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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()
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