import os
import cv2
import numpy as np
import sys
import pickle
from optparse import OptionParser
import time
from keras_frcnn import config
import keras_frcnn.resnet as nn
from keras import backend as K
from keras.layers import Input
from keras.models import Model
from keras_frcnn import roi_helpers
from keras_frcnn import data_generators
from sklearn.metrics import average_precision_score
def get_map(pred, gt, f):
T = {}
P = {}
fx, fy = f
for bbox in gt:
bbox['bbox_matched'] = False
pred_probs = np.array([s['prob'] for s in pred])
box_idx_sorted_by_prob = np.argsort(pred_probs)[::-1]
for box_idx in box_idx_sorted_by_prob:
pred_box = pred[box_idx]
pred_class = pred_box['class']
pred_x1 = pred_box['x1']
pred_x2 = pred_box['x2']
pred_y1 = pred_box['y1']
pred_y2 = pred_box['y2']
pred_prob = pred_box['prob']
if pred_class not in P:
P[pred_class] = []
T[pred_class] = []
P[pred_class].append(pred_prob)
found_match = False
for gt_box in gt:
gt_class = gt_box['class']
gt_x1 = gt_box['x1']/fx
gt_x2 = gt_box['x2']/fx
gt_y1 = gt_box['y1']/fy
gt_y2 = gt_box['y2']/fy
gt_seen = gt_box['bbox_matched']
if gt_class != pred_class:
continue
if gt_seen:
continue
iou = data_generators.iou((pred_x1, pred_y1, pred_x2, pred_y2), (gt_x1, gt_y1, gt_x2, gt_y2))
if iou >= 0.5:
found_match = True
gt_box['bbox_matched'] = True
break
else:
continue
T[pred_class].append(int(found_match))
for gt_box in gt:
if not gt_box['bbox_matched'] and not gt_box['difficult']:
if gt_box['class'] not in P:
P[gt_box['class']] = []
T[gt_box['class']] = []
T[gt_box['class']].append(1)
P[gt_box['class']].append(0)
#import pdb
#pdb.set_trace()
return T, P
sys.setrecursionlimit(40000)
parser = OptionParser()
parser.add_option("-p", "--path", dest="test_path", help="Path to test data.")
parser.add_option("-n", "--num_rois", dest="num_rois",
help="Number of ROIs per iteration. Higher means more memory use.", default=32)
parser.add_option("--config_filename", dest="config_filename", help=
"Location to read the metadata related to the training (generated when training).",
default="config.pickle")
parser.add_option("-o", "--parser", dest="parser", help="Parser to use. One of simple or pascal_voc",
default="pascal_voc"),
(options, args) = parser.parse_args()
if not options.test_path: # if filename is not given
parser.error('Error: path to test data must be specified. Pass --path to command line')
if options.parser == 'pascal_voc':
from keras_frcnn.pascal_voc_parser import get_data
elif options.parser == 'simple':
from keras_frcnn.simple_parser import get_data
else:
raise ValueError("Command line option parser must be one of 'pascal_voc' or 'simple'")
config_output_filename = options.config_filename
with open(config_output_filename, 'r') as f_in:
C = pickle.load(f_in)
# turn off any data augmentation at test time
C.use_horizontal_flips = False
C.use_vertical_flips = False
C.rot_90 = False
img_path = options.test_path
def format_img(img, C):
img_min_side = float(C.im_size)
(height,width,_) = img.shape
if width <= height:
f = img_min_side/width
new_height = int(f * height)
new_width = int(img_min_side)
else:
f = img_min_side/height
new_width = int(f * width)
new_height = int(img_min_side)
fx = width/float(new_width)
fy = height/float(new_height)
img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
img = img[:, :, (2, 1, 0)]
img = img.astype(np.float32)
img[:, :, 0] -= C.img_channel_mean[0]
img[:, :, 1] -= C.img_channel_mean[1]
img[:, :, 2] -= C.img_channel_mean[2]
img /= C.img_scaling_factor
img = np.transpose(img, (2, 0, 1))
img = np.expand_dims(img, axis=0)
return img, fx, fy
class_mapping = C.class_mapping
if 'bg' not in class_mapping:
class_mapping['bg'] = len(class_mapping)
class_mapping = {v: k for k, v in class_mapping.iteritems()}
print(class_mapping)
class_to_color = {class_mapping[v]: np.random.randint(0, 255, 3) for v in class_mapping}
C.num_rois = int(options.num_rois)
if K.image_dim_ordering() == 'th':
input_shape_img = (3, None, None)
input_shape_features = (1024, None, None)
else:
input_shape_img = (None, None, 3)
input_shape_features = (None, None, 1024)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(C.num_rois, 4))
feature_map_input = Input(shape=input_shape_features)
# define the base network (resnet here, can be VGG, Inception, etc)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(C.anchor_box_scales) * len(C.anchor_box_ratios)
rpn_layers = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(feature_map_input, roi_input, C.num_rois, nb_classes=len(class_mapping), trainable=True)
model_rpn = Model(img_input, rpn_layers)
model_classifier_only = Model([feature_map_input, roi_input], classifier)
model_classifier = Model([feature_map_input, roi_input], classifier)
model_rpn.load_weights(C.model_path, by_name=True)
model_classifier.load_weights(C.model_path, by_name=True)
model_rpn.compile(optimizer='sgd', loss='mse')
model_classifier.compile(optimizer='sgd', loss='mse')
all_imgs, _, _ = get_data(options.test_path)
test_imgs = [s for s in all_imgs if s['imageset'] == 'test']
T = {}
P = {}
for idx, img_data in enumerate(test_imgs):
print('{}/{}'.format(idx,len(test_imgs)))
st = time.time()
filepath = img_data['filepath']
img = cv2.imread(filepath)
X, fx, fy = format_img(img, C)
if K.image_dim_ordering() == 'tf':
X = np.transpose(X, (0, 2, 3, 1))
# get the feature maps and output from the RPN
[Y1, Y2, F] = model_rpn.predict(X)
R = roi_helpers.rpn_to_roi(Y1, Y2, C, K.image_dim_ordering(), overlap_thresh=0.7)
# convert from (x1,y1,x2,y2) to (x,y,w,h)
R[:, 2] -= R[:, 0]
R[:, 3] -= R[:, 1]
# apply the spatial pyramid pooling to the proposed regions
bboxes = {}
probs = {}
for jk in range(R.shape[0] // C.num_rois + 1):
ROIs = np.expand_dims(R[C.num_rois * jk:C.num_rois * (jk + 1), :], axis=0)
if ROIs.shape[1] == 0:
break
if jk == R.shape[0] // C.num_rois:
# pad R
curr_shape = ROIs.shape
target_shape = (curr_shape[0], C.num_rois, curr_shape[2])
ROIs_padded = np.zeros(target_shape).astype(ROIs.dtype)
ROIs_padded[:, :curr_shape[1], :] = ROIs
ROIs_padded[0, curr_shape[1]:, :] = ROIs[0, 0, :]
ROIs = ROIs_padded
[P_cls, P_regr] = model_classifier_only.predict([F, ROIs])
for ii in range(P_cls.shape[1]):
if np.argmax(P_cls[0, ii, :]) == (P_cls.shape[2] - 1):
continue
cls_name = class_mapping[np.argmax(P_cls[0, ii, :])]
if cls_name not in bboxes:
bboxes[cls_name] = []
probs[cls_name] = []
(x, y, w, h) = ROIs[0, ii, :]
cls_num = np.argmax(P_cls[0, ii, :])
try:
(tx, ty, tw, th) = P_regr[0, ii, 4 * cls_num:4 * (cls_num + 1)]
tx /= C.classifier_regr_std[0]
ty /= C.classifier_regr_std[1]
tw /= C.classifier_regr_std[2]
th /= C.classifier_regr_std[3]
x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)
except:
pass
bboxes[cls_name].append([16 * x, 16 * y, 16 * (x + w), 16 * (y + h)])
probs[cls_name].append(np.max(P_cls[0, ii, :]))
all_dets = []
for key in bboxes:
bbox = np.array(bboxes[key])
new_boxes, new_probs = roi_helpers.non_max_suppression_fast(bbox, np.array(probs[key]), overlap_thresh=0.5)
for jk in range(new_boxes.shape[0]):
(x1, y1, x2, y2) = new_boxes[jk, :]
det = {'x1': x1, 'x2': x2, 'y1': y1, 'y2': y2, 'class': key, 'prob': new_probs[jk]}
all_dets.append(det)
print('Elapsed time = {}'.format(time.time() - st))
t, p = get_map(all_dets, img_data['bboxes'], (fx, fy))
for key in t.keys():
if key not in T:
T[key] = []
P[key] = []
T[key].extend(t[key])
P[key].extend(p[key])
all_aps = []
for key in T.keys():
ap = average_precision_score(T[key], P[key])
print('{} AP: {}'.format(key, ap))
all_aps.append(ap)
print('mAP = {}'.format(np.mean(np.array(all_aps))))
#print(T)
#print(P)
