Python src.file_utils.makedirs() Examples
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code examples of src.file_utils.makedirs().
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Example #1
Source File: script_nav_agent_release.py From models with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #2
Source File: script_nav_agent_release.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #3
Source File: utils.py From models with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #4
Source File: utils.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #5
Source File: script_nav_agent_release.py From object_detection_with_tensorflow with MIT License | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #6
Source File: utils.py From object_detection_with_tensorflow with MIT License | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #7
Source File: script_nav_agent_release.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #8
Source File: utils.py From DOTA_models with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #9
Source File: utils.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #10
Source File: script_nav_agent_release.py From hands-detection with MIT License | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #11
Source File: utils.py From hands-detection with MIT License | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #12
Source File: script_nav_agent_release.py From Gun-Detector with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #13
Source File: utils.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #14
Source File: utils.py From Gun-Detector with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #15
Source File: script_nav_agent_release.py From yolo_v2 with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #16
Source File: utils.py From yolo_v2 with Apache License 2.0 | 5 votes |
def mkdir_if_missing(output_dir): if not fu.exists(output_dir): fu.makedirs(output_dir)
Example #17
Source File: script_nav_agent_release.py From DOTA_models with Apache License 2.0 | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #18
Source File: script_nav_agent_release.py From multilabel-image-classification-tensorflow with MIT License | 5 votes |
def _launcher(config_name, logdir): args = _setup_args(config_name, logdir) fu.makedirs(args.logdir) if args.control.train: _train(args) if args.control.test: _test(args)
Example #19
Source File: script_plot_trajectory.py From object_detection_kitti with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print file_name with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #20
Source File: script_plot_trajectory.py From models with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print(file_name) with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #21
Source File: script_plot_trajectory.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print(file_name) with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #22
Source File: script_plot_trajectory.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print(file_name) with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #23
Source File: script_plot_trajectory.py From object_detection_with_tensorflow with MIT License | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print file_name with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #24
Source File: script_plot_trajectory.py From hands-detection with MIT License | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print file_name with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #25
Source File: script_plot_trajectory.py From Gun-Detector with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print(file_name) with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #26
Source File: script_plot_trajectory.py From yolo_v2 with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print file_name with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)
Example #27
Source File: script_plot_trajectory.py From DOTA_models with Apache License 2.0 | 4 votes |
def plot_trajectory(dt, hardness, orig_maps, out_dir): out_dir = os.path.join(out_dir, FLAGS.config_name+_get_suffix_str(), FLAGS.imset) fu.makedirs(out_dir) out_file = os.path.join(out_dir, 'all_locs_at_t.pkl') dt['hardness'] = hardness utils.save_variables(out_file, dt.values(), dt.keys(), overwrite=True) #Plot trajectories onto the maps plt.set_cmap('gray') for i in range(4000): goal_loc = dt['all_goal_locs'][i, :, :] locs = np.concatenate((dt['all_locs'][i,:,:], dt['all_locs'][i,:,:]), axis=0) xymin = np.minimum(np.min(goal_loc, axis=0), np.min(locs, axis=0)) xymax = np.maximum(np.max(goal_loc, axis=0), np.max(locs, axis=0)) xy1 = (xymax+xymin)/2. - 1.*np.maximum(np.max(xymax-xymin), 24) xy2 = (xymax+xymin)/2. + 1.*np.maximum(np.max(xymax-xymin), 24) fig, ax = utils.tight_imshow_figure(plt, figsize=(6,6)) ax.set_axis_on() ax.patch.set_facecolor((0.333, 0.333, 0.333)) ax.set_xticks([]) ax.set_yticks([]) all_locs = dt['all_locs'][i,:,:]*1 uniq = np.where(np.any(all_locs[1:,:] != all_locs[:-1,:], axis=1))[0]+1 uniq = np.sort(uniq).tolist() uniq.insert(0,0) uniq = np.array(uniq) all_locs = all_locs[uniq, :] ax.plot(dt['all_locs'][i, 0, 0], dt['all_locs'][i, 0, 1], 'b.', markersize=24) ax.plot(dt['all_goal_locs'][i, 0, 0], dt['all_goal_locs'][i, 0, 1], 'g*', markersize=19) ax.plot(all_locs[:,0], all_locs[:,1], 'r', alpha=0.4, linewidth=2) ax.scatter(all_locs[:,0], all_locs[:,1], c=5+np.arange(all_locs.shape[0])*1./all_locs.shape[0], cmap='Reds', s=30, linewidth=0) ax.imshow(orig_maps, origin='lower', vmin=-1.0, vmax=2.0, aspect='equal') ax.set_xlim([xy1[0], xy2[0]]) ax.set_ylim([xy1[1], xy2[1]]) file_name = os.path.join(out_dir, 'trajectory_{:04d}.png'.format(i)) print file_name with fu.fopen(file_name, 'w') as f: plt.savefig(f) plt.close(fig)