在使用MMDetection训练之前，需要对图像进行可视化验证，验证数据和标签是否对齐。
# 数据集可视化
import os
import matplotlib.pyplot as plt
from PIL import Imageoriginal_images = []
images = []
texts = []
plt.figure(figsize=(16,12))image_paths = [filename for filename in os.listdir(r"E:\****************************")][:8] # 取前8张图片for i, filename in enumerate(image_paths):name = os.path.splitext(filename)[0]image = Image.open(os.path.join(r"E:\***************************",filename)).convert("RGB")plt.subplot(4,2,i+1)plt.imshow(image)plt.title(f"{filename}")plt.xticks([]) # 设置坐标轴plt.yticks([])
plt.tight_layout()
plt.show()以上代码 提供了数据集图片查看的功能，需要加入自己对应的图片路径。
以下代码 提供了COCO数据集标签与图片的显示功能，从数据集中随机选取了8张图片进行展示，以可视化数据集图片与标签是否对准。需要填入json路径和image的保存路径。
# COCO 数据集可视化
from pycocotools.coco import COCO
import numpy as np
import os.path as osp
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
from PIL import Imagedef apply_exif_orientation(image):_ExIF_ORIENT = 274if not hasattr(image,'getexif'):return imagetry:exif = image.getexif()except Exception:exif = Noneif exif is None:return imageorientation = exif.get(_ExIF_ORIENT)method = {2: Image.FLIP_LEFT_RIGHT,3: Image.ROTATE_180,4: Image.FLIP_TOP_BOTTOM,5: Image.TRANSPOSE,6: Image.ROTATE_270,7: Image.TRANSVERSE,8: Image.ROTATE_90,}.get(orientation)if method is not None:return image.transpose(method)return imagedef show_bbox_only(coco, anns, show_label_bbox = True, is_filling = True):if len(anns) == 0:returnax = plt.gca()ax.set_autoscale_on(False) # 自动调整坐标轴范围image2color = dict()for cat in coco.getCatIds():image2color[cat] = (np.random.random((1, 3)) * 0.7 + 0.3).tolist()[0]polygons = []colors = []for ann in anns:color = image2color[ann["category_id"]]bbox_xmin, bbox_ymin, bbox_w, bbox_h = ann['bbox']poly = [[bbox_xmin, bbox_ymin],[bbox_xmin, bbox_ymin+bbox_h],[bbox_xmin+bbox_w, bbox_ymin+bbox_h], [bbox_xmin+bbox_w, bbox_ymin]]polygons.append(Polygon(np.array(poly).reshape((4,2))))colors.append(color)if show_label_bbox:label_bbox = dict(facecolor = color)else:label_bbox = Noneax.text(bbox_xmin,bbox_ymin,"%s" % (coco.loadCats(ann['category_id'])[0]['name']),color = 'white',bbox = label_bbox)if is_filling:p = PatchCollection(polygons, facecolor = colors, linewidths = 0, alpha = 0.4)ax.add_collection(p)p = PatchCollection(polygons, facecolor = None, linewidths = 0, alpha = 0.4)ax.add_collection(p)coco = COCO(r'E:\*******保存的json文件夹\test.json')
image_ids = coco.getImgIds()
np.random.shuffle(image_ids)plt.figure(figsize=(16,12))for i in range(8):image_data = coco.loadImgs(image_ids[i])[0]image_path = osp.join(r'E:\保存的图片文件夹',image_data['file_name'])annotation_ids = coco.getAnnIds(imgIds=image_data['id'], catIds=[], iscrowd=0)annotations = coco.loadAnns(annotation_ids)ax = plt.subplot(4,2,i+1)image = Image.open(image_path).convert('RGB')image = apply_exif_orientation(image)ax.imshow(image)show_bbox_only(coco, annotations)plt.title(f"{filename}")plt.xticks([])plt.yticks([])plt.tight_layout()
plt.show()
