144 lines
6.2 KiB
Python
144 lines
6.2 KiB
Python
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import contextlib
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import math
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from pathlib import Path
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import torch
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from .. import threaded
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from ..general import xywh2xyxy
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from ..plots import Annotator, colors
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@threaded
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def plot_images_and_masks(images, targets, masks, 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(targets, torch.Tensor):
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targets = targets.cpu().numpy()
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if isinstance(masks, torch.Tensor):
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masks = masks.cpu().numpy().astype(int)
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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(targets) > 0:
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idx = targets[:, 0] == i
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ti = targets[idx] # image targets
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boxes = xywh2xyxy(ti[:, 2:6]).T
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classes = ti[:, 1].astype('int')
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labels = ti.shape[1] == 6 # labels if no conf column
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conf = None if labels else ti[:, 6] # 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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cls = classes[j]
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color = colors(cls)
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cls = names[cls] if names else cls
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
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annotator.box_label(box, label, color=color)
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# Plot masks
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if len(masks):
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if masks.max() > 1.0: # mean that masks are overlap
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image_masks = masks[[i]] # (1, 640, 640)
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nl = len(ti)
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index = np.arange(nl).reshape(nl, 1, 1) + 1
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image_masks = np.repeat(image_masks, nl, axis=0)
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image_masks = np.where(image_masks == index, 1.0, 0.0)
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else:
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image_masks = masks[idx]
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im = np.asarray(annotator.im).copy()
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for j, box in enumerate(boxes.T.tolist()):
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if labels or conf[j] > 0.25: # 0.25 conf thresh
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color = colors(classes[j])
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mh, mw = image_masks[j].shape
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if mh != h or mw != w:
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mask = image_masks[j].astype(np.uint8)
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mask = cv2.resize(mask, (w, h))
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mask = mask.astype(bool)
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else:
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mask = image_masks[j].astype(bool)
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with contextlib.suppress(Exception):
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im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
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annotator.fromarray(im)
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annotator.im.save(fname) # save
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def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
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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, 8, figsize=(18, 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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index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
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0.1 * data.values[:, 11])
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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, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
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y = data.values[:, j]
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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=2)
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if best:
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# best
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ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
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ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
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else:
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# last
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ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
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ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
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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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