[Bugfix] Fix ndarray video color from VideoAsset (#21064)

Signed-off-by: Isotr0py <2037008807@qq.com>

tests/multimodal/test_video.py

@@ -1,14 +1,22 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import tempfile
+from pathlib import Path
+
 import numpy as np
 import numpy.typing as npt
 import pytest
+from PIL import Image
 
-from vllm import envs
+from vllm.assets.base import get_vllm_public_assets
+from vllm.assets.video import video_to_ndarrays, video_to_pil_images_list
 from vllm.multimodal.image import ImageMediaIO
 from vllm.multimodal.video import (VIDEO_LOADER_REGISTRY, VideoLoader,
                                    VideoMediaIO)
 
+from .utils import cosine_similarity, create_video_from_image, normalize_image
+
 NUM_FRAMES = 10
 FAKE_OUTPUT_1 = np.random.rand(NUM_FRAMES, 1280, 720, 3)
 FAKE_OUTPUT_2 = np.random.rand(NUM_FRAMES, 1280, 720, 3)
@@ -59,30 +67,79 @@ class Assert10Frames1FPSVideoLoader(VideoLoader):
         return FAKE_OUTPUT_2
 
 
-def test_video_media_io_kwargs():
-    envs.VLLM_VIDEO_LOADER_BACKEND = "assert_10_frames_1_fps"
-    imageio = ImageMediaIO()
+def test_video_media_io_kwargs(monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_VIDEO_LOADER_BACKEND", "assert_10_frames_1_fps")
+        imageio = ImageMediaIO()
 
-    # Verify that different args pass/fail assertions as expected.
-    videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 1.0})
-    _ = videoio.load_bytes(b"test")
-
-    videoio = VideoMediaIO(
-        imageio, **{
-            "num_frames": 10,
-            "fps": 1.0,
-            "not_used": "not_used"
-        })
-    _ = videoio.load_bytes(b"test")
-
-    with pytest.raises(AssertionError, match="bad num_frames"):
-        videoio = VideoMediaIO(imageio, **{})
+        # Verify that different args pass/fail assertions as expected.
+        videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 1.0})
         _ = videoio.load_bytes(b"test")
 
-    with pytest.raises(AssertionError, match="bad num_frames"):
-        videoio = VideoMediaIO(imageio, **{"num_frames": 9, "fps": 1.0})
+        videoio = VideoMediaIO(
+            imageio, **{
+                "num_frames": 10,
+                "fps": 1.0,
+                "not_used": "not_used"
+            })
         _ = videoio.load_bytes(b"test")
 
-    with pytest.raises(AssertionError, match="bad fps"):
-        videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 2.0})
-        _ = videoio.load_bytes(b"test")
+        with pytest.raises(AssertionError, match="bad num_frames"):
+            videoio = VideoMediaIO(imageio, **{})
+            _ = videoio.load_bytes(b"test")
+
+        with pytest.raises(AssertionError, match="bad num_frames"):
+            videoio = VideoMediaIO(imageio, **{"num_frames": 9, "fps": 1.0})
+            _ = videoio.load_bytes(b"test")
+
+        with pytest.raises(AssertionError, match="bad fps"):
+            videoio = VideoMediaIO(imageio, **{"num_frames": 10, "fps": 2.0})
+            _ = videoio.load_bytes(b"test")
+
+
+@pytest.mark.parametrize("is_color", [True, False])
+@pytest.mark.parametrize("fourcc, ext", [("mp4v", "mp4"), ("XVID", "avi")])
+def test_opencv_video_io_colorspace(is_color: bool, fourcc: str, ext: str):
+    """
+    Test all functions that use OpenCV for video I/O return RGB format.
+    Both RGB and grayscale videos are tested.
+    """
+    image_path = get_vllm_public_assets(filename="stop_sign.jpg",
+                                        s3_prefix="vision_model_images")
+    image = Image.open(image_path)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        if not is_color:
+            image_path = f"{tmpdir}/test_grayscale_image.png"
+            image = image.convert("L")
+            image.save(image_path)
+            # Convert to gray RGB for comparison
+            image = image.convert("RGB")
+        video_path = f"{tmpdir}/test_RGB_video.{ext}"
+        create_video_from_image(
+            image_path,
+            video_path,
+            num_frames=2,
+            is_color=is_color,
+            fourcc=fourcc,
+        )
+
+        frames = video_to_ndarrays(video_path)
+        for frame in frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99
+
+        pil_frames = video_to_pil_images_list(video_path)
+        for frame in pil_frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99
+
+        io_frames, _ = VideoMediaIO(ImageMediaIO()).load_file(Path(video_path))
+        for frame in io_frames:
+            sim = cosine_similarity(normalize_image(np.array(frame)),
+                                    normalize_image(np.array(image)))
+            assert np.sum(np.isnan(sim)) / sim.size < 0.001
+            assert np.nanmean(sim) > 0.99