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This commit is contained in:
Jordon Brooks 2023-07-30 17:29:32 +01:00
parent 60c6c97071
commit 93ccce5ec1
6 changed files with 46 additions and 34 deletions
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4
.gitignore vendored
View file

@ -12,5 +12,5 @@
!video_compression_model.py
!global_train.py
!log.py
!test_data/training.json
!test_data/validation.json
!test_data/training/training.json
!test_data/validation/validation.json

13
DeepEncode.py
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@ -3,14 +3,14 @@
import tensorflow as tf
import numpy as np
import cv2
from video_compression_model import VideoCompressionModel
from video_compression_model import PRESET_SPEED_CATEGORIES, VideoCompressionModel
# Constants
CHUNK_SIZE = 10 # Adjust based on available memory and video resolution
CHUNK_SIZE = 24 # Adjust based on available memory and video resolution
COMPRESSED_VIDEO_FILE = 'compressed_video.avi'
MAX_FRAMES = 0 # Limit the number of frames processed
CRF = 25.0 # Example CRF value
PRESET_SPEED = 4 # Index for "fast" in our defined list
CRF = 24.0 # Example CRF value
PRESET_SPEED = "veryslow" # Index for "fast" in our defined list
# Load the trained model
model = tf.keras.models.load_model('models/model.tf', custom_objects={'VideoCompressionModel': VideoCompressionModel})
@ -42,6 +42,7 @@ def predict_frame(uncompressed_frame, model, crf_value, preset_speed_value):
compressed_frame = model.predict({
"compressed_frame": uncompressed_frame,
"uncompressed_frame": uncompressed_frame,
"crf": crf_array,
"preset_speed": preset_speed_array
})
@ -49,7 +50,7 @@ def predict_frame(uncompressed_frame, model, crf_value, preset_speed_value):
display_frame = np.clip(cv2.cvtColor(compressed_frame[0], cv2.COLOR_BGR2RGB) * 255.0, 0, 255).astype(np.uint8)
cv2.imshow("comp", display_frame)
cv2.waitKey(10)
cv2.waitKey(1)
return compressed_frame[0]
@ -70,7 +71,7 @@ if MAX_FRAMES != 0 and total_frames > MAX_FRAMES:
for i in range(total_frames):
uncompressed_frame = load_frame_from_video(UNCOMPRESSED_VIDEO_FILE, frame_num=i)
compressed_frame = predict_frame(uncompressed_frame, model, CRF, PRESET_SPEED)
compressed_frame = predict_frame(uncompressed_frame, model, CRF, PRESET_SPEED_CATEGORIES.index(PRESET_SPEED))
compressed_frame = np.clip(compressed_frame * 255.0, 0, 255).astype(np.uint8)
compressed_frame = cv2.cvtColor(compressed_frame, cv2.COLOR_RGB2BGR)

24
test_data/training.json → test_data/training/training.json
View file

@ -1,73 +1,73 @@
[
{
"video_file": "x264_crf-51_preset-ultrafast.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "ultrafast"
},
{
"video_file": "x264_crf-16_preset-veryslow.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 16,
"preset_speed": "veryslow"
},
{
"video_file": "x264_crf-18_preset-ultrafast.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 18,
"preset_speed": "ultrafast"
},
{
"video_file": "x264_crf-18_preset-veryslow.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 18,
"preset_speed": "veryslow"
},
{
"video_file": "x264_crf-50_preset-veryslow.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 50,
"preset_speed": "veryslow"
},
{
"video_file": "x264_crf-51_preset-fast.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "fast"
},
{
"video_file": "x264_crf-51_preset-faster.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "faster"
},
{
"video_file": "x264_crf-51_preset-medium.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "medium"
},
{
"video_file": "x264_crf-51_preset-slow.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "slow"
},
{
"video_file": "x264_crf-51_preset-slower.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "slower"
},
{
"video_file": "x264_crf-51_preset-superfast.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "superfast"
},
{
"video_file": "x264_crf-51_preset-veryfast.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "veryfast"
}

8
test_data/validation.json
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@ -1,8 +0,0 @@
[
{
"video_file": "x264_crf-16_preset-veryslow.mkv",
"uncompressed_video_file": "x264_crf-5_preset-veryslow.mkv",
"crf": 16,
"preset_speed": "veryslow"
}
]

9
test_data/validation/validation.json Normal file
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@ -0,0 +1,9 @@
[
{
"video_file": "Scene2_x264_crf-51_preset-veryslow.mkv",
"uncompressed_video_file": "Scene2_x264_crf-5_preset-veryslow.mkv",
"crf": 51,
"preset_speed": "veryslow"
}
]

22
train_model.py
View file

@ -18,10 +18,12 @@ from global_train import LOGGER
BATCH_SIZE = 4
EPOCHS = 100
LEARNING_RATE = 0.000001
TRAIN_SAMPLES = 50
TRAIN_SAMPLES = 100
MODEL_SAVE_FILE = "models/model.tf"
MODEL_CHECKPOINT_DIR = "checkpoints"
EARLY_STOP = 10
WIDTH = 638
HEIGHT = 360
def load_video_metadata(list_path):
LOGGER.trace(f"Entering: load_video_metadata({list_path})")
@ -67,11 +69,11 @@ def load_video_samples(list_path, samples=TRAIN_SAMPLES):
compressed_frames, uncompressed_frames = [], []
try:
cap = cv2.VideoCapture(os.path.join("test_data/", video_file))
cap_uncompressed = cv2.VideoCapture(os.path.join("test_data/", uncompressed_video_file))
cap = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), video_file))
cap_uncompressed = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), uncompressed_video_file))
if not cap.isOpened() or not cap_uncompressed.isOpened():
raise RuntimeError(f"Could not open video files {video_file} or {uncompressed_video_file}")
raise RuntimeError(f"Could not open video files {video_file} or {uncompressed_video_file}, searched under: {os.path.dirname(list_path)}")
for _ in range(frames_per_video):
ret, frame_compressed = cap.read()
@ -79,6 +81,14 @@ def load_video_samples(list_path, samples=TRAIN_SAMPLES):
if not ret or not ret_uncompressed:
continue
# Check frame dimensions and resize if necessary
if frame.shape[:2] != (WIDTH, HEIGHT):
LOGGER.warn(f"Resizing video: {video_file}")
frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
if frame_compressed.shape[:2] != (WIDTH, HEIGHT):
LOGGER.warn(f"Resizing video: {uncompressed_video_file}")
frame_compressed = cv2.resize(frame_compressed, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame_compressed = cv2.cvtColor(frame_compressed, cv2.COLOR_BGR2RGB)
@ -149,8 +159,8 @@ def main():
# Load training and validation samples
LOGGER.debug("Loading training and validation samples.")
training_samples = load_video_samples("test_data/training.json")
validation_samples = load_video_samples("test_data/validation.json", args.training_samples // 2)
training_samples = load_video_samples("test_data/training/training.json")
validation_samples = load_video_samples("test_data/validation/validation.json", args.training_samples // 2)
train_generator = VideoDataGenerator(training_samples, args.batch_size)
val_generator = VideoDataGenerator(validation_samples, args.batch_size)