#! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Run a YOLOv3/YOLOv2 style detection model on test images.
"""
import colorsys
import os, sys, argparse
import cv2
import time
from timeit import default_timer as timer
import tensorflow as tf
import numpy as np
from tensorflow.keras import backend as K
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Input, Lambda
from tensorflow_model_optimization.sparsity import keras as sparsity
from PIL import Image
from yolo3.model import get_yolo3_model, get_yolo3_inference_model#, get_yolo3_prenms_model
from yolo3.postprocess_np import yolo3_postprocess_np
from yolo2.model import get_yolo2_model, get_yolo2_inference_model
from yolo2.postprocess_np import yolo2_postprocess_np
from common.data_utils import preprocess_image
from common.utils import get_classes, get_anchors, get_colors, draw_boxes
from tensorflow.keras.utils import multi_gpu_model
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
#tf.enable_eager_execution()
default_config = {
"model_type": 'tiny_yolo3_darknet',
"weights_path": os.path.join('weights', 'yolov3-tiny.h5'),
"pruning_model": False,
"anchors_path": os.path.join('configs', 'tiny_yolo3_anchors.txt'),
"classes_path": os.path.join('configs', 'coco_classes.txt'),
"score" : 0.1,
"iou" : 0.4,
"model_image_size" : (416, 416),
"gpu_num" : 1,
}
class YOLO_np(object):
_defaults = default_config
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
super(YOLO_np, self).__init__()
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = get_classes(self.classes_path)
self.anchors = get_anchors(self.anchors_path)
self.colors = get_colors(self.class_names)
K.set_learning_phase(0)
self.yolo_model = self._generate_model()
def _generate_model(self):
'''to generate the bounding boxes'''
weights_path = os.path.expanduser(self.weights_path)
assert weights_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#YOLOv3 model has 9 anchors and 3 feature layers but
#Tiny YOLOv3 model has 6 anchors and 2 feature layers,
#so we can calculate feature layers number to get model type
num_feature_layers = num_anchors//3
try:
if num_anchors == 5:
# YOLOv2 use 5 anchors
yolo_model, _ = get_yolo2_model(self.model_type, num_anchors, num_classes, input_shape=self.model_image_size + (3,), model_pruning=self.pruning_model)
else:
yolo_model, _ = get_yolo3_model(self.model_type, num_feature_layers, num_anchors, num_classes, input_shape=self.model_image_size + (3,), model_pruning=self.pruning_model)
yolo_model.load_weights(weights_path) # make sure model, anchors and classes match
if self.pruning_model:
yolo_model = sparsity.strip_pruning(yolo_model)
yolo_model.summary()
except Exception as e:
print(repr(e))
assert yolo_model.layers[-1].output_shape[-1] == \
num_anchors/len(yolo_model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
print('{} model, anchors, and classes loaded.'.format(weights_path))
if self.gpu_num>=2:
yolo_model = multi_gpu_model(yolo_model, gpus=self.gpu_num)
return yolo_model
def detect_image(self, image):
if self.model_image_size != (None, None):
assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
image_data = preprocess_image(image, self.model_image_size)
#origin image shape, in (height, width) format
image_shape = tuple(reversed(image.size))
start = time.time()
out_boxes, out_classes, out_scores = self.predict(image_data, image_shape)
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
end = time.time()
print("Inference time: {:.8f}s".format(end - start))
#draw result on input image
image_array = np.array(image, dtype='uint8')
image_array = draw_boxes(image_array, out_boxes, out_classes, out_scores, self.class_names, self.colors)
return Image.fromarray(image_array)
def predict(self, image_data, image_shape):
num_anchors = len(self.anchors)
if num_anchors == 5:
# YOLOv2 use 5 anchors
out_boxes, out_classes, out_scores = yolo2_postprocess_np(self.yolo_model.predict(image_data), image_shape, self.anchors, len(self.class_names), self.model_image_size, max_boxes=100)
else:
out_boxes, out_classes, out_scores = yolo3_postprocess_np(self.yolo_model.predict(image_data), image_shape, self.anchors, len(self.class_names), self.model_image_size, max_boxes=100)
return out_boxes, out_classes, out_scores
def dump_model_file(self, output_model_file):
self.yolo_model.save(output_model_file)
class YOLO(object):
_defaults = default_config
@classmethod
def get_defaults(cls, n):
if n in cls._defaults:
return cls._defaults[n]
else:
return "Unrecognized attribute name '" + n + "'"
def __init__(self, **kwargs):
super(YOLO, self).__init__()
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = get_classes(self.classes_path)
self.anchors = get_anchors(self.anchors_path)
self.colors = get_colors(self.class_names)
K.set_learning_phase(0)
self.inference_model = self._generate_model()
def _generate_model(self):
'''to generate the bounding boxes'''
weights_path = os.path.expanduser(self.weights_path)
assert weights_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
#YOLOv3 model has 9 anchors and 3 feature layers but
#Tiny YOLOv3 model has 6 anchors and 2 feature layers,
#so we can calculate feature layers number to get model type
num_feature_layers = num_anchors//3
if num_anchors == 5:
# YOLOv2 use 5 anchors
inference_model = get_yolo2_inference_model(self.model_type, self.anchors, num_classes, weights_path=weights_path, input_shape=self.model_image_size + (3,), confidence=0.1)
else:
inference_model = get_yolo3_inference_model(self.model_type, self.anchors, num_classes, weights_path=weights_path, input_shape=self.model_image_size + (3,), confidence=0.1)
inference_model.summary()
return inference_model
def predict(self, image_data, image_shape):
out_boxes, out_scores, out_classes = self.inference_model.predict([image_data, image_shape])
out_boxes = out_boxes[0]
out_scores = out_scores[0]
out_classes = out_classes[0]
out_boxes = out_boxes.astype(np.int32)
out_classes = out_classes.astype(np.int32)
return out_boxes, out_classes, out_scores
def detect_image(self, image):
if self.model_image_size != (None, None):
assert self.model_image_size[0]%32 == 0, 'Multiples of 32 required'
assert self.model_image_size[1]%32 == 0, 'Multiples of 32 required'
image_data = preprocess_image(image, self.model_image_size)
# prepare origin image shape, (height, width) format
image_shape = np.array([image.size[1], image.size[0]])
image_shape = np.expand_dims(image_shape, 0)
start = time.time()
out_boxes, out_classes, out_scores = self.predict(image_data, image_shape)
end = time.time()
print('Found {} boxes for {}'.format(len(out_boxes), 'img'))
print("Inference time: {:.8f}s".format(end - start))
#draw result on input image
image_array = np.array(image, dtype='uint8')
image_array = draw_boxes(image_array, out_boxes, out_classes, out_scores, self.class_names, self.colors)
return Image.fromarray(image_array)
def dump_model_file(self, output_model_file):
self.inference_model.save(output_model_file)
def dump_saved_model(self, saved_model_path):
model = self.inference_model
os.makedirs(saved_model_path, exist_ok=True)
tf.keras.experimental.export_saved_model(model, saved_model_path)
print('export inference model to %s' % str(saved_model_path))
#class YOLO_prenms(object):
#_defaults = default_config
#@classmethod
#def get_defaults(cls, n):
#if n in cls._defaults:
#return cls._defaults[n]
#else:
#return "Unrecognized attribute name '" + n + "'"
#def __init__(self, **kwargs):
#super(YOLO_prenms, self).__init__()
#self.__dict__.update(self._defaults) # set up default values
#self.__dict__.update(kwargs) # and update with user overrides
#self.class_names = get_classes(self.classes_path)
#self.anchors = get_anchors(self.anchors_path)
#self.colors = get_colors(self.class_names)
#K.set_learning_phase(0)
#self.prenms_model = self._generate_model()
#def _generate_model(self):
#'''to generate the bounding boxes'''
#weights_path = os.path.expanduser(self.weights_path)
#assert weights_path.endswith('.h5'), 'Keras model or weights must be a .h5 file.'
## Load model, or construct model and load weights.
#num_anchors = len(self.anchors)
#num_classes = len(self.class_names)
##YOLOv3 model has 9 anchors and 3 feature layers but
##Tiny YOLOv3 model has 6 anchors and 2 feature layers,
##so we can calculate feature layers number to get model type
#num_feature_layers = num_anchors//3
#prenms_model = get_yolo3_prenms_model(self.model_type, self.anchors, num_classes, weights_path=weights_path, input_shape=self.model_image_size + (3,))
#return prenms_model
#def dump_model_file(self, output_model_file):
#self.prenms_model.save(output_model_file)
#def dump_saved_model(self, saved_model_path):
#model = self.prenms_model
#os.makedirs(saved_model_path, exist_ok=True)
#tf.keras.experimental.export_saved_model(model, saved_model_path)
#print('export inference model to %s' % str(saved_model_path))
def detect_video(yolo, video_path, output_path=""):
import cv2
vid = cv2.VideoCapture(0 if video_path == '0' else video_path)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
# here we encode the video to MPEG-4 for better compatibility, you can use ffmpeg later
# to convert it to x264 to reduce file size:
# ffmpeg -i test.mp4 -vcodec libx264 -f mp4 test_264.mp4
#
#video_FourCC = cv2.VideoWriter_fourcc(*'XVID') if video_path == '0' else int(vid.get(cv2.CAP_PROP_FOURCC))
video_FourCC = cv2.VideoWriter_fourcc(*'XVID') if video_path == '0' else cv2.VideoWriter_fourcc(*"mp4v")
video_fps = vid.get(cv2.CAP_PROP_FPS)
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
isOutput = True if output_path != "" else False
if isOutput:
print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
out = cv2.VideoWriter(output_path, video_FourCC, (5. if video_path == '0' else video_fps), video_size)
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
while True:
return_value, frame = vid.read()
image = Image.fromarray(frame)
image = yolo.detect_image(image)
result = np.asarray(image)
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = "FPS: " + str(curr_fps)
curr_fps = 0
cv2.putText(result, text=fps, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.50, color=(255, 0, 0), thickness=2)
cv2.namedWindow("result", cv2.WINDOW_NORMAL)
cv2.imshow("result", result)
if isOutput:
out.write(result)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release everything if job is finished
vid.release()
if isOutput:
out.release()
cv2.destroyAllWindows()
def detect_img(yolo):
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = yolo.detect_image(image)
r_image.show()
if __name__ == '__main__':
# class YOLO defines the default value, so suppress any default here
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='demo or dump out YOLO h5 model')
'''
Command line options
'''
parser.add_argument(
'--model_type', type=str,
help='YOLO model type: yolo3_mobilenet_lite/tiny_yolo3_mobilenet/yolo3_darknet/..., default ' + YOLO.get_defaults("model_type")
)
parser.add_argument(
'--weights_path', type=str,
help='path to model weight file, default ' + YOLO.get_defaults("weights_path")
)
parser.add_argument(
'--pruning_model', default=False, action="store_true",
help='Whether to be a pruning model/weights file')
parser.add_argument(
'--anchors_path', type=str,
help='path to anchor definitions, default ' + YOLO.get_defaults("anchors_path")
)
parser.add_argument(
'--classes_path', type=str,
help='path to class definitions, default ' + YOLO.get_defaults("classes_path")
)
parser.add_argument(
'--model_image_size', type=str,
help='model image input size as <height>x<width>, default ' +
str(YOLO.get_defaults("model_image_size")[0])+'x'+str(YOLO.get_defaults("model_image_size")[1]),
default=str(YOLO.get_defaults("model_image_size")[0])+'x'+str(YOLO.get_defaults("model_image_size")[1])
)
parser.add_argument(
'--gpu_num', type=int,
help='Number of GPU to use, default ' + str(YOLO.get_defaults("gpu_num"))
)
parser.add_argument(
'--image', default=False, action="store_true",
help='Image detection mode, will ignore all positional arguments'
)
'''
Command line positional arguments -- for video detection mode
'''
parser.add_argument(
"--input", nargs='?', type=str,required=False,default='./path2your_video',
help = "Video input path"
)
parser.add_argument(
"--output", nargs='?', type=str, default="",
help = "[Optional] Video output path"
)
'''
Command line positional arguments -- for model dump
'''
parser.add_argument(
'--dump_model', default=False, action="store_true",
help='Dump out training model to inference model'
)
parser.add_argument(
'--output_model_file', type=str,
help='output inference model file'
)
args = parser.parse_args()
# param parse
if args.model_image_size:
height, width = args.model_image_size.split('x')
args.model_image_size = (int(height), int(width))
assert (args.model_image_size[0]%32 == 0 and args.model_image_size[1]%32 == 0), 'model_image_size should be multiples of 32'
# get wrapped inference object
yolo = YOLO_np(**vars(args))
if args.dump_model:
"""
Dump out training model to inference model
"""
if not args.output_model_file:
raise ValueError('output model file is not specified')
print('Dumping out training model to inference model')
yolo.dump_model_file(args.output_model_file)
sys.exit()
if args.image:
"""
Image detection mode, disregard any remaining command line arguments
"""
print("Image detection mode")
if "input" in args:
print(" Ignoring remaining command line arguments: " + args.input + "," + args.output)
detect_img(yolo)
elif "input" in args:
detect_video(yolo, args.input, args.output)
else:
print("Must specify at least video_input_path. See usage with --help.")
