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不解释，原理很简单
code
from fast_rcnn.config import cfg, get_output_dir
import numpy as np
import cv2
import caffe
from fast_rcnn.nms_wrapper import nms, soft_nms
import cPickle
import os
def im_detect(net, im, targe_size):
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im = cv2.resize(im_orig, None, None, fx=float(targe_size)/float(im.shape[1]), fy=float(targe_size)/float(im.shape[0]), interpolation=cv2.INTER_LINEAR)
blob = np.zeros((1, targe_size, targe_size, 3), dtype=np.float32)
blob[0, :, :, :] = im
channel_swap = (0, 3, 1, 2)
blob = blob.transpose(channel_swap)
net.blobs['data'].reshape(1, 3, im.shape[0], im.shape[1])
net.blobs['data'].data[...] = blob
detections = net.forward()['detection_out']
det_label = detections[0, 0, :, 1]
det_conf = detections[0, 0, :, 2]
det_xmin = np.minimum(np.maximum(detections[0, 0, :, 3] * im_orig.shape[1], 0), im_orig.shape[1] - 1)
det_ymin = np.minimum(np.maximum(detections[0, 0, :, 4] * im_orig.shape[0], 0), im_orig.shape[0] - 1)
det_xmax = np.minimum(np.maximum(detections[0, 0, :, 5] * im_orig.shape[1], 0), im_orig.shape[1] - 1)
det_ymax = np.minimum(np.maximum(detections[0, 0, :, 6] * im_orig.shape[0], 0), im_orig.shape[0] - 1)
dets = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf, det_label))
keep_index = np.where(dets[:, 4] >= 0)[0]
dets = dets[keep_index, :]
return dets
def im_detect_ratio(net, im, targe_size1, targe_size2):
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
if im_orig.shape[0] < im_orig.shape[1]:
tmp = targe_size1
targe_size1 = targe_size2
targe_size2 = tmp
im = cv2.resize(im_orig, None, None, fx=float(targe_size2)/float(im.shape[1]), fy=float(targe_size1)/float(im.shape[0]), interpolation=cv2.INTER_LINEAR)
blob = np.zeros((1, int(targe_size1), int(targe_size2), 3), dtype=np.float32)
blob[0, :, :, :] = im
channel_swap = (0, 3, 1, 2)
blob = blob.transpose(channel_swap)
net.blobs['data'].reshape(1, 3, im.shape[0], im.shape[1])
net.blobs['data'].data[...] = blob
detections = net.forward()['detection_out']
det_label = detections[0, 0, :, 1]
det_conf = detections[0, 0, :, 2]
det_xmin = np.minimum(np.maximum(detections[0, 0, :, 3] * im_orig.shape[1], 0), im_orig.shape[1] - 1)
det_ymin = np.minimum(np.maximum(detections[0, 0, :, 4] * im_orig.shape[0], 0), im_orig.shape[0] - 1)
det_xmax = np.minimum(np.maximum(detections[0, 0, :, 5] * im_orig.shape[1], 0), im_orig.shape[1] - 1)
det_ymax = np.minimum(np.maximum(detections[0, 0, :, 6] * im_orig.shape[0], 0), im_orig.shape[0] - 1)
dets = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf, det_label))
keep_index = np.where(dets[:, 4] >= 0)[0]
dets = dets[keep_index, :]
return dets
def flip_im_detect(net, im, targe_size):
im_f = cv2.flip(im, 1)
det_f = im_detect(net, im_f, targe_size)
det_t = np.zeros(det_f.shape)
det_t[:, 0] = im.shape[1] - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = im.shape[1] - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
det_t[:, 5] = det_f[:, 5]
return det_t
def flip_im_detect_ratio(net, im, targe_size1, targe_size2):
im_f = cv2.flip(im, 1)
det_f = im_detect_ratio(net, im_f, targe_size1, targe_size2)
det_t = np.zeros(det_f.shape)
det_t[:, 0] = im.shape[1] - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = im.shape[1] - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
det_t[:, 5] = det_f[:, 5]
return det_t
def vis_detections(im, class_name, dets, thresh=0.1):
"""Visual debugging of detections."""
index = np.where(dets[:, -1] > thresh)[0]
if index.shape[0] == 0:
return
det = dets[index,:]
import matplotlib.pyplot as plt
im = im[:, :, (2, 1, 0)]
plt.cla()
plt.imshow(im)
for i in xrange(det.shape[0]):
bbox = det[i, :4]
score = det[i, -1]
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='g', linewidth=3)
)
plt.text(bbox[0], bbox[1], '{} {:.3f}'.format(class_name, score), color='r')
plt.show()
def bbox_vote(det):
if det.shape[0] <= 1:
return det
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
# det = det[np.where(det[:, 4] > 0.2)[0], :]
dets = []
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# get needed merge det and delete these det
merge_index = np.where(o >= 0.45)[0]
det_accu = det[merge_index, :]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
try:
dets = np.row_stack((dets, det_accu))
except:
dets = det_accu
continue
else:
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
return dets
def soft_bbox_vote(det):
if det.shape[0] <= 1:
return det
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
dets = []
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# get needed merge det and delete these det
merge_index = np.where(o >= 0.45)[0]
det_accu = det[merge_index, :]
det_accu_iou = o[merge_index]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
try:
dets = np.row_stack((dets, det_accu))
except:
dets = det_accu
continue
else:
soft_det_accu = det_accu.copy()
soft_det_accu[:, 4] = soft_det_accu[:, 4] * (1 - det_accu_iou)
soft_index = np.where(soft_det_accu[:, 4] >= cfg.confidence_threshold)[0]
soft_det_accu = soft_det_accu[soft_index, :]
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
if soft_det_accu.shape[0] > 0:
det_accu_sum = np.row_stack((soft_det_accu, det_accu_sum))
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
order = dets[:, 4].ravel().argsort()[::-1]
dets = dets[order, :]
return dets
def single_scale_test_net(net, imdb, targe_size=320, vis=False):
num_images = len(imdb.image_index)
all_boxes = [[[] for _ in xrange(num_images)] for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
for i in xrange(num_images):
im = cv2.imread(imdb.image_path_at(i))
det = im_detect(net, im, targe_size)
for j in xrange(1, imdb.num_classes):
inds = np.where(det[:, -1] == j)[0]
if inds.shape[0] > 0:
cls_dets = det[inds, :-1].astype(np.float32)
if 'coco' in imdb.name:
keep = soft_nms(cls_dets, sigma=0.5, Nt=0.30, threshold=cfg.confidence_threshold, method=1)
cls_dets = cls_dets[keep, :]
all_boxes[j][i] = cls_dets
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
print 'im_detect: {:d}/{:d}'.format(i + 1, num_images)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
if imdb.name == 'voc_2012_test':
print 'Saving detections'
imdb.config['use_salt'] = False
imdb._write_voc_results_file(all_boxes)
else:
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def multi_scale_test_net_320(net, imdb, vis=False):
targe_size = 320
num_images = len(imdb.image_index)
all_boxes = [[[] for _ in xrange(num_images)] for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
for i in xrange(num_images):
im = cv2.imread(imdb.image_path_at(i))
# ori and flip
det0 = im_detect(net, im, targe_size)
det0_f = flip_im_detect(net, im, targe_size)
det0 = np.row_stack((det0, det0_f))
det_r = im_detect_ratio(net, im, targe_size, int(0.6*targe_size))
det_r_f = flip_im_detect_ratio(net, im, targe_size, int(0.6*targe_size))
det_r = np.row_stack((det_r, det_r_f))
# shrink: only detect big object
det1 = im_detect(net, im, int(0.6*targe_size))
det1_f = flip_im_detect(net, im, int(0.6*targe_size))
det1 = np.row_stack((det1, det1_f))
index = np.where(np.maximum(det1[:, 2] - det1[:, 0] + 1, det1[:, 3] - det1[:, 1] + 1) > 32)[0]
det1 = det1[index, :]
#enlarge: only detect small object
det2 = im_detect(net, im, int(1.2*targe_size))
det2_f = flip_im_detect(net, im, int(1.2*targe_size))
det2 = np.row_stack((det2, det2_f))
index = np.where(np.minimum(det2[:, 2] - det2[:, 0] + 1, det2[:, 3] - det2[:, 1] + 1) < 160)[0]
det2 = det2[index, :]
det3 = im_detect(net, im, int(1.4*targe_size))
det3_f = flip_im_detect(net, im, int(1.4*targe_size))
det3 = np.row_stack((det3, det3_f))
index = np.where(np.minimum(det3[:, 2] - det3[:, 0] + 1, det3[:, 3] - det3[:, 1] + 1) < 128)[0]
det3 = det3[index, :]
det4 = im_detect(net, im, int(1.6*targe_size))
det4_f = flip_im_detect(net, im, int(1.6*targe_size))
det4 = np.row_stack((det4, det4_f))
index = np.where(np.minimum(det4[:, 2] - det4[:, 0] + 1, det4[:, 3] - det4[:, 1] + 1) < 96)[0]
det4 = det4[index, :]
det5 = im_detect(net, im, int(1.8*targe_size))
det5_f = flip_im_detect(net, im, int(1.8*targe_size))
det5 = np.row_stack((det5, det5_f))
index = np.where(np.minimum(det5[:, 2] - det5[:, 0] + 1, det5[:, 3] - det5[:, 1] + 1) < 64)[0]
det5 = det5[index, :]
det7 = im_detect(net, im, int(2.2*targe_size))
det7_f = flip_im_detect(net, im, int(2.2*targe_size))
det7 = np.row_stack((det7, det7_f))
index = np.where(np.minimum(det7[:, 2] - det7[:, 0] + 1, det7[:, 3] - det7[:, 1] + 1) < 32)[0]
det7 = det7[index, :]
# More scales make coco get better performance
if 'coco' in imdb.name:
det6 = im_detect(net, im, int(2.0*targe_size))
det6_f = flip_im_detect(net, im, int(2.0*targe_size))
det6 = np.row_stack((det6, det6_f))
index = np.where(np.minimum(det6[:, 2] - det6[:, 0] + 1, det6[:, 3] - det6[:, 1] + 1) < 48)[0]
det6 = det6[index, :]
det = np.row_stack((det0, det_r, det1, det2, det3, det4, det5, det7, det6))
else:
det = np.row_stack((det0, det_r, det1, det2, det3, det4, det5, det7))
for j in xrange(1, imdb.num_classes):
inds = np.where(det[:, -1] == j)[0]
if inds.shape[0] > 0:
cls_dets = det[inds, :-1].astype(np.float32)
if 'coco' in imdb.name:
cls_dets = soft_bbox_vote(cls_dets)
else:
cls_dets = bbox_vote(cls_dets)
all_boxes[j][i] = cls_dets
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
print 'im_detect: {:d}/{:d}'.format(i + 1, num_images)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
if imdb.name == 'voc_2012_test':
print 'Saving detections'
imdb.config['use_salt'] = False
imdb._write_voc_results_file(all_boxes)
else:
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def multi_scale_test_net_512(net, imdb, vis=False):
targe_size = 512
num_images = len(imdb.image_index)
all_boxes = [[[] for _ in xrange(num_images)] for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
for i in xrange(num_images):
im = cv2.imread(imdb.image_path_at(i))
# ori and flip
det0 = im_detect(net, im, targe_size)
det0_f = flip_im_detect(net, im, targe_size)
det0 = np.row_stack((det0, det0_f))
det_r = im_detect_ratio(net, im, targe_size, int(0.75*targe_size))
det_r_f = flip_im_detect_ratio(net, im, targe_size, int(0.75*targe_size))
det_r = np.row_stack((det_r, det_r_f))
# shrink: only detect big object
det_s1 = im_detect(net, im, int(0.5*targe_size))
det_s1_f = flip_im_detect(net, im, int(0.5*targe_size))
det_s1 = np.row_stack((det_s1, det_s1_f))
det_s2 = im_detect(net, im, int(0.75*targe_size))
det_s2_f = flip_im_detect(net, im, int(0.75*targe_size))
det_s2 = np.row_stack((det_s2, det_s2_f))
# #enlarge: only detect small object
det3 = im_detect(net, im, int(1.75*targe_size))
det3_f = flip_im_detect(net, im, int(1.75*targe_size))
det3 = np.row_stack((det3, det3_f))
index = np.where(np.minimum(det3[:, 2] - det3[:, 0] + 1, det3[:, 3] - det3[:, 1] + 1) < 128)[0]
det3 = det3[index, :]
det4 = im_detect(net, im, int(1.5*targe_size))
det4_f = flip_im_detect(net, im, int(1.5*targe_size))
det4 = np.row_stack((det4, det4_f))
index = np.where(np.minimum(det4[:, 2] - det4[:, 0] + 1, det4[:, 3] - det4[:, 1] + 1) < 192)[0]
det4 = det4[index, :]
# More scales make coco get better performance
if 'coco' in imdb.name:
det5 = im_detect(net, im, int(1.25*targe_size))
det5_f = flip_im_detect(net, im, int(1.25*targe_size))
det5 = np.row_stack((det5, det5_f))
index = np.where(np.minimum(det5[:, 2] - det5[:, 0] + 1, det5[:, 3] - det5[:, 1] + 1) < 224)[0]
det5 = det5[index, :]
det6 = im_detect(net, im, int(2*targe_size))
det6_f = flip_im_detect(net, im, int(2*targe_size))
det6 = np.row_stack((det6, det6_f))
index = np.where(np.minimum(det6[:, 2] - det6[:, 0] + 1, det6[:, 3] - det6[:, 1] + 1) < 96)[0]
det6 = det6[index, :]
det7 = im_detect(net, im, int(2.25*targe_size))
det7_f = flip_im_detect(net, im, int(2.25*targe_size))
det7 = np.row_stack((det7, det7_f))
index = np.where(np.minimum(det7[:, 2] - det7[:, 0] + 1, det7[:, 3] - det7[:, 1] + 1) < 64)[0]
det7 = det7[index, :]
det = np.row_stack((det0, det_r, det_s1, det_s2, det3, det4, det5, det6, det7))
else:
det = np.row_stack((det0, det_r, det_s1, det_s2, det3, det4))
for j in xrange(1, imdb.num_classes):
inds = np.where(det[:, -1] == j)[0]
if inds.shape[0] > 0:
cls_dets = det[inds, :-1].astype(np.float32)
if 'coco' in imdb.name:
cls_dets = soft_bbox_vote(cls_dets)
else:
cls_dets = bbox_vote(cls_dets)
all_boxes[j][i] = cls_dets
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
print 'im_detect: {:d}/{:d}'.format(i + 1, num_images)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
if imdb.name == 'voc_2012_test':
print 'Saving detections'
imdb.config['use_salt'] = False
imdb._write_voc_results_file(all_boxes)
else:
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)