import contextlib import math from pathlib import Path import cv2 import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from .. import threaded from ..general import xywh2xyxy from ..plots import Annotator, colors @threaded def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None): # Plot image grid with labels if isinstance(images, torch.Tensor): images = images.cpu().float().numpy() if isinstance(targets, torch.Tensor): targets = targets.cpu().numpy() if isinstance(masks, torch.Tensor): masks = masks.cpu().numpy().astype(int) max_size = 1920 # max image size max_subplots = 16 # max image subplots, i.e. 4x4 bs, _, h, w = images.shape # batch size, _, height, width bs = min(bs, max_subplots) # limit plot images ns = np.ceil(bs ** 0.5) # number of subplots (square) if np.max(images[0]) <= 1: images *= 255 # de-normalise (optional) # Build Image mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init for i, im in enumerate(images): if i == max_subplots: # if last batch has fewer images than we expect break x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin im = im.transpose(1, 2, 0) mosaic[y:y + h, x:x + w, :] = im # Resize (optional) scale = max_size / ns / max(h, w) if scale < 1: h = math.ceil(scale * h) w = math.ceil(scale * w) mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) # Annotate fs = int((h + w) * ns * 0.01) # font size annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) for i in range(i + 1): x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders if paths: annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames if len(targets) > 0: idx = targets[:, 0] == i ti = targets[idx] # image targets boxes = xywh2xyxy(ti[:, 2:6]).T classes = ti[:, 1].astype('int') labels = ti.shape[1] == 6 # labels if no conf column conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) if boxes.shape[1]: if boxes.max() <= 1.01: # if normalized with tolerance 0.01 boxes[[0, 2]] *= w # scale to pixels boxes[[1, 3]] *= h elif scale < 1: # absolute coords need scale if image scales boxes *= scale boxes[[0, 2]] += x boxes[[1, 3]] += y for j, box in enumerate(boxes.T.tolist()): cls = classes[j] color = colors(cls) cls = names[cls] if names else cls if labels or conf[j] > 0.25: # 0.25 conf thresh label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' annotator.box_label(box, label, color=color) # Plot masks if len(masks): if masks.max() > 1.0: # mean that masks are overlap image_masks = masks[[i]] # (1, 640, 640) nl = len(ti) index = np.arange(nl).reshape(nl, 1, 1) + 1 image_masks = np.repeat(image_masks, nl, axis=0) image_masks = np.where(image_masks == index, 1.0, 0.0) else: image_masks = masks[idx] im = np.asarray(annotator.im).copy() for j, box in enumerate(boxes.T.tolist()): if labels or conf[j] > 0.25: # 0.25 conf thresh color = colors(classes[j]) mh, mw = image_masks[j].shape if mh != h or mw != w: mask = image_masks[j].astype(np.uint8) mask = cv2.resize(mask, (w, h)) mask = mask.astype(bool) else: mask = image_masks[j].astype(bool) with contextlib.suppress(Exception): im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 annotator.fromarray(im) annotator.im.save(fname) # save def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') save_dir = Path(file).parent if file else Path(dir) fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) ax = ax.ravel() files = list(save_dir.glob("results*.csv")) assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." for f in files: try: data = pd.read_csv(f) index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11]) s = [x.strip() for x in data.columns] x = data.values[:, 0] for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): y = data.values[:, j] # y[y == 0] = np.nan # don't show zero values ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2) if best: # best ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3) ax[i].set_title(s[j] + f"\n{round(y[index], 5)}") else: # last ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3) ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}") # if j in [8, 9, 10]: # share train and val loss y axes # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) except Exception as e: print(f"Warning: Plotting error for {f}: {e}") ax[1].legend() fig.savefig(save_dir / "results.png", dpi=200) plt.close()