updates

detect.py

@@ -61,25 +61,19 @@ def detect(opt):
     imgs = []  # Stores image paths
     img_detections = []  # Stores detections for each image index
     prev_time = time.time()
-    detections = None
     for batch_i, (img_paths, img) in enumerate(dataloader):
         print(batch_i, img.shape, end=' ')
-        preds = []
 
         # Get detections
         with torch.no_grad():
-            # Normal orientation
             chip = torch.from_numpy(img).unsqueeze(0).to(device)
             pred = model(chip)
             pred = pred[pred[:, :, 4] > opt.conf_thres]
 
             if len(pred) > 0:
-                preds.append(pred.unsqueeze(0))
-
-        if len(preds) > 0:
-            detections = non_max_suppression(torch.cat(preds, 1), opt.conf_thres, opt.nms_thres)
-            img_detections.extend(detections)
-            imgs.extend(img_paths)
+                detections = non_max_suppression(pred.unsqueeze(0), opt.conf_thres, opt.nms_thres)
+                img_detections.extend(detections)
+                imgs.extend(img_paths)
 
         print('Batch %d... (Done %.3fs)' % (batch_i, time.time() - prev_time))
         prev_time = time.time()

utils/utils.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 import torch.nn.functional as F
 
-# set printoptions
+# Set printoptions
 torch.set_printoptions(linewidth=1320, precision=5, profile='long')
 np.set_printoptions(linewidth=320, formatter={'float_kind': '{11.5g}'.format})  # format short g, %precision=5
 
@@ -19,7 +19,7 @@ def load_classes(path):
     return names
 
 
-def modelinfo(model):
+def modelinfo(model):  # Plots a line-by-line description of a PyTorch model
     nparams = sum(x.numel() for x in model.parameters())
     ngradients = sum(x.numel() for x in model.parameters() if x.requires_grad)
     print('\n%4s %70s %9s %12s %20s %12s %12s' % ('', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
@@ -39,17 +39,17 @@ def xview_class_weights(indices):  # weights of each class in the training set,
     return weights[indices]
 
 
-def plot_one_box(x, im, color=None, label=None, line_thickness=None):
-    tl = line_thickness or round(0.003 * max(im.shape[0:2]))  # line thickness
+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
     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(im, c1, c2, color, thickness=tl)
+    cv2.rectangle(img, c1, c2, color, thickness=tl)
     if label:
         tf = max(tl - 1, 1)  # font thickness
         t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
         c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(im, c1, c2, color, -1)  # filled
-        cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+        cv2.rectangle(img, c1, c2, color, -1)  # filled
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
 
 
 def weights_init_normal(m):
@@ -61,13 +61,22 @@ def weights_init_normal(m):
         torch.nn.init.constant_(m.bias.data, 0.0)
 
 
-def xyxy2xywh(box):
-    xywh = np.zeros(box.shape)
-    xywh[:, 0] = (box[:, 0] + box[:, 2]) / 2
-    xywh[:, 1] = (box[:, 1] + box[:, 3]) / 2
-    xywh[:, 2] = box[:, 2] - box[:, 0]
-    xywh[:, 3] = box[:, 3] - box[:, 1]
-    return xywh
+def xyxy2xywh(x):  # Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
+    y = np.zeros(x.shape)
+    y[:, 0] = (x[:, 0] + x[:, 2]) / 2
+    y[:, 1] = (x[:, 1] + x[:, 3]) / 2
+    y[:, 2] = x[:, 2] - x[:, 0]
+    y[:, 3] = x[:, 3] - x[:, 1]
+    return y
+
+
+def xywh2xyxy(x):  # Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.zeros(x.shape)
+    y[:, 0] = (x[:, 1] - x[:, 3] / 2)
+    y[:, 1] = (x[:, 2] - x[:, 4] / 2)
+    y[:, 2] = (x[:, 1] + x[:, 3] / 2)
+    y[:, 3] = (x[:, 2] + x[:, 4] / 2)
+    return y
 
 
 def compute_ap(recall, precision):
@@ -98,9 +107,6 @@ def compute_ap(recall, precision):
 
 
 def bbox_iou(box1, box2, x1y1x2y2=True):
-    # if len(box1.shape) == 1:
-    #    box1 = box1.reshape(1, 4)
-
     """
     Returns the IoU of two bounding boxes
     """