Update PR curve (#1428)

* Update PR curve

* legend outside

* list(Path().glob())

utils/plots.py

@@ -65,7 +65,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
         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)
@@ -200,7 +200,7 @@ def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
     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])
@@ -217,7 +217,7 @@ def plot_test_txt():  # from utils.general import *; plot_test()
     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']
@@ -230,7 +230,7 @@ def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
     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()
@@ -294,7 +294,7 @@ def plot_labels(labels, save_dir=''):
         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)
@@ -318,7 +318,7 @@ def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general im
     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
@@ -342,20 +342,18 @@ def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_
 
 
 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:
@@ -367,7 +365,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
                 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