Added live webcam versions

.gitignore

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+*.pth
+__pycache__/

run_video_webcam.py

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+import argparse
+import cv2
+import matplotlib
+import numpy as np
+import torch
+from depth_anything_v2.dpt import DepthAnythingV2
+
+
+def get_device():
+    return 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Depth Anything V2 - Live Feed')
+
+    parser.add_argument('--input-size', type=int, default=518)
+    parser.add_argument('--encoder', type=str, default='vits', choices=['vits', 'vitb', 'vitl', 'vitg'])
+    parser.add_argument('--pred-only', dest='pred_only', action='store_true')
+    parser.add_argument('--grayscale', dest='grayscale', action='store_true')
+    parser.add_argument('--camera-index', type=int, default=0)
+
+    args = parser.parse_args()
+
+    DEVICE = get_device()
+
+    # Reverted to your original configuration
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+
+    print(f"Initializing model on {DEVICE}...")
+    depth_anything = DepthAnythingV2(**model_configs[args.encoder])
+    depth_anything.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{args.encoder}.pth', map_location='cpu'))
+    depth_anything = depth_anything.to(DEVICE).eval()
+
+    # Initialize webcam
+    raw_video = cv2.VideoCapture(args.camera_index)
+    if not raw_video.isOpened():
+        raise RuntimeError(f'Unable to open webcam at index {args.camera_index}.')
+
+    margin_width = 50
+    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
+
+    print("Starting live feed. Press 'q' to exit.")
+
+    while raw_video.isOpened():
+        ret, raw_frame = raw_video.read()
+        if not ret:
+            break
+
+        # Inference
+        depth = depth_anything.infer_image(raw_frame, args.input_size)
+
+        # Normalize and colorize based on your original logic
+        depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+        depth = depth.astype(np.uint8)
+
+        if args.grayscale:
+            depth = np.repeat(depth[..., np.newaxis], 3, axis=-1)
+        else:
+            depth = (cmap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
+
+        # Combine or isolate frames
+        if args.pred_only:
+            combined_frame = depth
+        else:
+            frame_height = raw_frame.shape[0]
+            split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
+            combined_frame = cv2.hconcat([raw_frame, split_region, depth])
+
+        # Display window
+        cv2.imshow('Depth Anything V2 - Live', combined_frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    raw_video.release()
+    cv2.destroyAllWindows()

run_webcam.py

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+import argparse
+import cv2
+import glob
+import matplotlib
+import numpy as np
+import os
+import torch
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+
+def get_device():
+    return 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+
+
+def load_model(encoder, target_device):
+    model_configs = {
+        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
+    }
+
+    model = DepthAnythingV2(**model_configs[encoder])
+    model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location='cpu'))
+    return model.to(target_device).eval()
+
+
+def render_depth(depth, colormap, grayscale):
+    depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+    depth = depth.astype(np.uint8)
+
+    if grayscale:
+        return np.repeat(depth[..., np.newaxis], 3, axis=-1)
+
+    return (colormap(depth)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
+
+
+def combine_frames(raw_frame, depth_frame, pred_only):
+    if pred_only:
+        return depth_frame
+
+    split_region = np.ones((raw_frame.shape[0], 50, 3), dtype=np.uint8) * 255
+    return cv2.hconcat([raw_frame, split_region, depth_frame])
+
+
+def get_filenames(img_path):
+    if os.path.isfile(img_path):
+        if img_path.endswith('txt'):
+            with open(img_path, 'r', encoding='utf-8') as handle:
+                return handle.read().splitlines()
+
+        return [img_path]
+
+    return glob.glob(os.path.join(img_path, '**/*'), recursive=True)
+
+
+def run_on_webcam(model, input_size, colormap, grayscale, pred_only, camera_index):
+    raw_video = cv2.VideoCapture(camera_index)
+    if not raw_video.isOpened():
+        raise RuntimeError(f'Unable to open webcam at index {camera_index}.')
+
+    print("Starting live feed. Press 'q' to exit.")
+
+    try:
+        while raw_video.isOpened():
+            ret, raw_frame = raw_video.read()
+            if not ret:
+                break
+
+            depth = model.infer_image(raw_frame, input_size)
+            depth_frame = render_depth(depth, colormap, grayscale)
+            combined_frame = combine_frames(raw_frame, depth_frame, pred_only)
+
+            cv2.imshow('Depth Anything V2 - Live', combined_frame)
+
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+    finally:
+        raw_video.release()
+        cv2.destroyAllWindows()
+
+
+def run_on_files(model, input_filenames, input_size, outdir, colormap, grayscale, pred_only):
+    os.makedirs(outdir, exist_ok=True)
+
+    for k, filename in enumerate(input_filenames):
+        print(f'Progress {k+1}/{len(input_filenames)}: {filename}')
+
+        raw_image = cv2.imread(filename)
+        if raw_image is None:
+            print(f'Skipping unreadable file: {filename}')
+            continue
+
+        depth = model.infer_image(raw_image, input_size)
+        depth_frame = render_depth(depth, colormap, grayscale)
+        output_frame = combine_frames(raw_image, depth_frame, pred_only)
+
+        cv2.imwrite(os.path.join(outdir, os.path.splitext(os.path.basename(filename))[0] + '.png'), output_frame)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Depth Anything V2')
+
+    parser.add_argument('--img-path', type=str)
+    parser.add_argument('--input-size', type=int, default=518)
+    parser.add_argument('--outdir', type=str, default='./vis_depth')
+    parser.add_argument('--webcam', action='store_true', help='run on a live webcam feed instead of image files')
+    parser.add_argument('--camera-index', type=int, default=0, help='OpenCV camera index to use with --webcam')
+
+    parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'])
+
+    parser.add_argument('--pred-only', dest='pred_only', action='store_true', help='only display the prediction')
+    parser.add_argument('--grayscale', dest='grayscale', action='store_true', help='do not apply colorful palette')
+
+    args = parser.parse_args()
+
+    if not args.webcam and not args.img_path:
+        parser.error('--img-path is required unless --webcam is set')
+
+    device = get_device()
+    print(f'Initializing model on {device}...')
+    depth_anything = load_model(args.encoder, device)
+
+    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
+
+    if args.webcam:
+        run_on_webcam(depth_anything, args.input_size, cmap, args.grayscale, args.pred_only, args.camera_index)
+    else:
+        filenames = get_filenames(args.img_path)
+        run_on_files(depth_anything, filenames, args.input_size, args.outdir, cmap, args.grayscale, args.pred_only)