pbCreator.py
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from __future__ import division, print_function
import tensorflow as tf
import numpy as np
import argparse
import cv2
from misc_utils import parse_anchors, read_class_names
from nms_utils import gpu_nms
from plot_utils import get_color_table, plot_one_box
from data_utils import letterbox_resize
from model import yolov3
parser = argparse.ArgumentParser(description="YOLO-V3 test single image test procedure.")
parser.add_argument("--input_image", type=str,
help="The path of the input image.")
parser.add_argument("--anchor_path", type=str, default="../../data/yolo_anchors.txt",
help="The path of the anchor txt file.")
parser.add_argument("--new_size", nargs='*', type=int, default=[416, 416],
help="Resize the input image with `new_size`, size format: [width, height]")
parser.add_argument("--letterbox_resize", type=lambda x: (str(x).lower() == 'true'), default=True,
help="Whether to use the letterbox resize.")
parser.add_argument("--class_name_path", type=str, default="../../data/classes.txt",
help="The path of the class names.")
parser.add_argument("--restore_path", type=str, default="../../data/darknet_weights/yolov3.ckpt",
help="The path of the weights to restore.")
parser.add_argument("--pb_path", type=str, default="../../data/pb",
help="The directory of pb files")
args = parser.parse_args()
args.anchors = parse_anchors(args.anchor_path)
args.classes = read_class_names(args.class_name_path)
args.num_class = len(args.classes)
color_table = get_color_table(args.num_class)
img_ori = cv2.imread(args.input_image)
if args.letterbox_resize:
img, resize_ratio, dw, dh = letterbox_resize(img_ori, args.new_size[0], args.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(args.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] / 255.
graph = tf.Graph()
with tf.Session(graph=graph) as sess:
input_data = tf.placeholder(tf.float32, [1, args.new_size[1], args.new_size[0], 3], name='input_data')
yolo_model = yolov3(args.num_class, args.anchors)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(input_data, False)
pred_boxes, pred_confs, pred_probs = yolo_model.predict(pred_feature_maps)
pred_scores = pred_confs * pred_probs
boxes, scores, labels = gpu_nms(pred_boxes, pred_scores, args.num_class, max_boxes=200, score_thresh=0.3, nms_thresh=0.45)
saver = tf.train.Saver()
saver.restore(sess, args.restore_path)
boxes_, scores_, labels_ = sess.run([boxes, scores, labels], feed_dict={input_data: img})
if args.letterbox_resize:
boxes_[:, [0, 2]] = (boxes_[:, [0, 2]] - dw) / resize_ratio
boxes_[:, [1, 3]] = (boxes_[:, [1, 3]] - dh) / resize_ratio
else:
boxes_[:, [0, 2]] *= (width_ori/float(args.new_size[0]))
boxes_[:, [1, 3]] *= (height_ori/float(args.new_size[1]))
print("box coords:")
print(boxes_)
print('*' * 30)
print("scores:")
print(scores_)
print('*' * 30)
print("labels:")
print(labels_)
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i]
plot_one_box(img_ori, [x0, y0, x1, y1], label=args.classes[labels_[i]] + ', {:.2f}%'.format(scores_[i] * 100), color=color_table[labels_[i]])
cv2.imshow('Detection result', img_ori)
cv2.imwrite('detection_result.jpg', img_ori)
cv2.waitKey(0)
saver.save(sess, args.pb_path+'/pb.ckpt')
tf.io.write_graph(sess.graph_def, args.pb_path, 'model.pb', as_text=False)
tf.io.write_graph(sess.graph_def, args.pb_path, 'model.pbtxt', as_text=True)