Update PR curve (#1428)

* Update PR curve

* legend outside

* list(Path().glob())

test.py

     # Compute statistics
     stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
     if len(stats) and stats[0].any():
-        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
+        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
         p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # [P, R, AP@0.5, AP@0.5:0.95]
         mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
         nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class

utils/metrics.py

 # Model validation metrics
 
+from pathlib import Path
+
 import matplotlib.pyplot as plt
 import numpy as np
 
     return (x[:, :4] * w).sum(1)
 
 
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'):
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]):
     """ Compute the average precision, given the recall and precision curves.
     Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
         pred_cls:  Predicted object classes (nparray).
         target_cls:  True object classes (nparray).
         plot:  Plot precision-recall curve at mAP@0.5
-        fname:  Plot filename
+        save_dir:  Plot save directory
     # Returns
         The average precision as computed in py-faster-rcnn.
     """
     f1 = 2 * p * r / (p + r + 1e-16)
 
     if plot:
-        py = np.stack(py, axis=1)
-        fig, ax = plt.subplots(1, 1, figsize=(5, 5))
-        ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
-        ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
-        ax.set_xlabel('Recall')
-        ax.set_ylabel('Precision')
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        plt.legend()
-        fig.tight_layout()
-        fig.savefig(fname, dpi=200)
+        plot_pr_curve(px, py, ap, save_dir, names)
 
     return p, r, ap, f1, unique_classes.astype('int32')
 
         ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
 
     return ap, mpre, mrec
+
+
+def plot_pr_curve(px, py, ap, save_dir='.', names=()):
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6))
+    py = np.stack(py, axis=1)
+
+    if 0 < len(names) < 21:  # show mAP in legend if < 10 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)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+    ax.set_xlabel('Recall')
+    ax.set_ylabel('Precision')
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.tight_layout()
+    fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)

utils/plots.py

         cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
 
 
-def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
+def plot_wh_methods():  # from utils.plots import *; plot_wh_methods()
     # Compares the two methods for width-height anchor multiplication
     # https://github.com/ultralytics/yolov3/issues/168
     x = np.arange(-4.0, 4.0, .1)
     plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
 
 
-def plot_test_txt():  # from utils.general import *; plot_test()
+def plot_test_txt():  # from utils.plots import *; plot_test()
     # Plot test.txt histograms
     x = np.loadtxt('test.txt', dtype=np.float32)
     box = xyxy2xywh(x[:, :4])
     plt.savefig('hist1d.png', dpi=200)
 
 
-def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
+def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
     # Plot targets.txt histograms
     x = np.loadtxt('targets.txt', dtype=np.float32).T
     s = ['x targets', 'y targets', 'width targets', 'height targets']
     plt.savefig('targets.jpg', dpi=200)
 
 
-def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
+def plot_study_txt(f='study.txt', x=None):  # from utils.plots import *; plot_study_txt()
     # Plot study.txt generated by test.py
     fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
     ax = ax.ravel()
         pass
 
 
-def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general import *; plot_evolution()
+def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()
     # Plot hyperparameter evolution results in evolve.txt
     with open(yaml_file) as f:
         hyp = yaml.load(f, Loader=yaml.FullLoader)
     print('\nPlot saved as evolve.png')
 
 
-def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
+def plot_results_overlay(start=0, stop=0):  # from utils.plots import *; plot_results_overlay()
     # Plot training 'results*.txt', overlaying train and val losses
     s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
     t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
 
 
 def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
-    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
-    # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
+    # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()
     s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
          'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
     if bucket:
-        # os.system('rm -rf storage.googleapis.com')
         # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
         files = ['results%g.txt' % x for x in id]
         c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
         os.system(c)
     else:
-        files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
+        files = list(Path(save_dir).glob('results*.txt'))
     assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
     for fi, f in enumerate(files):
         try:
                 if i in [0, 1, 2, 5, 6, 7]:
                     y[y == 0] = np.nan  # don't show zero loss values
                     # y /= y[0]  # normalize
-                label = labels[fi] if len(labels) else Path(f).stem
+                label = labels[fi] if len(labels) else f.stem
                 ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
                 ax[i].set_title(s[i])
                 # if i in [5, 6, 7]:  # share train and val loss y axes