import cv2
from tensorflow.keras import Input, Model
from darknet import darknet_base
from predict import predict
inputs = Input(shape=(None, None, 3))
outputs, config = darknet_base(inputs)
model = Model(inputs, outputs)
vidcap = cv2.VideoCapture('data/demo.mp4')
success, image = vidcap.read()
size = (int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
fourcc = cv2.VideoWriter_fourcc(*'DIVX')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, size)
while success:
output_image = predict(model, image, config)
out.write(output_image)
success, image = vidcap.read()
vidcap.release()
out.release()
# NOTE: Because of a bug in TensorFlow, this should be run in the console
# NOTE: python tflite.py
import os
import tensorflow as tf
from tensorflow.contrib.lite.python import lite
from tensorflow.keras import Input, Model
from darknet import darknet_base
from definitions import ROOT_DIR
inputs = Input(shape=(None, None, 3))
# NOTE: Here, we do not include the YOLO head because TFLite does not
# NOTE: support custom layers yet. Therefore, we'll need to implement
# NOTE: the YOLO head ourselves.
outputs, config = darknet_base(inputs, include_yolo_head=False)
model = Model(inputs, outputs)
model_path = os.path.join(ROOT_DIR, 'model', 'yolov3.h5')
tf.keras.models.save_model(model, model_path, overwrite=True)
# Sanity check to see if model loads properly
# NOTE: See https://github.com/keras-team/keras/issues/4609#issuecomment-329292173
# on why we have to pass in `tf: tf` in `custom_objects`
model = tf.keras.models.load_model(model_path, custom_objects={'tf': tf})
converter = lite.TocoConverter.from_keras_model_file(
model_path,
input_shapes={'input_1': [1, config['width'], config['height'], 3]})
converter.post_training_quantize = True
import time
import cv2
from tensorflow.keras import Input, Model
from darknet import darknet_base
from predict import predict, predict_with_yolo_head
INCLUDE_YOLO_HEAD = True
stream = cv2.VideoCapture(0)
inputs = Input(shape=(None, None, 3))
outputs, config = darknet_base(inputs, include_yolo_head=INCLUDE_YOLO_HEAD)
model = Model(inputs, outputs)
while True:
# Capture frame-by-frame
grabbed, frame = stream.read()
if not grabbed:
break
# Run detection
start = time.time()
if INCLUDE_YOLO_HEAD:
output_image = predict(model, frame, config)
else:
output_image = predict_with_yolo_head(model,
frame,
config,
confidence=0.3,
from darknet import darknet_base
import argparse
from tensorflow.keras import Input, Model
import tensorflow as tf
parser = argparse.ArgumentParser(description='Keras to TF-Lite converter')
parser.add_argument('--h5_path', default='../../pretrain_models/yolov3/yolov3.h5',
help='Path to Darknet h5 weights file.')
parser.add_argument('--output_path', default='../../pretrain_models/yolov3/yolov3_weights.tflite',
help='Path to output Keras model file.')
args = parser.parse_args()
inputs = Input(shape=(None, None, 3))
outputs, config = darknet_base(
inputs, yolo_version='yolov3', data_dir='../../pretrain_models/yolov3', include_yolo_head=False)
model = Model(inputs, outputs)
model_path = args.h5_path
tf.keras.models.save_model(model, model_path, overwrite=True)
model.summary()
# Sanity check to see if model loads properly
# NOTE: See https://github.com/keras-team/keras/issues/4609#issuecomment-329292173
# on why we have to pass in `tf: tf` in `custom_objects`
model = tf.keras.models.load_model(model_path,
custom_objects={'tf': tf})
converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(model_path,
input_shapes={'input_1': [1, config['width'], config['height'], 3]})
tflite_model = converter.convert()
parser = argparse.ArgumentParser(description='Keras to TF-Lite converter')
parser.add_argument('--h5_path', default='../../pretrain_models/yolov3_tflite.h5',
help='Path to Darknet h5 weights file.')
parser.add_argument('--output_path', default='../../pretrain_models/yolov3.tflite',
help='Path to output Keras model file.')
args = parser.parse_args()
inputs = Input(shape=(None, None, 3))
# NOTE: Here, we do not include the YOLO head because TFLite does not
# NOTE: support custom layers yet. Therefore, we'll need to implement
# NOTE: the YOLO head ourselves.
outputs, config = darknet_base(
inputs, YOLO_VERSION='yolov3', data_dir='weights', include_yolo_head=False)
model = Model(inputs, outputs)
model_path = args.h5_path
tf.keras.models.save_model(model, model_path, overwrite=True)
model.summary()
# Sanity check to see if model loads properly
# NOTE: See https://github.com/keras-team/keras/issues/4609#issuecomment-329292173
# on why we have to pass in `tf: tf` in `custom_objects`
model = tf.keras.models.load_model(model_path,
custom_objects={'tf': tf})
converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(model_path,
input_shapes={'input_1': [1, config['width'], config['height'], 3]})
#converter.post_training_quantize = True
parser = argparse.ArgumentParser(description='Keras to TF-Lite converter')
parser.add_argument('--h5_path',
default='weights/yolo-obj-608-water_15000.h5',
help='Path to Darknet h5 weights file.')
parser.add_argument('--output_path',
default='weights/yolo-obj-608-water_15000.tflite',
help='Path to output Keras model file.')
args = parser.parse_args()
inputs = Input(shape=(None, None, 3))
# NOTE: Here, we do not include the YOLO head because TFLite does not
# NOTE: support custom layers yet. Therefore, we'll need to implement
# NOTE: the YOLO head ourselves.
outputs, config = darknet_base(inputs,
YOLO_VERSION='yolo-obj-608-water_15000',
data_dir='weights',
include_yolo_head=False)
model = Model(inputs, outputs)
model_path = args.h5_path
tf.keras.models.save_model(model, model_path, overwrite=True)
model.summary()
# Sanity check to see if model loads properly
# NOTE: See https://github.com/keras-team/keras/issues/4609#issuecomment-329292173
# on why we have to pass in `tf: tf` in `custom_objects`
model = tf.keras.models.load_model(model_path, custom_objects={'tf': tf})
converter = tf.contrib.lite.TFLiteConverter.from_keras_model_file(
model_path,
input_shapes={'input_1': [1, config['width'], config['height'], 3]})
