180s

infer.py

@@ -1,86 +1,86 @@
-from PIL import Image
-import cv2 as cv
-import torch
-from RealESRGAN import RealESRGAN
-import tempfile
-import numpy as np
-import tqdm
-import ffmpeg
-import spaces
-
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-@spaces.GPU(duration=60)
-def infer_image(img: Image.Image, size_modifier: int ) -> Image.Image:
-    if img is None:
-        raise Exception("Image not uploaded")
-    
-    width, height = img.size
-    
-    if width >= 5000 or height >= 5000:
-        raise Exception("The image is too large.")
-
-    model = RealESRGAN(device, scale=size_modifier)
-    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)
-
-    result = model.predict(img.convert('RGB'))
-    print(f"Image size ({device}): {size_modifier} ... OK")
-    return result
-
-@spaces.GPU(duration=300)
-def infer_video(video_filepath: str, size_modifier: int) -> str:
-    model = RealESRGAN(device, scale=size_modifier)
-    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)
-
-    cap = cv.VideoCapture(video_filepath)
-    
-    tmpfile = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-    vid_output = tmpfile.name
-    tmpfile.close()
-
-    # Check if the input video has an audio stream
-    probe = ffmpeg.probe(video_filepath)
-    has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])
-
-    if has_audio:
-        # Extract audio from the input video
-        audio_file = video_filepath.replace(".mp4", ".wav")
-        ffmpeg.input(video_filepath).output(audio_file, format='wav', ac=1).run(overwrite_output=True)
-
-    vid_writer = cv.VideoWriter(
-        vid_output,
-        fourcc=cv.VideoWriter.fourcc(*'mp4v'),
-        fps=cap.get(cv.CAP_PROP_FPS),
-        frameSize=(int(cap.get(cv.CAP_PROP_FRAME_WIDTH)) * size_modifier, int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)) * size_modifier)
-    )
-
-    n_frames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))
-
-    for _ in tqdm.tqdm(range(n_frames)):
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
-        frame = Image.fromarray(frame)
-
-        upscaled_frame = model.predict(frame.convert('RGB'))
-        
-        upscaled_frame = np.array(upscaled_frame)
-        upscaled_frame = cv.cvtColor(upscaled_frame, cv.COLOR_RGB2BGR)
-
-        vid_writer.write(upscaled_frame)
-
-    vid_writer.release()
-
-    if has_audio:
-        # Re-encode the video with the modified audio
-        ffmpeg.input(vid_output).output(video_filepath.replace(".mp4", "_upscaled.mp4"), vcodec='libx264', acodec='aac', audio_bitrate='320k').run(overwrite_output=True)
-
-        # Replace the original audio with the upscaled audio
-        ffmpeg.input(audio_file).output(video_filepath.replace(".mp4", "_upscaled.mp4"), acodec='aac', audio_bitrate='320k').run(overwrite_output=True)
-
-    print(f"Video file : {video_filepath}")
-
+from PIL import Image
+import cv2 as cv
+import torch
+from RealESRGAN import RealESRGAN
+import tempfile
+import numpy as np
+import tqdm
+import ffmpeg
+import spaces
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+@spaces.GPU(duration=60)
+def infer_image(img: Image.Image, size_modifier: int ) -> Image.Image:
+    if img is None:
+        raise Exception("Image not uploaded")
+    
+    width, height = img.size
+    
+    if width >= 5000 or height >= 5000:
+        raise Exception("The image is too large.")
+
+    model = RealESRGAN(device, scale=size_modifier)
+    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)
+
+    result = model.predict(img.convert('RGB'))
+    print(f"Image size ({device}): {size_modifier} ... OK")
+    return result
+
+@spaces.GPU(duration=180)
+def infer_video(video_filepath: str, size_modifier: int) -> str:
+    model = RealESRGAN(device, scale=size_modifier)
+    model.load_weights(f'weights/RealESRGAN_x{size_modifier}.pth', download=False)
+
+    cap = cv.VideoCapture(video_filepath)
+    
+    tmpfile = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
+    vid_output = tmpfile.name
+    tmpfile.close()
+
+    # Check if the input video has an audio stream
+    probe = ffmpeg.probe(video_filepath)
+    has_audio = any(stream['codec_type'] == 'audio' for stream in probe['streams'])
+
+    if has_audio:
+        # Extract audio from the input video
+        audio_file = video_filepath.replace(".mp4", ".wav")
+        ffmpeg.input(video_filepath).output(audio_file, format='wav', ac=1).run(overwrite_output=True)
+
+    vid_writer = cv.VideoWriter(
+        vid_output,
+        fourcc=cv.VideoWriter.fourcc(*'mp4v'),
+        fps=cap.get(cv.CAP_PROP_FPS),
+        frameSize=(int(cap.get(cv.CAP_PROP_FRAME_WIDTH)) * size_modifier, int(cap.get(cv.CAP_PROP_FRAME_HEIGHT)) * size_modifier)
+    )
+
+    n_frames = int(cap.get(cv.CAP_PROP_FRAME_COUNT))
+
+    for _ in tqdm.tqdm(range(n_frames)):
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
+        frame = Image.fromarray(frame)
+
+        upscaled_frame = model.predict(frame.convert('RGB'))
+        
+        upscaled_frame = np.array(upscaled_frame)
+        upscaled_frame = cv.cvtColor(upscaled_frame, cv.COLOR_RGB2BGR)
+
+        vid_writer.write(upscaled_frame)
+
+    vid_writer.release()
+
+    if has_audio:
+        # Re-encode the video with the modified audio
+        ffmpeg.input(vid_output).output(video_filepath.replace(".mp4", "_upscaled.mp4"), vcodec='libx264', acodec='aac', audio_bitrate='320k').run(overwrite_output=True)
+
+        # Replace the original audio with the upscaled audio
+        ffmpeg.input(audio_file).output(video_filepath.replace(".mp4", "_upscaled.mp4"), acodec='aac', audio_bitrate='320k').run(overwrite_output=True)
+
+    print(f"Video file : {video_filepath}")
+
     return vid_output.replace(".mp4", "_upscaled.mp4") if has_audio else vid_output