totalResultsFile.write("\npre nms:" + str(cfg.TEST.RPN_PRE_NMS_TOP_N))
totalResultsFile.write("\npost nms:" + str(cfg.TEST.RPN_POST_NMS_TOP_N))
totalResultsFile.write("\ntest lists:" + readlists)
for model_idx in xrange(iterStart, iterEnd, iterStep):
caffemodel = modelprefix + str(model_idx) + "0000.caffemodel"
writeFile = open(
toTxtDir + "/ret_" + caffemodel.split('.')[0] + ".txt", "w")
net = caffe.Net(prototxt, modelPath + caffemodel, caffe.TEST)
numFrame = 0
n_pos_gt = 0
n_pos_re = 0
n_pos_tp = 0
for filename in filelists:
picName = filename.strip().split(' ')[0].split('.')[0] + '.jpg'
nhand_gt, handcls_gt, box_gt = get_gt_from_xml(
from_xml_path + filename.strip().split(' ')[0].split('.')[0] +
'.xml')
n_pos_gt += nhand_gt
frame = cv2.imread(from_img_path + picName)
numFrame += 1
handcls, handbox, handscore = demo(net, frame)
nhand = handbox.shape[0]
n_pos_re += nhand
nTP = 0
if nhand > 0:
writeFile.write(picName + ' ' + str(nhand))
for i in xrange(0, nhand):
writeFile.write(' hand %d %d %d %d' %
(handbox[i][0], handbox[i][1],
handbox[i][2], handbox[i][3]))
if __name__ == '__main__':
speical_config()
filelists = readFile.readlines()
writeFile = open(to_xml_path+"../neg_"+ caffemodel.split('.')[-2].split('/')[-1]+".txt", "w")
net = caffe.Net(prototxt, model_path+caffemodel, caffe.TEST)
# print '\n\nLoaded network: {:s}'.format(caffemodel)
numFrame = 0
n_hand_gt = 0
n_false_null = 0
n_false_neg = 0
handcls_gt = np.array([], dtype = int)
box_gt = np.array([], dtype = int)
for line in filelists:
fileame = line.strip().split(' ')[0].split('.')[0]
xml_path = from_xml_path + fileame+ '.xml'
n_hand_gt, handcls_gt, box_gt = get_gt_from_xml( from_xml_path + fileame+ '.xml' )
frame = cv2.imread(from_img_path+fileame+'.jpg')
copyframe = frame.copy()
numFrame+=1
handcls, handbox, handscore = demo(net, frame, CONF_THRESH, NMS_THRESH)
nhand = handbox.shape[0]
nTP = 0
if nhand > 0:
for i in xrange(0, nhand):
writeFile.write(' hand %d %d %d %d'%(handbox[i][0],handbox[i][1],handbox[i][2],handbox[i][3]))
if (IOU_multi(handbox[i], box_gt) > iouN):
n_false_neg += 1
else:
for gt_idx in xrange(box_gt.shape[0]):
print box_gt[gt_idx]
cv2.rectangle(frame, (box_gt[gt_idx][0], box_gt[gt_idx][1]), (box_gt[gt_idx][2], box_gt[gt_idx][3]), (255, 0, 0), 4)