Metric-Confidence plots feature addition

utils/metrics.py

@@ -38,9 +38,9 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
     # Create Precision-Recall curve and compute AP for each class
     px, py = np.linspace(0, 1, 1000), []  # for plotting
-    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
+    # pr_score = np.linspace(0, 1, 100)  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
     s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
-    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
+    ap, p, r = np.zeros(s), np.zeros(s + [1000]), np.zeros(s + [1000])
     for ci, c in enumerate(unique_classes):
         i = pred_cls == c
         n_l = (target_cls == c).sum()  # number of labels
@@ -55,11 +55,11 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
             # Recall
             recall = tpc / (n_l + 1e-16)  # recall curve
-            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
+            r[ci, 0] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
 
             # Precision
             precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
+            p[ci, 0] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
 
             # AP from recall-precision curve
             for j in range(tp.shape[1]):
@@ -72,6 +72,9 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
     if plot:
         plot_pr_curve(px, py, ap, save_dir, names)
+        plot_mc_curve(px, f1[:, 0].T, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p[:, 0].T, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r[:, 0].T, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
 
     return p, r, ap, f1, unique_classes.astype('int32')
 
@@ -182,12 +185,13 @@ def print(self):
 # Plots ----------------------------------------------------------------------------------------------------------------
 
 def plot_pr_curve(px, py, ap, save_dir='.', names=()):
+    # Precision-recall curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
     py = np.stack(py, axis=1)
 
-    if 0 < len(names) < 21:  # show mAP in legend if < 10 classes
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
         for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0])  # plot(recall, precision)
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
     else:
         ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
 
@@ -198,3 +202,23 @@ def plot_pr_curve(px, py, ap, save_dir='.', names=()):
     ax.set_ylim(0, 1)
     plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
     fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)
+
+
+def plot_mc_curve(px, py, save_dir='.', names=(), xlabel='Confidence', ylabel='Metric'):
+    # Metric-confidence curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py.T):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(recall, precision)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    y = py.mean(1)
+    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(save_dir, dpi=250)