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detector.py
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import tensorflow as tf
import numpy as np
import args as cfg
import argparse
import cv2
import os
from utils.misc_utils import parse_anchors, read_class_names
from utils.nms_utils import gpu_nms
from utils.plot_utils import get_color_table, plot_one_box
from utils.data_aug import letterbox_resize
from time import localtime
from model import yolov3
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
from plate_rec import HyperLPR_plate_recognition
fontC = ImageFont.truetype('platech.ttf', 32, 0)
class detector(object):
def __init__(self):
self.new_size = cfg.img_size
self.resize = True
self.class_name_path = './data/mydata.names'
self.restore_path = cfg.restore_path
self.classes = read_class_names(self.class_name_path)
self.num_class = len(self.classes)
self.color_table = get_color_table(self.num_class)
self.sess = tf.Session()
self.init_params()
def get_time(self, name):
out = '{}年 {}月 {}日\n时间：{}\n识别车牌：{}\n'
t = localtime()
out = out.format(
t.tm_year, t.tm_mon, t.tm_mday,
str(t.tm_hour)+':'+str(t.tm_min)+':'+str(t.tm_sec),
name
)
return out
def drawTest(self, image, addText, x1, y1):
img = Image.fromarray(image)
draw = ImageDraw.Draw(img)
draw.text((x1, y1),
addText.encode("utf-8").decode("utf-8"),
(0, 200, 255), font=fontC)
imagex = np.array(img)
return imagex
def init_params(self):
self.input_data = tf.placeholder(
tf.float32, [1, self.new_size[1], self.new_size[0], 3], name='input_data')
self.yolo_model = yolov3(self.num_class, cfg.anchors)
with tf.variable_scope('yolov3'):
self.pred_feature_maps = self.yolo_model.forward(
self.input_data, False)
self.pred_boxes, self.pred_confs, self.pred_probs = self.yolo_model.predict(
self.pred_feature_maps)
self.pred_scores = self.pred_confs * self.pred_probs
self.boxes, self.scores, self.labels = gpu_nms(
self.pred_boxes, self.pred_scores, self.num_class, max_boxes=200, score_thresh=0.25, nms_thresh=0.45)
self.saver = tf.train.Saver()
self.saver.restore(self.sess, self.restore_path)
def demo(self, pt):
img_ori = cv2.imread(pt)
if self.resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, self. new_size[0], self.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(self.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] - 127.5
boxes_, scores_, labels_ = self.sess.run(
[self.boxes, self.scores, self.labels], feed_dict={self.input_data: img})
# rescale the coordinates to the original image
if 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(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori/float(new_size[1]))
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i].astype(np.int)
cv2.rectangle(img_ori, (x0, y0), (x1, y1), (0, 200, 255), 4)
res, con = HyperLPR_plate_recognition(img_ori, (x0, y0, x1, y1))
label = '置信度: {:.2f}%\n'.format(
scores_[i] * 100) + self.get_time(res)
img_ori = self.drawTest(img_ori, label, 10, 10)
cv2.imshow('result', img_ori)
cv2.waitKey(0)
def detect(self, img_ori):
if self.resize:
img, resize_ratio, dw, dh = letterbox_resize(
img_ori, self. new_size[0], self.new_size[1])
else:
height_ori, width_ori = img_ori.shape[:2]
img = cv2.resize(img_ori, tuple(self.new_size))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = np.asarray(img, np.float32)
img = img[np.newaxis, :] - 127.5
boxes_, scores_, labels_ = self.sess.run(
[self.boxes, self.scores, self.labels], feed_dict={self.input_data: img})
# rescale the coordinates to the original image
if 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(new_size[0]))
boxes_[:, [1, 3]] *= (height_ori/float(new_size[1]))
for i in range(len(boxes_)):
x0, y0, x1, y1 = boxes_[i].astype(np.int)
cv2.rectangle(img_ori, (x0, y0), (x1, y1), (0, 200, 255), 4)
res, con = HyperLPR_plate_recognition(img_ori, (x0, y0, x1, y1))
label = '置信度: {:.2f}%\n'.format(
scores_[i] * 100) + self.get_time(res)
img_ori = self.drawTest(img_ori, label, 10, 10)
return img_ori
if __name__ == '__main__':
det = detector()
test_path = './demo_images'
im_names = os.listdir(test_path)
for name in im_names:
pt = os.path.join(test_path, name)
det.demo(pt)
cv2.destroyAllWindows()