Local-Feature-Matching-master.zip

# Please do not modify this file. import numpy as np import cv2 import pickle def im2single(im): im = im.astype(np.float32) / 255 return im def single2im(im): im *= 255 im = im.astype(np.uint8) return im def rgb2gray(rgb): """Convert RGB image to grayscale Args: - rgb: A numpy array of shape (m,n,c) representing an RGB image Returns: - gray: A numpy array of shape (m,n) representing the corresponding grayscale image """ return np.dot(rgb[...,:3], [0.299, 0.587, 0.144]) def load_image(path): """ Args: - path: string representing a filepath to an image """ return im2single(cv2.imread(path))[:, :, ::-1] def save_image(path, im): """ Args: - path: - im: A numpy array of shape """ return cv2.imwrite(path, single2im(im.copy())[:, :, ::-1]) def cheat_interest_points(eval_file, scale_factor): """ Args: - eval_file: string representing the file path to the list of known correspondences - scale_factor: Python float representing the scale needed to map from the original image coordinates to the resolution being used for the current experiment. Returns: - x1: A numpy array of shape (k,) containing ground truth x-coordinates of imgA correspondence pts - y1: A numpy array of shape (k,) containing ground truth y-coordinates of imgA correspondence pts - x2: A numpy array of shape (k,) containing ground truth x-coordinates of imgB correspondence pts - y2: A numpy array of shape (k,) containing ground truth y-coordinates of imgB correspondence pts """ with open(eval_file, 'rb') as f: d = pickle.load(f, encoding='latin1') return d['x1'] * scale_factor, d['y1'] * scale_factor, d['x2'] * scale_factor,\ d['y2'] * scale_factor def hstack_images(imgA, imgB): """ Stacks 2 images side-by-side and creates one combined image. Args: - imgA: A numpy array of shape (M,N,3) representing rgb image - imgB: A numpy array of shape (D,E,3) representing rgb image Returns: - newImg: A numpy array of shape (max(M,D), N+E, 3) """ Height = max(imgA.shape[0], imgB.shape[0]) Width = imgA.shape[1] + imgB.shape[1] newImg = np.zeros((Height, Width, 3), dtype=imgA.dtype) newImg[:imgA.shape[0], :imgA.shape[1], :] = imgA newImg[:imgB.shape[0], imgA.shape[1]:, :] = imgB return newImg def show_interest_points(img, X, Y): """ Visualized interest points on an image with random colors Args: - img: A numpy array of shape (M,N,C) - X: A numpy array of shape (k,) containing x-locations of interest points - Y: A numpy array of shape (k,) containing y-locations of interest points Returns: - newImg: A numpy array of shape (M,N,C) showing the original image with colored circles at keypoints plotted on top of it """ newImg = img.copy() for x, y in zip(X.astype(int), Y.astype(int)): cur_color = np.random.rand(3) newImg = cv2.circle(newImg, (x, y), 10, cur_color, -1, cv2.LINE_AA) return newImg def show_correspondence_circles(imgA, imgB, X1, Y1, X2, Y2): """ Visualizes corresponding points between two images by plotting circles at each correspondence location. Corresponding points will have the same random color. Args: - imgA: A numpy array of shape (M,N,3) - imgB: A numpy array of shape (D,E,3) - x1: A numpy array of shape (k,) containing x-locations of keypoints in imgA - y1: A numpy array of shape (k,) containing y-locations of keypoints in imgA - x2: A numpy array of shape (j,) containing x-locations of keypoints in imgB - y2: A numpy array of shape (j,) containing y-locations of keypoints in imgB Returns: - newImg: A numpy array of shape (max(M,D), N+E, 3) """ newImg = hstack_images(imgA, imgB) shiftX = imgA.shape[1] X1 = X1.astype(np.int) Y1 = Y1.astype(np.int) X2 = X2.astype(np.int) Y2 = Y2.astype(np.int) for x1, y1, x2, y2 in zip(X1, Y1, X2, Y2): cur_color = np.random.rand(3) green = (0, 1, 0) newImg = cv2.circle(newImg, (x1, y1), 10, cur_color, -1, cv2.LINE_AA) newImg = cv2.circle(newImg, (x1, y1), 10, green, 2, cv2.LINE_AA) newImg = cv2.circle(newImg, (x2+shiftX, y2), 10, cur_color, -1, cv2.LINE_AA) newImg = cv2.circle(newImg, (x2+shiftX, y2), 10, green, 2, cv2.LINE_AA) return newImg def show_correspondence_lines(imgA, imgB, X1, Y1, X2, Y2, line_colors=None): """ Visualizes corresponding points between two images by drawing a line segment between the two images for each (x1,y1) (x2,y2) pair. Args: - imgA: A numpy array of shape (M,N,3) - imgB: A numpy array of shape (D,E,3) - x1: A numpy array of shape (k,) containing x-locations of keypoints in imgA - y1: A numpy array of shape (k,) containing y-locations of keypoints in imgA - x2: A numpy array of shape (j,) containing x-locations of keypoints in imgB - y2: A numpy array of shape (j,) containing y-locations of keypoints in imgB - line_colors: A numpy array of shape (N x 3) with colors of correspondence lines (optional) Returns: - newImg: A numpy array of shape (max(M,D), N+E, 3) """ newImg = hstack_images(imgA, imgB) shiftX = imgA.shape[1] X1 = X1.astype(np.int) Y1 = Y1.astype(np.int) X2 = X2.astype(np.int) Y2 = Y2.astype(np.int) dot_colors = np.random.rand(len(X1), 3) if line_colors is None: line_colors = dot_colors for x1, y1, x2, y2, dot_color, line_color in zip(X1, Y1, X2, Y2, dot_colors, line_colors): newImg = cv2.circle(newImg, (x1, y1), 5, dot_color, -1) newImg = cv2.circle(newImg, (x2+shiftX, y2), 5, dot_color, -1) newImg = cv2.line(newImg, (x1, y1), (x2+shiftX, y2), line_color, 2, cv2.LINE_AA) return newImg def show_ground_truth_corr(imgA, imgB, corr_file, show_lines=True): """ Show the ground truth correspondeces Args: - imgA: string, representing the filepath to the first image - imgB: string, representing the filepath to the second image - corr_file: filepath to pickle (.pkl) file containing the correspondences - show_lines: boolean, whether to visualize the correspondences as line segments """ imgA = load_image(imgA) imgB = load_image(imgB) with open(corr_file, 'rb') as f: d = pickle.load(f) if show_lines: return show_correspondence_lines(imgA, imgB, d['x1'], d['y1'], d['x2'], d['y2']) else: # show circles return show_correspondence_circles(imgA, imgB, d['x1'], d['y1'], d['x2'], d['y2']) def load_corr_pkl_file(corr_fpath): """ Load ground truth correspondences from a pickle (.pkl) file. """ with open(corr_fpath, 'rb') as f: d = pickle.load(f, encoding='latin1') x1 = d['x1'].squeeze() y1 = d['y1'].squeeze() x2 = d['x2'].squeeze() y2 = d['y2'].squeeze() return x1,y1,x2,y2 def evaluate_correspondence(imgA, imgB, corr_fpath, scale_factor, x1_est, y1_est, x2_est, y2_est, confidences=None, num_req_matches=100): """ Function to evaluate estimated correspondences against ground truth. Args: - imgA: A numpy array of shape (M,N,C) representing a first image - imgB: A numpy array of shape (M,N,C) representing a second image - corr_fpath: string, representing a filepath to a .pkl file containing ground truth correspondences - scale_factor: scale factor on the size of the images - x1_est: A numpy array of shape (k,) containing estimated x-coordinates of imgA correspondence pts - y1_est: A numpy array of shape (k,) containing estimated y-coordinates of imgA correspondence pts - x2_est: A numpy array of shape (k,) containing estimated x-coordinates of imgB correspondence pts - y2_est: A numpy array of shape (k,) containing estimate