import os, torch, cv2, math, tqdm, time, shutil, argparse, json, pickle
import numpy as np
from prettytable import PrettyTable
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
# Rescale boxes (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
def box_iou(box1, box2, eps=1e-7):
"""
Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
Args:
box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes.
box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
Returns:
(torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2.
"""
# NOTE: Need .float() to get accurate iou values
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
(a1, a2), (b1, b2) = box1.float().unsqueeze(1).chunk(2, 2), box2.float().unsqueeze(0).chunk(2, 2)
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp_(0).prod(2)
# IoU = inter / (area1 + area2 - inter)
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def process_batch(detections, labels, iouv):
"""
Return correct prediction matrix
Arguments:
detections (array[N, 6]), x1, y1, x2, y2, conf, class
labels (array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (array[N, 10]), for 10 IoU levels
"""
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
iou = box_iou(labels[:, 1:], detections[:, :4])
correct_class = labels[:, 0:1] == detections[:, 5]
for i in range(len(iouv)):
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
def smooth(y, f=0.05):
# Box filter of fraction f
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
p = np.ones(nf // 2) # ones padding
yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (nparray, nx1 or nx10).
conf: Objectness value from 0-1 (nparray).
pred_cls: Predicted object classes (nparray).
target_cls: True object classes (nparray).
plot: Plot precision-recall curve at mAP@0.5
save_dir: Plot save directory
# Returns
The average precision as computed in py-faster-rcnn.
"""
# Sort by objectness
i = np.argsort(-conf)
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
# Find unique classes
unique_classes, nt = np.unique(target_cls, return_counts=True)
nc = unique_classes.shape[0] # number of classes, number of detections
# Create Precision-Recall curve and compute AP for each class
px, py = np.linspace(0, 1, 1000), [] # for plotting
ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
for ci, c in enumerate(unique_classes):
i = pred_cls == c
n_l = nt[ci] # number of labels
n_p = i.sum() # number of predictions
if n_p == 0 or n_l == 0:
continue
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum(0)
tpc = tp[i].cumsum(0)
# Recall
recall = tpc / (n_l + eps) # recall curve
r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
# Precision
precision = tpc / (tpc + fpc) # precision curve
p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
# AP from recall-precision curve
for j in range(tp.shape[1]):
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
if plot and j == 0:
py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
# Compute F1 (harmonic mean of precision and recall)
f1 = 2 * p * r / (p + r + eps)
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p[:, i], r[:, i], f1[:, i]
tp = (r * nt).round() # true positives
fp = (tp / (p + eps) - tp).round() # false positives
return tp, fp, p, r, f1, ap, unique_classes.astype(int)
def compute_ap(recall, precision):
""" Compute the average precision, given the recall and precision curves
# Arguments
recall: The recall curve (list)
precision: The precision curve (list)
# Returns
Average precision, precision curve, recall curve
"""
# Append sentinel values to beginning and end
mrec = np.concatenate(([0.0], recall, [1.0]))
mpre = np.concatenate(([1.0], precision, [0.0]))
# Compute the precision envelope
mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
# Integrate area under curve
method = 'interp' # methods: 'continuous', 'interp'
if method == 'interp':
x = np.linspace(0, 1, 101) # 101-point interp (COCO)
ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
else: # 'continuous'
i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
return ap, mpre, mrec
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--label_coco', type=str, default='/root/data/coco-test/annotations/test.json', help='label coco path')
parser.add_argument('--pred_coco', type=str, default='/root/mmdetection/detect_result/cascade-renn-new.pkl', help='pred coco path')
# parser.add_argument('--pred_coco', type=str, default='/home/hjj/Desktop/github_code/mmdetection-visdrone/work_dirs/dino-4scale_r50_8xb2-12e_visdrone/test/prediction.pickle', help='pred coco path')
parser.add_argument('--iou', type=float, default=0.7, help='iou threshold')
parser.add_argument('--conf', type=float, default=0.001, help='conf threshold')
opt = parser.parse_known_args()[0]
return opt
if __name__ == '__main__':
opt = parse_opt()
iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
niou = iouv.numel()
stats = []
label_coco_json_path, pred_coco_json_path = opt.label_coco, opt.pred_coco
with open(label_coco_json_path) as f:
label = json.load(f)
classes = []
for data in label['categories']:
classes.append(data['name'])
image_id_hw_dict = {}
for data in label['images']:
image_id_hw_dict[data['id']] = [data['height'], data['width']]
label_id_dict = {}
for data in tqdm.tqdm(label['annotations'], desc='Process label...'):
if data['image_id'] not in label_id_dict:
label_id_dict[data['image_id']] = []
category_id = data['category_id']
x_min, y_min, w, h = data['bbox'][0], data['bbox'][1], data['bbox'][2], data['bbox'][3]
x_max, y_max = x_min + w, y_min + h
label_id_dict[data['image_id']].append(np.array([int(category_id), x_min, y_min, x_max, y_max]))
if pred_coco_json_path.endswith('json'):
with open(pred_coco_json_path) as f:
pred = json.load(f)
pred_id_dict = {}
for data in tqdm.tqdm(pred, desc='Process pred...'):
if data['image_id'] not in pred_id_dict:
pred_id_dict[data['image_id']] = []
score = data['score']
category_id = data['category_id']
x_min, y_min, w, h = data['bbox'][0], data['bbox'][1], data['bbox'][2], data['bbox'][3]
x_max, y_max = x_min + w, y_min + h
pred_id_dict[data['image_id']].append(np.array([x_min, y_min, x_max, y_max, float(score), int(category_id)]))
else:
with open(pred_coco_json_path, 'rb') as f:
pred = pickle.load(f)
pred_id_dict = {}
for data in tqdm.tqdm(pred, desc='Process pred...'):
image_id = os.path.splitext(os.path.basename(data['img_path']))[0]
if image_id not in pred_id_dict:
pred_id_dict[image_id] = []
for i in range(data['pred_instances']['labels'].size(0)):
score = data['pred_instances']['scores'][i]
category_id = data['pred_instances']['labels'][i]
bboxes = data['pred_instances']['bboxes'][i]
x_min, y_min, x_max, y_max = bboxes.cpu().detach().numpy()
# x_min, x_max = x_min / data['scale_factor'][0], x_max / data['scale_factor'][0]
# y_min, y_max = y_min / data['scale_factor'][1], y_max / data['scale_factor'][1]
pred_id_dict[image_id].append(np.array([x_min, y_min, x_max, y_max, float(score), int(category_id)]))
for idx, image_id in enumerate(tqdm.tqdm(list(image_id_hw_dict.keys()), desc="Cal mAP...")):
label = np.array(label_id_dict[image_id])
if image_id not in pred_id_dict:
pred = np.empty((0, 6))
else:
pred = torch.from_numpy(np.array(pred_id_dict[image_id]))
nl, npr = label.shape[0], pred.shape[0]
correct = torch.zeros(npr, niou, dtype=torch.bool)
if npr == 0:
if nl:
stats.append((correct, *torch.zeros((2, 0)), torch.from_numpy(label[:, 0])))
continue
if nl:
correct = process_batch(pred, torch.from_numpy(label), iouv)
stats.append((correct, pred[:, 4], pred[:, 5], torch.from_numpy(label[:, 0])))
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)]
tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats)
print(f'precision:{p}')
print(f'recall:{r}')
print(f'mAP@0.5:{ap[:, 0]}')
# 计算 mAP@0.75
map75_index = int((0.75 - 0.5) / 0.05)
map75 = ap[:, map75_index]
print(f'mAP@0.75:{map75}')
table = PrettyTable()
table.title = f"Metrice"
table.field_names = ["Classes", 'Precision', 'Recall', 'mAP50', 'mAP50-95', 'mAP75']
table.add_row(['all', f'{np.mean(p):.3f}', f'{np.mean(r):.3f}', f'{np.mean(ap[:, 0]):.3f}', f'{np.mean(ap):.3f}', f'{np.mean(map75):.3f}'])
for cls_idx, classes in enumerate(classes):
table.add_row([classes, f'{p[cls_idx]:.3f}', f'{r[cls_idx]:.3f}', f'{ap[cls_idx, 0]:.3f}', f'{ap[cls_idx, :].mean():.3f}', f'{map75[cls_idx]:.3f}'])
print(table)
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