module updates

utils/utils.py

@@ -468,6 +468,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, fast=False, c
         nx6 (x1, y1, x2, y2, conf, cls)
     """
     nc = prediction[0].shape[1] - 5  # number of classes
+    xc = prediction[..., 4] > conf_thres  # candidates
 
     # Settings
     min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
@@ -487,7 +488,7 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, fast=False, c
     for xi, x in enumerate(prediction):  # image index, image inference
         # Apply constraints
         # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
-        x = x[x[:, 4] > conf_thres]  # confidence
+        x = x[xc[xi]]  # confidence
 
         # If none remain process next image
         if not x.shape[0]:
@@ -1074,9 +1075,9 @@ def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_re
         for i in range(5):
             for j in [i, i + 5]:
                 y = results[j, x]
-                # ax[i].plot(x, y, marker='.', label=s[j])
-                y_smooth = butter_lowpass_filtfilt(y)
-                ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
+                ax[i].plot(x, y, marker='.', label=s[j])
+                # y_smooth = butter_lowpass_filtfilt(y)
+                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
 
             ax[i].set_title(t[i])
             ax[i].legend()