velocity'][0]*0.90 - 0.03,\
setups['test'][i]['finger']['pos'][1] + setups['test'][i]['finger']['velocity'][1]*0.90,0)
else:
sim.resetObject('finger1',setups['real'][i]['finger']['pos'][0] - 0.03,\
setups['real'][i]['finger']['pos'][1],0)
sim.resetObject('finger2',setups['real'][i]['finger']['pos'][0] + setups['real'][i]['finger']['velocity'][0]*0.40 - 0.03,\
setups['real'][i]['finger']['pos'][1] + setups['real'][i]['finger']['velocity'][1]*0.40,0)
sim.resetObject('finger3',setups['real'][i]['finger']['pos'][0] + setups['real'][i]['finger']['velocity'][0]*0.90 - 0.03,\
setups['real'][i]['finger']['pos'][1] + setups['real'][i]['finger']['velocity'][1]*0.90,0)
pose_gt = res['history'][-1]['test_poses'][i]['gt']
#sim.resetObject(obj,100,100,0)
sim.resetObject('gt', pose_gt[obj]['x'], pose_gt[obj]['y'],
pose_gt[obj]['yaw'])
#img = sim.draw()
#img = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)
#cv2.imwrite(fp_posres % (i+20),img)
#img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
#sim.resetObject('gt',100,100,0)
pose_est = res['history'][-1]['test_poses'][i]['estimation']
sim.resetObject(obj, pose_est[obj]['x'], pose_est[obj]['y'],
pose_est[obj]['yaw'])
pos, rot = sim.getPosRot(sim.objects['gt']['shape'], pose_gt[obj]['x'],
pose_gt[obj]['y'], pose_gt[obj]['yaw'])
pos[0] = pos[0] - 0.001
pos[2] = pos[2] - 0.001
sim._p.resetBasePositionAndOrientation(sim.objects['gt']['bodyId'],
pos, rot)
img = sim.draw()
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imwrite(fp_posres % i, img)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if key == 'pos':
pos_push = [0, 0]
pos_push[
0] = np.cos(yaw0) * value[0] - np.sin(yaw0) * value[1] + x0
pos_push[
1] = np.sin(yaw0) * value[0] + np.cos(yaw0) * value[1] + y0
elif key == 'velocity':
vec_push = [0, 0]
vec_push[0] = np.cos(yaw0) * value[0] - np.sin(yaw0) * value[1]
vec_push[1] = np.sin(yaw0) * value[0] + np.cos(yaw0) * value[1]
elif key == 'duration':
duration = value
vec_push[0] = vec_push[0] * duration
vec_push[1] = vec_push[1] * duration
pos_begin = Pose2D(x0, y0, yaw0)
pos_final = Pose2D(x1, y1, yaw1)
pos_push = Pose2D(pos_push[0], pos_push[1], 0)
vec_push = Pose2D(vec_push[0], vec_push[1], 0)
planner.addObservation(pos_begin, pos_final, pos_push, vec_push,
duration)
cellinfos = planner.getProperties()
cellinfos_sim = sim.getCellBodyProperties(obj)
cnorm = matplotlib.colors.Normalize(vmin=0.1, vmax=5)
for info, info_sim in zip(cellinfos, cellinfos_sim):
info_sim['color'] = matplotlib.cm.coolwarm(cnorm(info['mass']))
sim.setCellBodyProperties(obj, cellinfos_sim)
x0, y0, yaw0 = (x1, y1, yaw1)