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yolo.py
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yolo.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
Run a YOLO_v3 style detection model on test images.
"""
import colorsys
import os
import random
from timeit import time
from timeit import default_timer as timer ### to calculate FPS
import cv2
import numpy as np
from keras import backend as K
from keras.models import load_model
from PIL import Image, ImageFont, ImageDraw
from yolo3.model import yolo_eval
from yolo3.utils import letterbox_image
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input",help="path to input video", default = "../video-image/car_over_pass1.mov")
ap.add_argument("-c", "--class",help="name of class", default = "car")
args = vars(ap.parse_args())
class YOLO(object):
def __init__(self):
self.model_path = 'model_data/yolo.h5' # 원래 yolo.h5
self.anchors_path = 'model_data/yolo_anchors.txt'
self.classes_path = 'model_data/coco_classes.txt'
#具体参数可实验后进行调整
if args["class"] == 'person':
self.score = 0.6 #0.8
self.iou = 0.6
self.model_image_size = (416,416)
if args["class"] == 'car':
self.score = 0.6
self.iou = 0.6
self.model_image_size = (416, 416)
if args["class"] == 'bicycle' or args["class"] == 'motorcycle':
self.score = 0.6
self.iou = 0.6
self.model_image_size = (416, 416)
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
#self.model_image_size = (416, 416) # fixed size or (None, None) small targets:(320,320) mid targets:(960,960)
self.is_fixed_size = self.model_image_size != (None, None)
self.boxes, self.scores, self.classes = self.generate()
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
#print(class_names)
return class_names
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
anchors = np.array(anchors).reshape(-1, 2)
return anchors
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith('.h5'), 'Keras model must be a .h5 file.'
self.yolo_model = load_model(model_path, compile=False)
print('{} model, anchors, and classes loaded.'.format(model_path))
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
random.seed(10101) # Fixed seed for consistent colors across runs.
random.shuffle(self.colors) # Shuffle colors to decorrelate adjacent classes.
random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(self.yolo_model.output, self.anchors,
len(self.class_names), self.input_image_shape,
score_threshold=self.score, iou_threshold=self.iou)
return boxes, scores, classes
def detect_image(self, image):
if self.is_fixed_size:
assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
boxed_image = letterbox_image(image, tuple(reversed(self.model_image_size)))
else:
new_image_size = (image.width - (image.width % 32),
image.height - (image.height % 32))
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype='float32')
#print(image_data.shape)
image_data /= 255.
image_data = np.expand_dims(image_data, 0) # Add batch dimension.
out_boxes, out_scores, out_classes = self.sess.run(
[self.boxes, self.scores, self.classes],
feed_dict={
self.yolo_model.input: image_data,
self.input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
return_boxs = []
return_class_name = []
person_counter = 0
for i, c in reversed(list(enumerate(out_classes))):
predicted_class = self.class_names[c]
#print(self.class_names[c])
'''
if predicted_class != 'person' and predicted_class != 'car':
print(predicted_class)
continue
'''
if predicted_class != args["class"]:
#print(predicted_class)
continue
person_counter += 1
#if predicted_class != 'car':
#continue
#label = predicted_class
box = out_boxes[i]
#score = out_scores[i]
x = int(box[1])
y = int(box[0])
w = int(box[3]-box[1])
h = int(box[2]-box[0])
if x < 0 :
w = w + x
x = 0
if y < 0 :
h = h + y
y = 0
return_boxs.append([x,y,w,h])
# print(return_boxs)
return_class_name.append([predicted_class])
# print(return_class_name)
# cv2.putText(image, str(self.class_names[c]),(int(box[0]), int(box[1] -50)),0, 5e-3 * 150, (0,255,0),2)
#print("Found person: ",person_counter)
return return_boxs,return_class_name
def close_session(self):
self.sess.close()