Update plots.py

utils/plots.py

@@ -54,7 +54,7 @@ def butter_lowpass(cutoff, fs, order):
     return filtfilt(b, a, data)  # forward-backward filter
 
 
-def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+def plot_one_box(x, img, color=None, label=None, line_thickness=3):
     # Plots one bounding box on image img
     tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
     color = color or [random.randint(0, 255) for _ in range(3)]
@@ -237,12 +237,13 @@ def plot_targets_txt():  # from utils.plots import *; plot_targets_txt()
     plt.savefig('targets.jpg', dpi=200)
 
 
-def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_txt()
+def plot_study_txt(path='/Users/glennjocher/Downloads/studies/study-segments5l-1280/',
+                   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()
 
-    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+    fig2, ax2 = plt.subplots(1, 1, figsize=(16, 8), tight_layout=True)
     # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]:
     for f in sorted(Path(path).glob('study*.txt')):
         y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
@@ -253,15 +254,15 @@ def plot_study_txt(path='', x=None):  # from utils.plots import *; plot_study_tx
         #     ax[i].set_title(s[i])
 
         j = y[3].argmax() + 1
-        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
+        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=1, markersize=4,
                  label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
 
     ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
              'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
 
     ax2.grid(alpha=0.2)
     ax2.set_yticks(np.arange(20, 60, 5))
-    ax2.set_xlim(0, 30)
+    ax2.set_xlim(0, 60)
     ax2.set_ylim(30, 55)
     ax2.set_xlabel('GPU Speed (ms/img)')
     ax2.set_ylabel('COCO AP val')