crazy-idea.py

import cv2
import numpy as np
import csv
import pywavefront

print("Hello World")
def extract_frames(video_path):
    cap = cv2.VideoCapture(video_path)
    frames = []
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(frame)
    cap.release()
    return frames



def find_correspondences(image1, image2):
    # Convert images to grayscale
    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
    
    # Initialize feature detector and descriptor
    sift = cv2.SIFT_create()
    
    # Detect and compute keypoints and descriptors
    keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
    keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)
    
    # Initialize matcher
def find_correspondences(image1, image2):
    # Convert images to grayscale
    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
    
    # Initialize feature detector and descriptor
    sift = cv2.SIFT_create()
    
    # Detect and compute keypoints and descriptors
    keypoints1, descriptors1 = sift.detectAndCompute(gray1, None)
    keypoints2, descriptors2 = sift.detectAndCompute(gray2, None)
    
    # Initialize matcher
    matcher = cv2.BFMatcher(cv2.NORM_L2)
    
    # Match keypoints and find the two nearest neighbors for each keypoint
    matches = matcher.knnMatch(descriptors1, descriptors2, k=2)
    
    # Filter matches using Lowe's ratio test
    ratio_threshold = 0.75
    good_matches = [first for first, second in matches if first.distance < ratio_threshold * second.distance]
    
    # Extract corresponding points
    points1 = np.float32([keypoints1[match.queryIdx].pt for match in good_matches])
    points2 = np.float32([keypoints2[match.trainIdx].pt for match in good_matches])
    
    return points1, points2


def calculate_disparity_map(image1, image2):
    # Convert images to grayscale
    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
    
    # Initialize stereo block matching
    stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
    
    # Compute disparity map
    disparity = stereo.compute(gray1, gray2)
    
    return disparity


def reconstruct_3d_points(disparity_map):
    # Define camera parameters
    focal_length = 1000.0  # Example focal length (adjust according to your setup)
    baseline = 0.1        # Example baseline distance (adjust according to your setup)
    
    # Calculate depth from disparity map
    depth = baseline * focal_length / (disparity_map + 1e-6)
    
    # Generate 3D point cloud
    height, width = disparity_map.shape[:2]
    u, v = np.meshgrid(np.arange(width), np.arange(height))
    points_3d = np.stack([u, v, depth], axis=-1)
    
    return points_3d

def export_points_to_obj(points_3d, output_path):
    with open(output_path, 'w') as file:
        for x in range(points_3d.shape[0]):
            for y in range(points_3d.shape[1]):
                z = points_3d[x, y, 2]
                file.write(f"v {x} {y} {z}\n")

if __name__ == '__main__':
    # Extract frames from a video
    frames = extract_frames('path_to_your_video.mp4')
    
    # Placeholder for 3D points accumulated from all frames
    all_points_3d = None

    # Loop through pairs of consecutive frames
    for i in range(len(frames) - 1):
        # Take two consecutive frames as stereo images
        image1 = frames[i]
        image2 = frames[i + 1]
        
        # Find correspondences between images
        points1, points2 = find_correspondences(image1, image2)
        
        # Calculate disparity map
        disparity_map = calculate_disparity_map(image1, image2)
        
        # Reconstruct 3D points
        points_3d = reconstruct_3d_points(disparity_map)

        # Accumulate 3D points
        if all_points_3d is None:
            all_points_3d = points_3d
        else:
            all_points_3d = np.concatenate((all_points_3d, points_3d), axis=0)
    
    # Export to .obj file
    export_points_to_obj(all_points_3d, 'output.obj')