Add segment line predictions (#9571)

segment/predict.py

@@ -42,9 +42,10 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 from models.common import DetectMultiBackend
 from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
 from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
-                           increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
+                           increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
+                           strip_optimizer, xyxy2xywh)
 from utils.plots import Annotator, colors, save_one_box
-from utils.segment.general import process_mask
+from utils.segment.general import masks2segments, process_mask
 from utils.torch_utils import select_device, smart_inference_mode
 
 
@@ -145,14 +146,16 @@ def run(
             save_path = str(save_dir / p.name)  # im.jpg
             txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
             s += '%gx%g ' % im.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             imc = im0.copy() if save_crop else im0  # for save_crop
             annotator = Annotator(im0, line_width=line_thickness, example=str(names))
             if len(det):
                 masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True)  # HWC
+                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()  # rescale boxes to im0 size
 
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
+                # Segments
+                if save_txt:
+                    segments = reversed(masks2segments(masks))
+                    segments = [scale_segments(im.shape[2:], x, im0.shape).round() for x in segments]
 
                 # Print results
                 for c in det[:, 5].unique():
@@ -165,10 +168,10 @@ def run(
                                 im_gpu=None if retina_masks else im[i])
 
                 # Write results
-                for *xyxy, conf, cls in reversed(det[:, :6]):
+                for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
                     if save_txt:  # Write to file
-                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
+                        segj = segments[j].reshape(-1)  # (n,2) to (n*2)
+                        line = (cls, *segj, conf) if save_conf else (cls, *segj)  # label format
                         with open(f'{txt_path}.txt', 'a') as f:
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
@@ -176,6 +179,7 @@ def run(
                         c = int(cls)  # integer class
                         label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                         annotator.box_label(xyxy, label, color=colors(c, True))
+                        annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
                     if save_crop:
                         save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 

utils/segment/general.py

 import cv2
+import numpy as np
 import torch
 import torch.nn.functional as F
 
@@ -118,3 +119,16 @@ def masks_iou(mask1, mask2, eps=1e-7):
     intersection = (mask1 * mask2).sum(1).clamp(0)  # (N, )
     union = (mask1.sum(1) + mask2.sum(1))[None] - intersection  # (area1 + area2) - intersection
     return intersection / (union + eps)
+
+
+def masks2segments(masks, strategy='largest'):
+    # Convert masks(n,160,160) into segments(n,xy)
+    segments = []
+    for x in masks.int().numpy().astype('uint8'):
+        c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
+        if strategy == 'concat':  # concatenate all segments
+            c = np.concatenate([x.reshape(-1, 2) for x in c])
+        elif strategy == 'largest':  # select largest segment
+            c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
+        segments.append(c.astype('float32'))
+    return segments