Disparity calculation (#12)

* add filter for z < 40

* change figure name

* stereo results in figure

* stereo figure

* pylint

* pylint(2)

* pylint updated

monoloco/eval/eval_kitti.py

@@ -113,7 +113,7 @@ def run(self):
 
     def printer(self, show, save):
         if save or show:
-            show_results(self.dic_stats, show, save)
+            show_results(self.dic_stats, show, save, stereo=self.stereo)
             show_spread(self.dic_stats, show, save)
             show_task_error(show, save)
 
@@ -178,7 +178,7 @@ def _estimate_error(self, out_gt, out, method):
                 self.update_uncertainty(stds_ale[idx], stds_epi[idx], dds[idx], dds_gt[idx_gt], cat)
                 dd_task_error = dds_gt[idx_gt] + (get_task_error(dds_gt[idx_gt]))**2
                 self.update_errors(dd_task_error, dds_gt[idx_gt], cat, self.errors['task_error'])
-                dd_pixel_error = get_pixel_error(dds_gt[idx_gt], zzs_gt[idx_gt])
+                dd_pixel_error = dds_gt[idx_gt] + get_pixel_error(zzs_gt[idx_gt])
                 self.update_errors(dd_pixel_error, dds_gt[idx_gt], cat, self.errors['pixel_error'])
 
     def _compare_error(self, out_gt, methods_out):
@@ -211,7 +211,7 @@ def _compare_error(self, out_gt, methods_out):
                 self.update_errors(dd_monoloco, dd_gt, cat, self.errors['monoloco_merged'])
                 self.update_errors(dd_geometric, dd_gt, cat, self.errors['geometric_merged'])
                 self.update_errors(dd_gt + get_task_error(dd_gt), dd_gt, cat, self.errors['task_error_merged'])
-                dd_pixel = get_pixel_error(dd_gt, zzs_gt[idx_gt])
+                dd_pixel = dd_gt + get_pixel_error(zzs_gt[idx_gt])
                 self.update_errors(dd_pixel, dd_gt, cat, self.errors['pixel_error_merged'])
 
                 for key in self.methods:

monoloco/eval/stereo_baselines.py

@@ -20,7 +20,7 @@ def baselines_association(baselines, zzs, keypoints, keypoints_right, reid_featu
         keypoints, keypoints_right, baselines, reid_features)
 
     # count maximum possible associations
-    cnt_stereo['max'] = min(keypoints.shape[0], keypoints_r.shape[0])
+    cnt_stereo['max'] = min(keypoints.shape[0], keypoints_r.shape[0])  # pylint: disable=E1136
 
     # Filter joints disparity and calculate avg disparity
     avg_disparities, disparities_x, disparities_y = mask_joint_disparity(keypoints, keypoints_r)
@@ -161,7 +161,8 @@ def verify_stereo(zz_stereo, zz_mono, disparity_x, disparity_y):
 
     if abs(zz_stereo - zz_mono) < z_max_difference and \
             avg_disparity_y < y_max_difference and \
-            cov < COV_MIN:
+            cov < COV_MIN\
+            and 4 < zz_stereo < 40:
         return True
     # if not np.isnan(zz_stereo):
     #     return True

monoloco/run.py

@@ -128,7 +128,7 @@ def main():
 
     elif args.command == 'eval':
         if args.geometric:
-            assert args.joints,  "joints argument not provided"
+            assert args.joints, "joints argument not provided"
             from .eval import geometric_baseline
             geometric_baseline(args.joints)
 

monoloco/train/trainer.py

@@ -231,7 +231,7 @@ def evaluate(self, load=False, model=None, debug=False):
                 # Debug plot for input-output distributions
                 if debug:
                     debug_plots(inputs, labels)
-                    exit()
+                    sys.exit()
 
                 # Forward pass
                 outputs = self.model(inputs)

monoloco/utils/kitti.py

@@ -93,8 +93,9 @@ def check_conditions(line, category, method, thresh=0.3):
         check = True
 
     else:
+        zz = float(line[13])
         conf = float(line[15])
-        if conf >= thresh:
+        if conf >= thresh and 0.5 < zz < 70:
             check = True
 
     return check

monoloco/utils/misc.py

@@ -30,12 +30,12 @@ def get_task_error(dd):
     return dd * mm
 
 
-def get_pixel_error(dd_gt, zz_gt):
+def get_pixel_error(zz_gt):
     """calculate error in stereo distance due to 1 pixel mismatch (function of depth)"""
 
     disp = 0.54 * 721 / zz_gt
-    delta_z = zz_gt - 0.54 * 721 / (disp - 1)
-    return dd_gt + delta_z
+    error = abs(zz_gt - 0.54 * 721 / (disp - 1))
+    return error
 
 
 def open_annotations(path_ann):

monoloco/visuals/figures.py

@@ -1,4 +1,3 @@
-
 # pylint: disable=R0915
 
 import math
@@ -9,11 +8,10 @@
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse
 
-from ..utils import get_task_error
-
+from ..utils import get_task_error, get_pixel_error
 
-def show_results(dic_stats, show=False, save=False):
 
+def show_results(dic_stats, show=False, save=False, stereo=False):
     """
     Visualize error as function of the distance and compare it with target errors based on human height analyses
     """
@@ -22,40 +20,41 @@ def show_results(dic_stats, show=False, save=False):
     phase = 'test'
     x_min = 0
     x_max = 38
+    y_min = 0
+    y_max = 4.7
     xx = np.linspace(0, 60, 100)
     excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
     clusters = tuple([clst for clst in dic_stats[phase]['monoloco'] if clst not in excl_clusters])
     yy_gender = get_task_error(xx)
 
-    plt.figure(0)
-    plt.grid(linewidth=0.2)
-    plt.xlabel("Ground-truth distance [m]")
-    plt.ylabel("Average localization error [m]")
-    plt.xlim(x_min, x_max)
-    labels = ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)']
-    mks = ['*', '^', 'p', 's', 'o']
-    mksizes = [6, 6, 6, 6, 6]
-    lws = [1.5, 1.5, 1.5, 2.2, 1.6]
-    colors = ['r', 'deepskyblue', 'grey', 'b', 'darkorange']
-    lstyles = ['solid', 'solid', 'solid', 'solid', 'dashdot']
-
-    for idx, method in enumerate(['m3d_merged', 'geometric_merged', 'monodepth_merged', 'monoloco_merged',
-                                  '3dop_merged']):
-        errs = [dic_stats[phase][method][clst]['mean'] for clst in clusters]
-        assert errs, "method %s empty" % method
-        xxs = get_distances(clusters)
-
-        plt.plot(xxs, errs, marker=mks[idx], markersize=mksizes[idx], linewidth=lws[idx], label=labels[idx],
-                 linestyle=lstyles[idx], color=colors[idx])
-    plt.plot(xx, yy_gender, '--', label="Task error", color='lightgreen', linewidth=2.5)
-    plt.legend(loc='upper left')
-    if save:
-        path_fig = os.path.join(dir_out, 'results.png')
-        plt.savefig(path_fig)
-        print("Figure of results saved in {}".format(path_fig))
-    if show:
-        plt.show()
-    plt.close()
+    styles = printing_styles(stereo)
+    for idx_style, (key, style) in enumerate(styles.items()):
+        plt.figure(idx_style)
+        plt.grid(linewidth=0.2)
+        plt.xlim(x_min, x_max)
+        plt.ylim(y_min, y_max)
+        plt.xlabel("Ground-truth distance [m]")
+        plt.ylabel("Average localization error [m]")
+        for idx, method in enumerate(style['methods']):
+            errs = [dic_stats[phase][method][clst]['mean'] for clst in clusters]
+            assert errs, "method %s empty" % method
+            xxs = get_distances(clusters)
+
+            plt.plot(xxs, errs, marker=style['mks'][idx], markersize=style['mksizes'][idx], linewidth=style['lws'][idx],
+                     label=style['labels'][idx], linestyle=style['lstyles'][idx], color=style['colors'][idx])
+        plt.plot(xx, yy_gender, '--', label="Task error", color='lightgreen', linewidth=2.5)
+        if key == 'stereo':
+            yy_stereo = get_pixel_error(xx)
+            plt.plot(xx, yy_stereo, linewidth=1.7, color='k', label='Pixel error')
+
+        plt.legend(loc='upper left')
+        if save:
+            path_fig = os.path.join(dir_out, 'results_' + key + '.png')
+            plt.savefig(path_fig)
+            print("Figure of results " + key + " saved in {}".format(path_fig))
+        if show:
+            plt.show()
+        plt.close()
 
 
 def show_spread(dic_stats, show=False, save=False):
@@ -66,7 +65,7 @@ def show_spread(dic_stats, show=False, save=False):
     excl_clusters = ['all', '50', '>50', 'easy', 'moderate', 'hard']
     clusters = tuple([clst for clst in dic_stats[phase]['our'] if clst not in excl_clusters])
 
-    plt.figure(1)
+    plt.figure(2)
     fig, ax = plt.subplots(2, sharex=True)
     plt.xlabel("Distance [m]")
     plt.ylabel("Aleatoric uncertainty [m]")
@@ -80,10 +79,10 @@ def show_spread(dic_stats, show=False, save=False):
     yys = get_task_error(np.array(xxs))
     ax[1].plot(xxs, bbs, marker='s', color='b', label="Spread b")
     ax[1].plot(xxs, yys, '--', color='lightgreen', label="Task error", linewidth=2.5)
-    yys_up = [rec_c + ar/2 * scale * yy for yy in yys]
-    bbs_up = [rec_c + ar/2 * scale * bb for bb in bbs]
-    yys_down = [rec_c - ar/2 * scale * yy for yy in yys]
-    bbs_down = [rec_c - ar/2 * scale * bb for bb in bbs]
+    yys_up = [rec_c + ar / 2 * scale * yy for yy in yys]
+    bbs_up = [rec_c + ar / 2 * scale * bb for bb in bbs]
+    yys_down = [rec_c - ar / 2 * scale * yy for yy in yys]
+    bbs_down = [rec_c - ar / 2 * scale * bb for bb in bbs]
 
     if plots_line:
         ax[0].plot(xxs, yys_up, '--', color='lightgreen', markersize=5, linewidth=1.4)
@@ -92,8 +91,8 @@ def show_spread(dic_stats, show=False, save=False):
         ax[0].plot(xxs, bbs_down, marker='s', color='b', markersize=5, linewidth=0.7)
 
     for idx, xx in enumerate(xxs):
-        te = Ellipse((xx, rec_c), width=yys[idx]*ar*scale, height=scale, angle=90, color='lightgreen', fill=True)
-        bi = Ellipse((xx, rec_c), width=bbs[idx]*ar*scale, height=scale, angle=90, color='b', linewidth=1.8,
+        te = Ellipse((xx, rec_c), width=yys[idx] * ar * scale, height=scale, angle=90, color='lightgreen', fill=True)
+        bi = Ellipse((xx, rec_c), width=bbs[idx] * ar * scale, height=scale, angle=90, color='b', linewidth=1.8,
                      fill=False)
 
         ax[0].add_patch(te)
@@ -113,9 +112,9 @@ def show_spread(dic_stats, show=False, save=False):
 
 def show_task_error(show, save):
     """Task error figure"""
-    plt.figure(2)
+    plt.figure(3)
     dir_out = 'docs'
-    xx = np.linspace(0, 40, 100)
+    xx = np.linspace(0.1, 50, 100)
     mu_men = 178
     mu_women = 165
     mu_child_m = 164
@@ -128,12 +127,14 @@ def show_task_error(show, save):
     yy_young_male = target_error(xx, mm_young_male)
     yy_young_female = target_error(xx, mm_young_female)
     yy_gender = target_error(xx, mm_gmm)
+    yy_stereo = get_pixel_error(xx)
     plt.grid(linewidth=0.3)
     plt.plot(xx, yy_young_male, linestyle='dotted', linewidth=2.1, color='b', label='Adult/young male')
     plt.plot(xx, yy_young_female, linestyle='dotted', linewidth=2.1, color='darkorange', label='Adult/young female')
     plt.plot(xx, yy_gender, '--', color='lightgreen', linewidth=2.8, label='Generic adult (task error)')
     plt.plot(xx, yy_female, '-.', linewidth=1.7, color='darkorange', label='Adult female')
     plt.plot(xx, yy_male, '-.', linewidth=1.7, color='b', label='Adult male')
+    plt.plot(xx, yy_stereo, linewidth=1.7, color='k', label='Pixel error')
     plt.xlim(np.min(xx), np.max(xx))
     plt.xlabel("Ground-truth distance from the camera $d_{gt}$ [m]")
     plt.ylabel("Localization error $\hat{e}$  due to human height variation [m]")  # pylint: disable=W1401
@@ -193,7 +194,6 @@ def calculate_gmm():
 
 
 def get_confidence(xx, zz, std):
-
     theta = math.atan2(zz, xx)
 
     delta_x = std * math.cos(theta)
@@ -215,11 +215,9 @@ def get_distances(clusters):
 
 
 def get_confidence_points(confidences, distances, errors):
-
     confidence_points = []
     distance_points = []
     for idx, dd in enumerate(distances):
-
         conf_perc = confidences[idx]
         confidence_points.append(errors[idx] + conf_perc)
         confidence_points.append(errors[idx] - conf_perc)
@@ -230,7 +228,6 @@ def get_confidence_points(confidences, distances, errors):
 
 
 def height_distributions():
-
     mu_men = 178
     std_men = 7
     mu_women = 165
@@ -256,7 +253,7 @@ def expandgrid(*itrs):
 
 def plot_dist(dist_gmm, dist_men, dist_women):
     try:
-        import seaborn as sns
+        import seaborn as sns  # pylint: disable=C0415
         sns.distplot(dist_men, hist=False, rug=False, label="Men")
         sns.distplot(dist_women, hist=False, rug=False, label="Women")
         sns.distplot(dist_gmm, hist=False, rug=False, label="GMM")
@@ -273,9 +270,30 @@ def get_percentile(dist_gmm):
     dd_gt = 1000
     mu_gmm = np.mean(dist_gmm)
     dist_d = dd_gt * mu_gmm / dist_gmm
-    perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5]) # Laplace bi => 63%
+    perc_d, _ = np.nanpercentile(dist_d, [18.5, 81.5])  # Laplace bi => 63%
     perc_d2, _ = np.nanpercentile(dist_d, [23, 77])
     mu_d = np.mean(dist_d)
     # mm_bi = (mu_d - perc_d) / mu_d
     # mm_test = (mu_d - perc_d2) / mu_d
     # mad_d = np.mean(np.abs(dist_d - mu_d))
+
+
+def printing_styles(stereo):
+    style = {'mono': {"labels": ['Mono3D', 'Geometric Baseline', 'MonoDepth', 'Our MonoLoco', '3DOP (stereo)'],
+                      "methods": ['m3d_merged', 'geometric_merged', 'monodepth_merged', 'monoloco_merged',
+                                  '3dop_merged'],
+                      "mks": ['*', '^', 'p', 's', 'o'],
+                      "mksizes": [6, 6, 6, 6, 6], "lws": [1.5, 1.5, 1.5, 2.2, 1.6],
+                      "colors": ['r', 'deepskyblue', 'grey', 'b', 'darkorange'],
+                      "lstyles": ['solid', 'solid', 'solid', 'solid', 'dashdot']}}
+    if stereo:
+        style['stereo'] = {"labels": ['3DOP', 'Pose Baseline', 'ReiD Baseline', 'Our MonoLoco (monocular)',
+                                      'Our Stereo Baseline'],
+                           "methods": ['3dop_merged', 'pose_merged', 'reid_merged', 'monoloco_merged',
+                                       'ml_stereo_merged'],
+                           "mks": ['o', '^', 'p', 's', 's'],
+                           "mksizes": [6, 6, 6, 4, 6], "lws": [1.5, 1.5, 1.5, 1.2, 1.5],
+                           "colors": ['darkorange', 'lightblue', 'red', 'b', 'b'],
+                           "lstyles": ['solid', 'solid', 'solid', 'dashed', 'solid']}
+
+    return style