File size: 8,110 Bytes
1266aec 9d449ad 1266aec 9d449ad 1266aec 9d449ad 1266aec 9d449ad 1266aec 9d449ad 1266aec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | #!/usr/bin/env python
"""FastDVDnet axmodel video inference, side-by-side output.
Input: mp4 video -> resize to axmodel fixed input -> 5-frame window -> axmodel -> output video.
Input dtype is inferred from the axmodel session so both float and uint8 models work."""
import argparse, os, time
import cv2, numpy as np
import axengine as axe
def _resolve_numpy_dtype(dtype, fallback):
try:
return np.dtype(dtype)
except TypeError:
name = str(dtype).lower()
if "float16" in name or "fp16" in name:
return np.dtype(np.float16)
if "float" in name:
return np.dtype(np.float32)
if "uint8" in name:
return np.dtype(np.uint8)
if "int8" in name:
return np.dtype(np.int8)
return np.dtype(fallback)
def read_video(video_path, max_frames=0):
cap=cv2.VideoCapture(video_path)
if not cap.isOpened(): raise RuntimeError('open fail: '+video_path)
fps=cap.get(cv2.CAP_PROP_FPS) or 25.0
ow=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)); oh=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
frames=[]
while True:
ok,f=cap.read()
if not ok: break
frames.append(f)
if max_frames and len(frames)>=max_frames: break
cap.release()
if not frames: raise RuntimeError('no frames')
return frames,fps,ow,oh
def reflect_index(idx,length):
if length<=1: return 0
while idx<0 or idx>=length:
if idx<0: idx=-idx
if idx>=length: idx=2*(length-1)-idx
return idx
def bgr_to_ax_input(fb, ah, aw, noisy_dtype):
r = cv2.resize(fb, (aw, ah), interpolation=cv2.INTER_AREA)
r = cv2.cvtColor(r, cv2.COLOR_BGR2RGB)
chw = r.transpose(2, 0, 1)
noisy_dtype = _resolve_numpy_dtype(noisy_dtype, np.float32)
if np.issubdtype(noisy_dtype, np.floating):
return (chw.astype(np.float32) / 255.0).astype(noisy_dtype, copy=False)
return chw.astype(noisy_dtype, copy=False)
def chw_float_to_bgr_u8(chw,tw,th):
hwc=(chw*255.).clip(0,255).astype(np.uint8).transpose(1,2,0)
bgr=cv2.cvtColor(hwc,cv2.COLOR_RGB2BGR)
if bgr.shape[1]!=tw or bgr.shape[0]!=th:
bgr=cv2.resize(bgr,(tw,th),interpolation=cv2.INTER_LINEAR)
return bgr
def denoise_axmodel(frames_bgr, noise_sigma_01, sess, ax_h, ax_w,
input_names, output_names, noisy_dtype, noise_map_dtype):
numframes = len(frames_bgr)
temp_psz, ctrl = 5, 2
chw_cache = {}
def get_chw(i):
i = i % numframes
if i not in chw_cache:
chw_cache[i] = bgr_to_ax_input(
frames_bgr[reflect_index(i, numframes)], ax_h, ax_w, noisy_dtype)
return chw_cache[i]
den_frames_bgr = []
inframes = []
for fridx in range(numframes):
if not inframes:
for off in range(temp_psz):
inframes.append(get_chw(fridx + off - ctrl))
else:
del inframes[0]
inframes.append(get_chw(fridx + ctrl))
noisy = np.concatenate(inframes, axis=0)[None, :, :, :]
noise_map = np.full(
(1, 1, ax_h, ax_w),
noise_sigma_01,
dtype=_resolve_numpy_dtype(noise_map_dtype, np.float32),
)
feeds = {input_names[0]: noisy, input_names[1]: noise_map}
out = sess.run(output_names, feeds)[0]
out = np.clip(out, 0.0, 1.0)
den_bgr = chw_float_to_bgr_u8(
out[0], frames_bgr[0].shape[1], frames_bgr[0].shape[0])
den_frames_bgr.append(den_bgr)
return den_frames_bgr
def write_side_by_side(frames_orig, frames_den, out_path, fps, label=True):
h, w = frames_orig[0].shape[:2]
os.makedirs(os.path.dirname(os.path.abspath(out_path)), exist_ok=True)
ext = os.path.splitext(out_path)[1].lower()
if ext == '.gif':
_write_gif(frames_orig, frames_den, out_path, fps, w, h, label)
else:
_write_mp4(frames_orig, frames_den, out_path, fps, w, h, label)
def _write_mp4(frames_orig, frames_den, out_path, fps, w, h, label):
fourcc = cv2.VideoWriter_fourcc(*"avc1")
writer = cv2.VideoWriter(out_path, fourcc, fps, (w * 2, h))
if not writer.isOpened():
# fallback to mp4v if avc1 not available
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
writer = cv2.VideoWriter(out_path, fourcc, fps, (w * 2, h))
if not writer.isOpened():
raise RuntimeError("video writer fail: {}".format(out_path))
for orig, den in zip(frames_orig, frames_den):
canvas = np.concatenate([orig, den], axis=1)
if label:
cv2.putText(canvas, "Original", (16, 34), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 255, 255), 2, cv2.LINE_AA)
cv2.putText(canvas, "AXModel Denoised", (w + 16, 34), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 255, 255), 2, cv2.LINE_AA)
writer.write(canvas)
writer.release()
def _write_gif(frames_orig, frames_den, out_path, fps, w, h, label):
try:
from PIL import Image
except ImportError:
raise RuntimeError("GIF output requires Pillow: pip install Pillow")
gif_frames = []
for orig, den in zip(frames_orig, frames_den):
canvas = np.concatenate([orig, den], axis=1)
if label:
cv2.putText(canvas, "Original", (16, 34), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 255, 255), 2, cv2.LINE_AA)
cv2.putText(canvas, "AXModel Denoised", (w + 16, 34), cv2.FONT_HERSHEY_SIMPLEX,
1.0, (0, 255, 255), 2, cv2.LINE_AA)
canvas_rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
gif_frames.append(Image.fromarray(canvas_rgb))
duration = int(1000.0 / fps)
gif_frames[0].save(
out_path, save_all=True, append_images=gif_frames[1:],
duration=duration, loop=0, optimize=True,
)
def main():
parser = argparse.ArgumentParser(description="FastDVDnet axmodel video inference")
parser.add_argument("--axmodel", type=str, default="./fastdvdnet_640x480.axmodel")
parser.add_argument("--video", type=str, default='./people-sunset.mp4', help="input mp4 video")
parser.add_argument("--noise_sigma", type=float, default=25.0, help="0-255")
parser.add_argument("--out_dir", type=str, default="./")
parser.add_argument("--max_frames", type=int, default=0, help="0=all")
parser.add_argument("--no_label", action="store_true")
parser.add_argument("--format", type=str, default="gif", choices=["mp4", "gif"],
help="output format")
args = parser.parse_args()
sess = axe.InferenceSession(args.axmodel, providers=["AxEngineExecutionProvider"])
inputs = sess.get_inputs()
input_names = [i.name for i in inputs]
output_names = [o.name for o in sess.get_outputs()]
noisy_dtype = getattr(inputs[0], "dtype", np.float32)
noise_map_dtype = getattr(inputs[1], "dtype", np.float32)
ax_h = inputs[0].shape[2]
ax_w = inputs[0].shape[3]
print("providers:", sess.get_providers())
print("inputs:", [(n, list(i.shape), str(getattr(i, "dtype", "unknown"))) for n, i in zip(input_names, inputs)])
print("outputs:", [(n, list(o.shape)) for n, o in zip(output_names, sess.get_outputs())])
base = os.path.splitext(os.path.basename(args.video))[0]
out_path = os.path.join(args.out_dir, base + "_axmodel_side_by_side." + args.format)
t0 = time.time()
frames_bgr, fps, orig_w, orig_h = read_video(args.video, args.max_frames)
print("video: {}x{} @ {:.1f}fps, {} frames".format(orig_w, orig_h, fps, len(frames_bgr)))
print("axmodel input: {}x{}".format(ax_w, ax_h))
sigma01 = args.noise_sigma / 255.0
den = denoise_axmodel(
frames_bgr, sigma01, sess, ax_h, ax_w,
input_names, output_names, noisy_dtype, noise_map_dtype,
)
write_side_by_side(frames_bgr, den, out_path, fps, label=not args.no_label)
dt = time.time() - t0
print("[OK] {} -> {}".format(args.video, out_path))
print(" frames={} time={:.2f}s fps={:.2f}".format(
len(frames_bgr), dt, len(frames_bgr) / max(dt, 0.001)))
if __name__ == "__main__":
main()
|