updates

detect.py

@@ -133,7 +133,7 @@ def detect(opt):
                     # Add the bbox to the plot
                     label = '%s %.2f' % (classes[int(cls_pred)], conf)
                     color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])]
-                    plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3)
+                    plot_one_box([x1, y1, x2, y2], img, label=label, color=color)
 
         if opt.plot_flag:
             # Save generated image with detections

test.py

@@ -8,7 +8,7 @@ parser = argparse.ArgumentParser()
 parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
 parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
-parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file')
+parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file')
 parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
 parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
 parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')
@@ -63,6 +63,9 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
         output = model(imgs)
         output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
 
+    # import matplotlib.pyplot as plt
+    # plt.imshow(imgs[1][0])
+
     # Compute average precision for each sample
     for sample_i in range(len(targets)):
         correct = []

utils/datasets.py

@@ -89,8 +89,7 @@ class load_images_and_labels():  # for training
 
     def __iter__(self):
         self.count = -1
-        self.shuffled_vector = np.random.permutation(self.nF)  # shuffled vector
-        # self.shuffled_vector = np.arange(self.nF)  # not shuffled
+        self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
         return self
 
     def __next__(self):

utils/utils.py

@@ -40,7 +40,7 @@ def xview_class_weights(indices):  # weights of each class in the training set,
 
 
 def plot_one_box(x, img, color=None, label=None, line_thickness=None):  # Plots one bounding box on image img
-    tl = line_thickness or round(0.003 * max(img.shape[0:2]))  # line thickness
+    tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1  # line thickness
     color = color or [random.randint(0, 255) for _ in range(3)]
     c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
     cv2.rectangle(img, c1, c2, color, thickness=tl)