Add idetection_profile() function to plots.py (#1700)

utils/plots.py

 # Plotting utils
 
 import glob
+import math
 import os
 import random
 from copy import copy
 from pathlib import Path
 
 import cv2
-import math
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
@@ -329,6 +329,39 @@ def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots impo
     print('\nPlot saved as evolve.png')
 
 
+def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
+    # Plot iDetection '*.txt' per-image logs. from . import *; profile_idetection()
+    fig, ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)
+    ax = ax.ravel()
+    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
+    files = list(Path(save_dir).glob('frames*.txt'))
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows
+            n = results.shape[1]  # number of rows
+            x = np.arange(start, min(stop, n) if stop else n)
+            t = (results[0] - results[0].min())  # set t0=0s
+            results[0] = x
+            for i, a in enumerate(ax):
+                if i < len(results):
+                    y = results[i, x]
+                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
+                    a.plot(t, y, marker='.', label=label, linewidth=1, markersize=5)
+                    a.set_title(s[i])
+                    a.set_xlabel('time (s)')
+                    # if fi == len(files) - 1:
+                    # a.set_ylim(bottom=0)
+                    for side in ['top', 'right']:
+                        a.spines[side].set_visible(False)
+                else:
+                    a.remove()
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
+
+    ax[1].legend()
+    fig.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
+
+
 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