Detection support (#60)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com>
This commit is contained in:
Ayush Chaurasia
GitHub
@ -459,14 +459,14 @@ def ap_per_class_box_and_mask(
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"boxes": {
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"p": results_boxes[0],
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"r": results_boxes[1],
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"ap": results_boxes[3],
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"f1": results_boxes[2],
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"ap": results_boxes[3],
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"ap_class": results_boxes[4]},
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"masks": {
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"p": results_masks[0],
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"r": results_masks[1],
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"ap": results_masks[3],
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"f1": results_masks[2],
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"ap": results_masks[3],
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"ap_class": results_masks[4]}}
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return results
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@ -547,7 +547,7 @@ class Metric:
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Args:
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results: tuple(p, r, ap, f1, ap_class)
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"""
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p, r, all_ap, f1, ap_class_index = results
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p, r, f1, all_ap, ap_class_index = results
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self.p = p
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self.r = r
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self.all_ap = all_ap
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@ -186,7 +186,15 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
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@threaded
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def plot_images_and_masks(images, batch_idx, cls, bboxes, masks, paths, confs=None, fname='images.jpg', names=None):
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def plot_images_and_masks(images,
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batch_idx,
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cls,
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bboxes,
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masks,
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confs=None,
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paths=None,
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fname='images.jpg',
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names=None):
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# Plot image grid with labels
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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@ -327,3 +335,99 @@ def output_to_target(output, max_det=300):
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targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
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targets = torch.cat(targets, 0).numpy()
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return targets[:, 0], targets[:, 1], targets[:, 2:6], targets[:, 6]
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@threaded
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def plot_images(images, batch_idx, cls, bboxes, confs=None, paths=None, fname='images.jpg', names=None):
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# Plot image grid with labels
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if isinstance(images, torch.Tensor):
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images = images.cpu().float().numpy()
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if isinstance(cls, torch.Tensor):
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cls = cls.cpu().numpy()
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if isinstance(bboxes, torch.Tensor):
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bboxes = bboxes.cpu().numpy()
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if isinstance(batch_idx, torch.Tensor):
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batch_idx = batch_idx.cpu().numpy()
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max_size = 1920 # max image size
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max_subplots = 16 # max image subplots, i.e. 4x4
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bs, _, h, w = images.shape # batch size, _, height, width
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bs = min(bs, max_subplots) # limit plot images
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ns = np.ceil(bs ** 0.5) # number of subplots (square)
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if np.max(images[0]) <= 1:
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images *= 255 # de-normalise (optional)
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# Build Image
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mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
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for i, im in enumerate(images):
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if i == max_subplots: # if last batch has fewer images than we expect
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break
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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im = im.transpose(1, 2, 0)
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mosaic[y:y + h, x:x + w, :] = im
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# Resize (optional)
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scale = max_size / ns / max(h, w)
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if scale < 1:
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h = math.ceil(scale * h)
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w = math.ceil(scale * w)
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mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
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# Annotate
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fs = int((h + w) * ns * 0.01) # font size
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annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
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for i in range(i + 1):
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x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
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annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
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if paths:
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annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
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if len(cls) > 0:
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idx = batch_idx == i
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boxes = xywh2xyxy(bboxes[idx]).T
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classes = cls[idx].astype('int')
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labels = confs is None # labels if no conf column
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conf = None if labels else confs[idx] # check for confidence presence (label vs pred)
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if boxes.shape[1]:
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if boxes.max() <= 1.01: # if normalized with tolerance 0.01
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boxes[[0, 2]] *= w # scale to pixels
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boxes[[1, 3]] *= h
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elif scale < 1: # absolute coords need scale if image scales
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boxes *= scale
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boxes[[0, 2]] += x
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boxes[[1, 3]] += y
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for j, box in enumerate(boxes.T.tolist()):
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c = classes[j]
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color = colors(c)
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c = names[c] if names else c
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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label = f'{c}' if labels else f'{c} {conf[j]:.1f}'
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annotator.box_label(box, label, color=color)
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annotator.im.save(fname) # save
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def plot_results(file='path/to/results.csv', dir=''):
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# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
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save_dir = Path(file).parent if file else Path(dir)
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fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
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ax = ax.ravel()
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files = list(save_dir.glob('results*.csv'))
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assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
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for f in files:
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try:
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data = pd.read_csv(f)
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s = [x.strip() for x in data.columns]
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x = data.values[:, 0]
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for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
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y = data.values[:, j].astype('float')
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# y[y == 0] = np.nan # don't show zero values
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ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
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ax[i].set_title(s[j], fontsize=12)
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# if j in [8, 9, 10]: # share train and val loss y axes
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# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
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except Exception as e:
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print(f'Warning: Plotting error for {f}: {e}')
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ax[1].legend()
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fig.savefig(save_dir / 'results.png', dpi=200)
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plt.close()
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