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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 numpy as np
# from keras import backend as K
# import tensorflow.python.keras.backend as K
import tensorflow.compat.v1.keras.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
class YOLO(object):
def __init__(self):
self.model_path = 'model_data/yolo.h5'
self.anchors_path = 'model_data/yolo_anchors.txt'
self.classes_path = 'model_data/coco_classes.txt'
self.score = 0.5
self.iou = 0.5
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.sess = K.get_session()
self.model_image_size = (None,None) #(416, 416) # fixed size or (None, None)
self.class_threshold = 0.6
self.net_h = 416
self.net_w = 416
self.obj_thresh = 0.7
self.nms_thresh = 0.45
self.is_fixed_size = self.model_image_size != (None, None)
# self.boxes, self.scores, self.classes = self.generate()
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]
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
#eturn [[116,90, 156,198, 373,326], [30,61, 62,45, 59,119], [10,13, 16,30, 33,23]]
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.
# self.yolo_model.input = image_data
# self.input_image_shape = [image.size[1], image.size[0]]
yolos = self.yolo_model.predict(image_data)
return_boxs = []
for i in range(len(yolos)):
# decode the output of the network
# we need out_boxes, out_scores, out_classes
# we are loosing class and score information here!
anchor_idx = 2 - i
tmpBox = self.decode_netout(yolos[i][0], self.anchors.flatten()[anchor_idx*6:(anchor_idx+1)*6], self.obj_thresh, self.nms_thresh, self.net_h, self.net_w)
for j in range(len(tmpBox)):
x = (tmpBox[j].x * image.size[0])
y = (tmpBox[j].y * image.size[1])
w = (tmpBox[j].w * image.size[0])
h = (tmpBox[j].h * image.size[1])
if x < 0 :
w = w + x
x = 0
if y < 0 :
h = h + y
y = 0
return_boxs.append([x,y,w,h, tmpBox[j].get_label(), tmpBox[j].get_score()])
return return_boxs
def close_session(self):
self.sess.close()
def decode_netout(self, netout, anchors, obj_thresh, nms_thresh, net_h, net_w):
grid_h, grid_w = netout.shape[:2]
nb_box = 3
netout = netout.reshape((grid_h, grid_w, nb_box, -1))
nb_class = netout.shape[-1] - 5
boxes = []
netout[..., :2] = self._sigmoid(netout[..., :2])
netout[..., 4:] = self._sigmoid(netout[..., 4:])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:]
netout[..., 5:] *= netout[..., 5:] > obj_thresh
for i in range(grid_h*grid_w):
row = i / grid_w
col = i % grid_w
for b in range(nb_box):
# 4th element is objectness score
objectness = netout[int(row)][int(col)][b][4]
#objectness = netout[..., :4]
#if(objectness.all() <= obj_thresh): continue
if(objectness <= obj_thresh): continue
# first 4 elements are x, y, w, and h
x, y, w, h = netout[int(row)][int(col)][b][:4]
x = (col + x) / grid_w # center position, unit: image width
y = (row + y) / grid_h # center position, unit: image height
w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width
h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height
# last elements are class probabilities
classes = netout[int(row)][col][b][5:]
# if (np.argmax(classes) == 0): continue
print(self.class_names[np.argmax(classes)] + " - " + str(objectness))
box = BoundBox(x-w/2, y-h/2, h, w, objectness, classes)
# box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)
boxes.append(box)
return boxes
def _sigmoid(self, x):
return 1. / (1. + np.exp(-x))
class BoundBox:
def __init__(self, x, y, h, w, objness = None, classes = None):
self.x = x
self.y = y
self.h = h
self.w = w
self.objness = objness
self.classes = classes
self.label = -1
self.score = -1
def get_label(self):
if self.label == -1:
self.label = np.argmax(self.classes)
return self.label
def get_score(self):
if self.score == -1:
self.score = self.classes[self.get_label()]
return self.score