Initial media depth project backup

Depth-Anything-V1-main/metric_depth/point_cloud_on_trackbar.py

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+"""
+Born out of Depth Anything V2
+Make sure you have the necessary libraries installed.
+Code by @1ssb
+
+This script processes a video to generate depth maps and corresponding point clouds for each frame.
+The resulting depth maps are saved in a video format, and the point clouds can be interactively generated for selected frames.
+
+Usage:
+    python script.py --video-path path_to_video --input-size 518 --outdir output_directory --encoder vitl --focal-length-x 470.4 --focal-length-y 470.4 --pred-only --grayscale
+
+Arguments:
+    --video-path: Path to the input video.
+    --input-size: Size to which the input frame is resized for depth prediction.
+    --outdir: Directory to save the output video and point clouds.
+    --encoder: Model encoder to use. Choices are ['vits', 'vitb', 'vitl', 'vitg'].
+    --focal-length-x: Focal length along the x-axis.
+    --focal-length-y: Focal length along the y-axis.
+    --pred-only: Only display the prediction without the original frame.
+    --grayscale: Do not apply colorful palette to the depth map.
+"""
+
+import argparse
+import cv2
+import glob
+import matplotlib
+import numpy as np
+import os
+import torch
+import open3d as o3d
+
+from depth_anything_v2.dpt import DepthAnythingV2
+
+
+def main():
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser(description='Depth Anything V2 with Point Cloud Generation')
+    parser.add_argument('--video-path', type=str, required=True, help='Path to the input video.')
+    parser.add_argument('--input-size', type=int, default=518, help='Size to which the input frame is resized for depth prediction.')
+    parser.add_argument('--outdir', type=str, default='./vis_video_depth', help='Directory to save the output video and point clouds.')
+    parser.add_argument('--encoder', type=str, default='vitl', choices=['vits', 'vitb', 'vitl', 'vitg'], help='Model encoder to use.')
+    parser.add_argument('--focal-length-x', default=470.4, type=float, help='Focal length along the x-axis.')
+    parser.add_argument('--focal-length-y', default=470.4, type=float, help='Focal length along the y-axis.')
+    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()
+
+    # Determine the device to use (CUDA, MPS, or CPU)
+    DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
+
+    # Model configuration based on the chosen encoder
+    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]}
+    }
+
+    # Initialize the DepthAnythingV2 model with the specified configuration
+    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()
+
+    # Get the list of video files to process
+    if os.path.isfile(args.video_path):
+        if args.video_path.endswith('txt'):
+            with open(args.video_path, 'r') as f:
+                lines = f.read().splitlines()
+        else:
+            filenames = [args.video_path]
+    else:
+        filenames = glob.glob(os.path.join(args.video_path, '**/*'), recursive=True)
+
+    # Create the output directory if it doesn't exist
+    os.makedirs(args.outdir, exist_ok=True)
+
+    margin_width = 50
+    cmap = matplotlib.colormaps.get_cmap('Spectral_r')
+
+    for k, filename in enumerate(filenames):
+        print(f'Processing {k+1}/{len(filenames)}: {filename}')
+
+        raw_video = cv2.VideoCapture(filename)
+        frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS))
+
+        if args.pred_only:
+            output_width = frame_width
+        else:
+            output_width = frame_width * 2 + margin_width
+
+        output_path = os.path.join(args.outdir, os.path.splitext(os.path.basename(filename))[0] + '.mp4')
+        out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height))
+
+        frame_index = 0
+        frame_data = []
+
+        while raw_video.isOpened():
+            ret, raw_frame = raw_video.read()
+            if not ret:
+                break
+
+            depth = depth_anything.infer_image(raw_frame, args.input_size)
+
+            depth_normalized = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
+            depth_normalized = depth_normalized.astype(np.uint8)
+
+            if args.grayscale:
+                depth_colored = np.repeat(depth_normalized[..., np.newaxis], 3, axis=-1)
+            else:
+                depth_colored = (cmap(depth_normalized)[:, :, :3] * 255)[:, :, ::-1].astype(np.uint8)
+
+            if args.pred_only:
+                out.write(depth_colored)
+            else:
+                split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
+                combined_frame = cv2.hconcat([raw_frame, split_region, depth_colored])
+                out.write(combined_frame)
+
+            frame_data.append((raw_frame, depth, depth_colored))
+            frame_index += 1
+
+        raw_video.release()
+        out.release()
+
+        # Function to create point cloud from depth map
+        def create_point_cloud(raw_frame, depth_map, frame_index):
+            height, width = raw_frame.shape[:2]
+            focal_length_x = args.focal_length_x
+            focal_length_y = args.focal_length_y
+
+            x, y = np.meshgrid(np.arange(width), np.arange(height))
+            x = (x - width / 2) / focal_length_x
+            y = (y - height / 2) / focal_length_y
+            z = np.array(depth_map)
+
+            points = np.stack((np.multiply(x, z), np.multiply(y, z), z), axis=-1).reshape(-1, 3)
+            colors = raw_frame.reshape(-1, 3) / 255.0
+
+            pcd = o3d.geometry.PointCloud()
+            pcd.points = o3d.utility.Vector3dVector(points)
+            pcd.colors = o3d.utility.Vector3dVector(colors)
+
+            pcd_path = os.path.join(args.outdir, f'frame_{frame_index}_point_cloud.ply')
+            o3d.io.write_point_cloud(pcd_path, pcd)
+            print(f'Point cloud saved to {pcd_path}')
+
+        # Interactive window to select a frame and generate its point cloud
+        def on_trackbar(val):
+            frame_index = val
+            raw_frame, depth_map, _ = frame_data[frame_index]
+            create_point_cloud(raw_frame, depth_map, frame_index)
+
+        if frame_data:
+            cv2.namedWindow('Select Frame for Point Cloud')
+            cv2.createTrackbar('Frame', 'Select Frame for Point Cloud', 0, frame_index - 1, on_trackbar)
+
+            while True:
+                key = cv2.waitKey(1) & 0xFF
+                if key == 27:  # Esc key to exit
+                    break
+
+            cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()