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predict_frcnn_kitti.py
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from __future__ import division
import os
import cv2
import numpy as np
import pickle
import time
from keras_frcnn import config
from keras import backend as K
from keras.layers import Input
from keras.models import Model
from keras_frcnn import roi_helpers
import argparse
import os
import keras_frcnn.resnet as nn
from keras_frcnn.visualize import draw_boxes_and_label_on_image_cv2
def format_img_size(img, cfg):
"""Formats the image size based on config"""
img_min_side = float(cfg.im_size)
(height, width, _) = img.shape
if width <= height:
ratio = img_min_side / width
new_height = int(ratio * height)
new_width = int(img_min_side)
else:
ratio = img_min_side / height
new_width = int(ratio * width)
new_height = int(img_min_side)
img = cv2.resize(img, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
return img, ratio
def format_img_channels(img, cfg):
"""Format the image channels based on config"""
img = img[:, :, (2, 1, 0)]
img = img.astype(np.float32)
img[:, :, 0] -= cfg.img_channel_mean[0]
img[:, :, 1] -= cfg.img_channel_mean[1]
img[:, :, 2] -= cfg.img_channel_mean[2]
img /= cfg.img_scaling_factor
img = np.transpose(img, (2, 0, 1))
img = np.expand_dims(img, axis=0)
return img
def format_img(img, C):
"""Format an image for model prediction based on config"""
img, ratio = format_img_size(img, C)
img = format_img_channels(img, C)
return img, ratio
# Define the method to transform the coordinates of the bounding box to its original size
def get_real_coordinates(ratio, x1, y1, x2, y2):
real_x1 = int(round(x1 // ratio))
real_y1 = int(round(y1 // ratio))
real_x2 = int(round(x2 // ratio))
real_y2 = int(round(y2 // ratio))
return real_x1, real_y1, real_x2, real_y2
def predict_single_image(img_path, model_rpn, model_classifier_only, cfg, class_mapping):
st = time.time()
img = cv2.imread(img_path)
if img is None:
print('reading image failed.')
exit(0)
X, ratio = format_img(img, cfg)
# -if K.image_dim_ordering() == 'tf':
if K.image_data_format() == 'channels last':
X = np.transpose(X, (0, 2, 3, 1))
# Get the feature maps and output from the RPN
[Y1, Y2, F] = model_rpn.predict(X)
# This is result contains all boxes, which is [x1, y1, x2, y2]
# -result = roi_helpers.rpn_to_roi(Y1, Y2, cfg, K.image_dim_ordering(), overlap_thresh=0.7)
result = roi_helpers.rpn_to_roi(Y1, Y2, cfg, K.image_data_format(), overlap_thresh=0.7)
# Convert from (x1,y1,x2,y2) to (x,y,w,h)
result[:, 2] -= result[:, 0]
result[:, 3] -= result[:, 1]
bbox_threshold = 0.8
# Apply the spatial pyramid pooling to the proposed regions
boxes = dict()
for jk in range(result.shape[0] // cfg.num_rois + 1):
rois = np.expand_dims(result[cfg.num_rois * jk:cfg.num_rois * (jk + 1), :], axis=0)
if rois.shape[1] == 0:
break
if jk == result.shape[0] // cfg.num_rois:
# pad R
curr_shape = rois.shape
target_shape = (curr_shape[0], cfg.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.max(p_cls[0, ii, :]) < bbox_threshold or np.argmax(p_cls[0, ii, :]) == (p_cls.shape[2] - 1):
continue
cls_num = np.argmax(p_cls[0, ii, :])
if cls_num not in boxes.keys():
boxes[cls_num] = []
(x, y, w, h) = rois[0, ii, :]
try:
(tx, ty, tw, th) = p_regr[0, ii, 4 * cls_num:4 * (cls_num + 1)]
tx /= cfg.classifier_regr_std[0]
ty /= cfg.classifier_regr_std[1]
tw /= cfg.classifier_regr_std[2]
th /= cfg.classifier_regr_std[3]
x, y, w, h = roi_helpers.apply_regr(x, y, w, h, tx, ty, tw, th)
except Exception as e:
print(e)
pass
boxes[cls_num].append(
[cfg.rpn_stride * x, cfg.rpn_stride * y, cfg.rpn_stride * (x + w), cfg.rpn_stride * (y + h),
np.max(p_cls[0, ii, :])])
# Add some nms to reduce many boxes
for cls_num, box in boxes.items():
boxes_nms = roi_helpers.non_max_suppression_fast(box, overlap_thresh=0.5)
boxes[cls_num] = boxes_nms
print(class_mapping[cls_num] + ":")
for b in boxes_nms:
b[0], b[1], b[2], b[3] = get_real_coordinates(ratio, b[0], b[1], b[2], b[3])
print('{} prob: {}'.format(b[0: 4], b[-1]))
img = draw_boxes_and_label_on_image_cv2(img, class_mapping, boxes)
print('Elapsed time = {}'.format(time.time() - st))
cv2.imshow('image', img)
result_path = './results_images/{}.png'.format(os.path.basename(img_path).split('.')[0])
print('result saved into ', result_path)
cv2.imwrite(result_path, img)
cv2.waitKey(0)
def predict(args_):
path = args_.path
with open('config.pickle', 'rb') as f_in:
cfg = pickle.load(f_in)
cfg.use_horizontal_flips = False
cfg.use_vertical_flips = False
cfg.rot_90 = False
class_mapping = cfg.class_mapping
if 'bg' not in class_mapping:
class_mapping['bg'] = len(class_mapping)
class_mapping = {v: k for k, v in class_mapping.items()}
input_shape_img = (None, None, 3)
input_shape_features = (None, None, 1024)
img_input = Input(shape=input_shape_img)
roi_input = Input(shape=(cfg.num_rois, 4))
feature_map_input = Input(shape=input_shape_features)
shared_layers = nn.nn_base(img_input, trainable=True)
# define the RPN, built on the base layers
num_anchors = len(cfg.anchor_box_scales) * len(cfg.anchor_box_ratios)
rpn_layers = nn.rpn(shared_layers, num_anchors)
classifier = nn.classifier(feature_map_input, roi_input, cfg.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)
print('Loading weights from {}'.format(cfg.model_path))
model_rpn.load_weights(cfg.model_path, by_name=True)
model_classifier.load_weights(cfg.model_path, by_name=True)
model_rpn.compile(optimizer='sgd', loss='mse')
model_classifier.compile(optimizer='sgd', loss='mse')
if os.path.isdir(path):
for idx, img_name in enumerate(sorted(os.listdir(path))):
if not img_name.lower().endswith(('.bmp', '.jpeg', '.jpg', '.png', '.tif', '.tiff')):
continue
print(img_name)
predict_single_image(os.path.join(path, img_name), model_rpn,
model_classifier_only, cfg, class_mapping)
elif os.path.isfile(path):
print('predict image from {}'.format(path))
predict_single_image(path, model_rpn, model_classifier_only, cfg, class_mapping)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--path', '-p', default='images/000010.png', help='image path')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
predict(args)