294 lines
12 KiB
Python
294 lines
12 KiB
Python
import numpy as np
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class Evaluator(object):
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def __init__(self, num_class):
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self.num_class = num_class
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# self.confusion_matrix = np.zeros((self.num_class,)*2) # 原始
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self.confusion_matrix = np.zeros((self.num_class, self.num_class)) # 改动
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def Recall_Precision(self):
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TP = np.diag(self.confusion_matrix)
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TP_add_FN = self.confusion_matrix.sum(axis=1) # 每一行的和
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TP_add_FP = self.confusion_matrix.sum(axis=0) # 每一列的和
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Recall = TP / TP_add_FN # 模型正确识别出为正类的样本的数量占总的正类样本数量的比值。一般情况下，Recall越高，说明有更多的正类样本被模型预测正确，模型的效果越好。
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Precision = TP / TP_add_FP # 表示在模型识别为正类的样本中，真正为正类的样本所占的比例。
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F1 = 2*(Precision*Recall)/(Precision+Recall)
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# recall= np.nanmean(Recall)
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# precision = np.nanmean(Precision)
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# f1= np.nanmean(F1)
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return Recall, Precision, F1
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def Pixel_Accuracy(self):
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Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()
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return Acc
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def Pixel_Accuracy_Class(self):
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Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1)
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Acc = np.nanmean(Acc)
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return Acc
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def Mean_Intersection_over_Union(self):
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class_IoU = np.diag(self.confusion_matrix) / (
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np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -
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np.diag(self.confusion_matrix))
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MIoU = np.nanmean(class_IoU)
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return class_IoU, MIoU
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def Frequency_Weighted_Intersection_over_Union(self):
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freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix)
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iu = np.diag(self.confusion_matrix) / (
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np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -
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np.diag(self.confusion_matrix))
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FWIoU = (freq[freq > 0] * iu[freq > 0]).sum()
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return FWIoU
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def _generate_matrix(self, gt_image, pre_image):
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mask = (gt_image >= 0) & (gt_image < self.num_class)
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label = self.num_class * gt_image[mask].astype('int') + pre_image[mask]
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count = np.bincount(label, minlength=self.num_class**2)
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confusion_matrix = count.reshape(self.num_class, self.num_class)
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return confusion_matrix
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def add_batch(self, gt_image, pre_image):
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assert gt_image.shape == pre_image.shape
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self.confusion_matrix += self._generate_matrix(gt_image, pre_image)
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def reset(self):
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# self.confusion_matrix = np.zeros((self.num_class,) * 2) # 原始
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self.confusion_matrix = np.zeros((self.num_class, self.num_class)) # 改动
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# Model validation metrics
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from . import general
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def fitness(x):
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# Model fitness as a weighted combination of metrics
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w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
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return (x[:, :4] * w).sum(1)
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def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
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""" Compute the average precision, given the recall and precision curves.
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Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
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# Arguments
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tp: True positives (nparray, nx1 or nx10).
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conf: Objectness value from 0-1 (nparray).
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pred_cls: Predicted object classes (nparray).
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target_cls: True object classes (nparray).
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plot: Plot precision-recall curve at mAP@0.5
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save_dir: Plot save directory
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# Sort by objectness
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i = np.argsort(-conf)
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tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
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# Find unique classes
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unique_classes = np.unique(target_cls)
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nc = unique_classes.shape[0] # number of classes, number of detections
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# Create Precision-Recall curve and compute AP for each class
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px, py = np.linspace(0, 1, 1000), [] # for plotting
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ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
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for ci, c in enumerate(unique_classes):
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i = pred_cls == c
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n_l = (target_cls == c).sum() # number of labels
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n_p = i.sum() # number of predictions
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if n_p == 0 or n_l == 0:
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continue
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else:
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# Accumulate FPs and TPs
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fpc = (1 - tp[i]).cumsum(0)
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tpc = tp[i].cumsum(0)
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# Recall
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recall = tpc / (n_l + 1e-16) # recall curve
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r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
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# Precision
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precision = tpc / (tpc + fpc) # precision curve
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p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
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# AP from recall-precision curve
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for j in range(tp.shape[1]):
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ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
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if plot and j == 0:
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py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
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# Compute F1 (harmonic mean of precision and recall)
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f1 = 2 * p * r / (p + r + 1e-16)
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if plot:
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plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
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plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
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plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
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plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
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i = f1.mean(0).argmax() # max F1 index
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return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
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def compute_ap(recall, precision):
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""" Compute the average precision, given the recall and precision curves
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# Arguments
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recall: The recall curve (list)
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precision: The precision curve (list)
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# Returns
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Average precision, precision curve, recall curve
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"""
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# Append sentinel values to beginning and end
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mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
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mpre = np.concatenate(([1.], precision, [0.]))
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# Compute the precision envelope
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mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
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# Integrate area under curve
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method = 'interp' # methods: 'continuous', 'interp'
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if method == 'interp':
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x = np.linspace(0, 1, 101) # 101-point interp (COCO)
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ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
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else: # 'continuous'
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i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
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ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
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return ap, mpre, mrec
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class ConfusionMatrix:
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# Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
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def __init__(self, nc, conf=0.25, iou_thres=0.45):
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self.matrix = np.zeros((nc + 1, nc + 1))
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self.nc = nc # number of classes
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self.conf = conf
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self.iou_thres = iou_thres
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def process_batch(self, detections, labels):
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"""
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Return intersection-over-union (Jaccard index) of boxes.
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Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
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Arguments:
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detections (Array[N, 6]), x1, y1, x2, y2, conf, class
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labels (Array[M, 5]), class, x1, y1, x2, y2
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Returns:
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None, updates confusion matrix accordingly
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"""
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detections = detections[detections[:, 4] > self.conf]
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gt_classes = labels[:, 0].int()
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detection_classes = detections[:, 5].int()
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iou = general.box_iou(labels[:, 1:], detections[:, :4])
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x = torch.where(iou > self.iou_thres)
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if x[0].shape[0]:
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matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
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if x[0].shape[0] > 1:
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
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matches = matches[matches[:, 2].argsort()[::-1]]
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matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
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else:
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matches = np.zeros((0, 3))
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n = matches.shape[0] > 0
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m0, m1, _ = matches.transpose().astype(np.int16)
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for i, gc in enumerate(gt_classes):
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j = m0 == i
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if n and sum(j) == 1:
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self.matrix[detection_classes[m1[j]], gc] += 1 # correct
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else:
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self.matrix[self.nc, gc] += 1 # background FP
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if n:
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for i, dc in enumerate(detection_classes):
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if not any(m1 == i):
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self.matrix[dc, self.nc] += 1 # background FN
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def matrix(self):
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return self.matrix
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def plot(self, save_dir='', names=()):
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try:
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import seaborn as sn
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array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize
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array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
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fig = plt.figure(figsize=(12, 9), tight_layout=True)
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sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size
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labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels
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sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
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xticklabels=names + ['background FP'] if labels else "auto",
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yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
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fig.axes[0].set_xlabel('True')
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fig.axes[0].set_ylabel('Predicted')
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fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
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except Exception as e:
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pass
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def print(self):
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for i in range(self.nc + 1):
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print(' '.join(map(str, self.matrix[i])))
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# Plots ----------------------------------------------------------------------------------------------------------------
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def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
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# Precision-recall curve
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
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py = np.stack(py, axis=1)
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if 0 < len(names) < 21: # display per-class legend if < 21 classes
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for i, y in enumerate(py.T):
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ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
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else:
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ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
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ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
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ax.set_xlabel('Recall')
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ax.set_ylabel('Precision')
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
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fig.savefig(Path(save_dir), dpi=250)
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def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
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# Metric-confidence curve
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fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
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if 0 < len(names) < 21: # display per-class legend if < 21 classes
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for i, y in enumerate(py):
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ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
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else:
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ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
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y = py.mean(0)
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ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
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ax.set_xlabel(xlabel)
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ax.set_ylabel(ylabel)
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ax.set_xlim(0, 1)
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ax.set_ylim(0, 1)
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plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
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fig.savefig(Path(save_dir), dpi=250)
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