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.ipynb_checkpoints
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data/EXCV10
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data/MaskedFace
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__pycache__
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f96d2a83",
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"metadata": {},
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"source": [
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"## Question 2 (20 marks)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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||||||
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"id": "6f53891a",
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||||||
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d3bbb31a",
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"metadata": {},
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"outputs": [],
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"source": [
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"n_clusters = 100"
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]
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},
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{
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||||||
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"cell_type": "markdown",
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"id": "28068e50",
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||||||
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"metadata": {},
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||||||
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"source": [
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"Read images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "87dd5c72",
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"metadata": {},
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"outputs": [],
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"source": [
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"im_book = cv2.imread('data/books.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"im_mount = cv2.imread('data/mount_rushmore_1.jpg', cv2.IMREAD_GRAYSCALE)\n",
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"im_notre = cv2.imread('data/notre_dame_1.jpg', cv2.IMREAD_GRAYSCALE)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "79c57454",
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"metadata": {},
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"source": [
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||||||
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"## Generate Clusters"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "2e571277",
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||||||
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_features(image):\n",
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" image = image[:, :, np.newaxis]\n",
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"\n",
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" # Initialize a SIFT detector\n",
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" sift = cv2.SIFT_create()\n",
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"\n",
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" # Detect keypoints and compute descriptors\n",
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" keypoints, descriptors = sift.detectAndCompute(image, None)\n",
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"\n",
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||||||
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" return keypoints, descriptors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "12d042c8",
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||||||
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"metadata": {},
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||||||
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"outputs": [],
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||||||
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"source": [
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||||||
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"from sklearn.cluster import KMeans\n",
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"\n",
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"def get_clusters(keypoints, descriptors, n_clusters=100):\n",
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"\n",
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" # Perform k-means clustering\n",
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" kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n",
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" kmeans.fit(descriptors)\n",
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"\n",
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" # Assign descriptors to clusters\n",
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" clusters = kmeans.predict(descriptors)\n",
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"\n",
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" # Convert keypoints to locations (x, y coordinates)\n",
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" locations = np.array([kp.pt for kp in keypoints], dtype=np.int64)\n",
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" \n",
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" return clusters, locations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b04681a0",
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"metadata": {},
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"outputs": [],
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||||||
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"source": [
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"kpts_book, des_book = get_features(im_book)\n",
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"kpts_mount, des_mount = get_features(im_mount)\n",
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||||||
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"kpts_notre, des_notre = get_features(im_notre)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "70d23c90",
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"metadata": {},
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||||||
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"outputs": [],
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"source": [
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"clusters_book, locations_book = get_clusters(kpts_book, des_book, n_clusters=n_clusters)\n",
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"clusters_mount, locations_mount = get_clusters(kpts_mount, des_mount, n_clusters=n_clusters)\n",
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"clusters_notre, locations_notre = get_clusters(kpts_notre, des_notre, n_clusters=n_clusters)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1518e19f",
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"metadata": {},
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"source": [
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||||||
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"## Method 1 (Two FOR loops)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "04f3901b",
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"metadata": {},
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"outputs": [],
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"source": [
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||||||
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"def m1_generate_bovw_spatial_histogram(im, locations, clusters, division):\n",
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" \"\"\"\n",
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" Create bag of visual words representation of an image based on the division information.\n",
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" \n",
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" Parameters:\n",
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" im (numpy.ndarray): Image array of data type uint8.\n",
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" locations (numpy.ndarray): Array of shape (N, 2) with Cartesian coordinates (x, y).\n",
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" clusters (numpy.ndarray): Array of shape (N,) with quantised cluster id.\n",
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" division (list): List of integers of length 2 indicating division along Y and X axes.\n",
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" \n",
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" Returns:\n",
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" numpy.ndarray: 1-dimensional array representing the BoVW spatial histogram.\n",
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" \"\"\"\n",
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"\n",
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" # Determine the size of each division\n",
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" div_height = im.shape[0] // division[0]\n",
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" div_width = im.shape[1] // division[1]\n",
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" \n",
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||||||
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" # Initialize the histogram\n",
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" num_clusters = np.unique(clusters).size\n",
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" histogram = np.zeros((division[0] * division[1] * num_clusters,), dtype=np.int64)\n",
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"\n",
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||||||
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" # Two FOR loops\n",
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" for div_y in range(division[0]):\n",
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" for div_x in range(division[1]):\n",
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" # Define the bounds of the current division\n",
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" y_start = div_y * div_height\n",
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" y_end = (div_y + 1) * div_height\n",
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||||||
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" x_start = div_x * div_width\n",
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||||||
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" x_end = (div_x + 1) * div_width\n",
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"\n",
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||||||
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" # Find features within the current division\n",
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" div_mask = (locations[:, 1] >= y_start) & (locations[:, 1] < y_end) & \\\n",
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" (locations[:, 0] >= x_start) & (locations[:, 0] < x_end)\n",
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||||||
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" div_locations = locations[div_mask]\n",
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" div_clusters = clusters[div_mask]\n",
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"\n",
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||||||
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" # Calculate the histogram for the current division\n",
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" for i in range(num_clusters):\n",
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" cluster_mask = (div_clusters == i)\n",
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" histogram[div_y * division[1] * num_clusters + div_x * num_clusters + i] = np.sum(cluster_mask)\n",
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||||||
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" \n",
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||||||
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" return histogram"
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||||||
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]
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||||||
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},
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||||||
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{
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||||||
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"cell_type": "markdown",
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||||||
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"id": "0f64e74a",
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||||||
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"metadata": {},
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||||||
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"source": [
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||||||
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"## Method 2 (One FOR loop)"
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||||||
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]
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||||||
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},
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||||||
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{
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||||||
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"cell_type": "code",
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||||||
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"execution_count": 9,
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||||||
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"id": "a597d45d",
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||||||
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"metadata": {},
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||||||
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"outputs": [],
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||||||
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"source": [
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||||||
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"def m2_generate_bovw_spatial_histogram(im, locations, clusters, division):\n",
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"\n",
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" img_shape = np.shape(im)\n",
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"\n",
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" height = img_shape[0]\n",
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" width = img_shape[1]\n",
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"\n",
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" ## Possible Mistakes: Some students swapped x and y\n",
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" div_x = division[1]\n",
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" div_y = division[0]\n",
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"\n",
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" x_size = width / div_x\n",
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" y_size = height / div_y\n",
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"\n",
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" num_divisions = division[0] * division[1]\n",
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"\n",
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" num_clusters = np.max(clusters) + 1\n",
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"\n",
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" histogram = np.zeros(num_clusters * num_divisions)\n",
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"\n",
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" # One FOR loop\n",
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" for i in range(len(locations)):\n",
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" point = locations[i]\n",
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" cluster = clusters[i]\n",
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"\n",
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" x_div = np.ceil((point[0] + 1) / x_size).astype(np.int64) - 1\n",
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" y_div = np.ceil((point[1] + 1) / y_size).astype(np.int64) - 1\n",
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"\n",
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" # Possible Mistakes: Some students miscalculated the boundary condition\n",
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" # x_div = np.ceil(point[0] / x_size).astype(np.int64) - 1\n",
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" # y_div = np.ceil(point[1] / y_size).astype(np.int64) - 1\n",
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"\n",
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" # Calculate the array position\n",
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" div = x_div + (y_div * div_x)\n",
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" array_pos = (div * num_clusters) + cluster\n",
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" \n",
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||||||
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" # Update the histogram\n",
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" histogram[array_pos] = histogram[array_pos] + 1\n",
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"\n",
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" return histogram.astype(int)"
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]
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||||||
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},
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{
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"cell_type": "markdown",
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||||||
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"id": "36f2fe5e",
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||||||
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"metadata": {},
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||||||
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"source": [
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||||||
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"## Put students' implementations here"
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]
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},
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{
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||||||
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"cell_type": "code",
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"execution_count": 10,
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"id": "3804d695",
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||||||
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"metadata": {},
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||||||
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"outputs": [],
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||||||
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"source": [
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||||||
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"# Be careful, some students used a different function name (e.g. bowv rather than bovw)\n",
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"def generate_bovw_spatial_histogram(im, locations, clusters, division):\n",
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" # Determine the number of clusters\n",
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" num_clusters = np.unique(clusters).size\n",
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"\n",
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" # Initialize histogram\n",
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" spatial_histogram = np.zeros(num_clusters * np.prod(division), dtype=np.int64)\n",
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"\n",
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" div_size_y = im.shape[0] // division[0]\n",
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" div_size_x = im.shape[1] // division[1]\n",
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"\n",
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" for div_y in range(division[0]):\n",
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" for div_x in range(division[1]):\n",
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" start_y = div_y * div_size_y\n",
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" end_y = (div_y + 1) * div_size_y if div_y < division[0] - 1 else im.shape[0]\n",
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" start_x = div_x * div_size_x\n",
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" end_x = (div_x + 1) * div_size_x if div_x < division[1] - 1 else im.shape[1]\n",
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" for loc, cluster_id in zip(locations, clusters):\n",
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" x, y = loc\n",
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" if start_y <= y < end_y and start_x <= x < end_x:\n",
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" index = (div_y * division[1] + div_x) * num_clusters + cluster_id\n",
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" spatial_histogram[index] += 1\n",
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" return spatial_histogram"
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]
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},
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{
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||||||
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"cell_type": "markdown",
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||||||
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"id": "0d516e6a",
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||||||
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"metadata": {},
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||||||
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"source": [
|
||||||
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"## Test (Should output ALL PASS)"
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]
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},
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{
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||||||
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"cell_type": "markdown",
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||||||
|
"id": "a269b35f",
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||||||
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"metadata": {},
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||||||
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"source": [
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||||||
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"Restart and Run ALL for each submission"
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|
]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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||||||
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"id": "95d72a5c",
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||||||
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"metadata": {
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||||||
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"scrolled": true
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},
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"outputs": [
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{
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||||||
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Testing division: [1, 1]\n",
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"PASS: Book\n",
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"PASS: Mount\n",
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"PASS: Notre\n",
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"Testing division: [2, 2]\n",
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"PASS: Book\n",
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"PASS: Mount\n",
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"PASS: Notre\n",
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"Testing division: [2, 3]\n",
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"PASS: Book\n",
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"PASS: Mount\n",
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"PASS: Notre\n",
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||||||
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"ALL PASS\n"
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||||||
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]
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||||||
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}
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||||||
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],
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"source": [
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"histograms = []\n",
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||||||
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"for division in [ [1, 1], [2, 2], [2, 3] ]:\n",
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" print('Testing division:', division)\n",
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"\n",
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||||||
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" m1_histogram_book = m1_generate_bovw_spatial_histogram(im_book, locations_book, clusters_book, division)\n",
|
||||||
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" m1_histogram_mount = m1_generate_bovw_spatial_histogram(im_mount, locations_mount, clusters_mount, division)\n",
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||||||
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" m1_histogram_notre = m1_generate_bovw_spatial_histogram(im_notre, locations_notre, clusters_notre, division)\n",
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||||||
|
"\n",
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||||||
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" m2_histogram_book = m2_generate_bovw_spatial_histogram(im_book, locations_book, clusters_book, division)\n",
|
||||||
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" m2_histogram_mount = m2_generate_bovw_spatial_histogram(im_mount, locations_mount, clusters_mount, division)\n",
|
||||||
|
" m2_histogram_notre = m2_generate_bovw_spatial_histogram(im_notre, locations_notre, clusters_notre, division)\n",
|
||||||
|
"\n",
|
||||||
|
" # Students' implementations\n",
|
||||||
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" histogram_book = generate_bovw_spatial_histogram(im_book, locations_book, clusters_book, division)\n",
|
||||||
|
" histogram_mount = generate_bovw_spatial_histogram(im_mount, locations_mount, clusters_mount, division)\n",
|
||||||
|
" histogram_notre = generate_bovw_spatial_histogram(im_notre, locations_notre, clusters_notre, division)\n",
|
||||||
|
" \n",
|
||||||
|
" assert np.allclose(m1_histogram_book, m2_histogram_book)\n",
|
||||||
|
" assert np.allclose(m1_histogram_book, histogram_book)\n",
|
||||||
|
" print(\"PASS: Book\")\n",
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||||||
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"\n",
|
||||||
|
" assert np.allclose(m1_histogram_mount, m2_histogram_mount)\n",
|
||||||
|
" assert np.allclose(m1_histogram_mount, histogram_mount)\n",
|
||||||
|
" print(\"PASS: Mount\")\n",
|
||||||
|
"\n",
|
||||||
|
" assert np.allclose(m1_histogram_notre, m2_histogram_notre)\n",
|
||||||
|
" assert np.allclose(m1_histogram_notre, histogram_notre)\n",
|
||||||
|
" print(\"PASS: Notre\")\n",
|
||||||
|
"\n",
|
||||||
|
" histograms.append( [m1_histogram_book, m1_histogram_mount, m1_histogram_notre] )\n",
|
||||||
|
"print(\"ALL PASS\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "00a11df8",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Save Output"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"id": "ea1fadb1",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"D:\\Anaconda3\\envs\\what\\lib\\site-packages\\numpy\\lib\\npyio.py:521: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
|
||||||
|
" arr = np.asanyarray(arr)\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"np.save('data/question_3_histogram.npy', histograms)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "ddc3036c",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "what",
|
||||||
|
"language": "python",
|
||||||
|
"name": "what"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.8.16"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,224 @@
|
||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "924a2a50",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Question 3 (10 marks)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"id": "2b3d1ba2",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import numpy as np"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"id": "478bad7a",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"points = np.load('data/points.npy').astype(np.uint8)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"id": "e07b0285",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def compute_rotation_matrix(points, theta):\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
" Write a function compute_rotation_matrix(points, theta) to compute the rotation matrix in\n",
|
||||||
|
" homogeneous coordinate system to rotate a shape depicted with 2-dimensional (x,y) coordinates\n",
|
||||||
|
" points with an angle 𝜃 (theta in the definition) in the anticlockwise direction about the centre of the shape.\n",
|
||||||
|
"\n",
|
||||||
|
" Parameters:\n",
|
||||||
|
" points: a 2-dimensional numpy array of data type uint8 with shape 𝑘 × 2. Each row\n",
|
||||||
|
" of points is a Cartesian coordinate (x, y).\n",
|
||||||
|
" \n",
|
||||||
|
" theta: a floating-point number denoting the angle of rotation in degree.\n",
|
||||||
|
" \n",
|
||||||
|
" Returns:\n",
|
||||||
|
" The expected output is a 2-dimensional numpy array of data type float64 with shape 3 × 3.\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
" # Convert theta from degrees to radians\n",
|
||||||
|
" theta_rad = np.radians(theta)\n",
|
||||||
|
"\n",
|
||||||
|
" # Calculate the centre of the shape\n",
|
||||||
|
" centre = np.mean(points, axis=0)\n",
|
||||||
|
"\n",
|
||||||
|
" # Define the translation matrices to move the centre of the shape to the origin and back\n",
|
||||||
|
" translation_to_origin = np.array([[1, 0, -centre[0]],\n",
|
||||||
|
" [0, 1, -centre[1]],\n",
|
||||||
|
" [0, 0, 1]], dtype=np.float64)\n",
|
||||||
|
"\n",
|
||||||
|
" translation_back = np.array([[1, 0, centre[0]],\n",
|
||||||
|
" [0, 1, centre[1]],\n",
|
||||||
|
" [0, 0, 1]], dtype=np.float64)\n",
|
||||||
|
"\n",
|
||||||
|
" # Define the rotation matrix about the origin\n",
|
||||||
|
" rotation = np.array([[np.cos(theta_rad), -np.sin(theta_rad), 0],\n",
|
||||||
|
" [np.sin(theta_rad), np.cos(theta_rad), 0],\n",
|
||||||
|
" [0, 0, 1]], dtype=np.float64)\n",
|
||||||
|
"\n",
|
||||||
|
" # Combine the translation and rotation into a single transformation matrix\n",
|
||||||
|
" rotation_matrix = np.dot(np.dot(translation_back, rotation), translation_to_origin)\n",
|
||||||
|
" \n",
|
||||||
|
" return rotation_matrix"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"id": "16ef4247",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"rotation_matrices = []\n",
|
||||||
|
"\n",
|
||||||
|
"for t in range(0, 365, 5):\n",
|
||||||
|
" rotation_matrices.append( compute_rotation_matrix(points, t) )"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "a130c201",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Save Output"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"id": "23967b5d",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"np.save('data/question_3_rotation_matrices.npy', rotation_matrices)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "a808d8a4",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Put students' implementations here"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"id": "b0836f8d",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def compute_rotation_matrix(points, theta):\n",
|
||||||
|
" # Convert points to float64\n",
|
||||||
|
" points = points.astype(np.float64)\n",
|
||||||
|
" # Calculate centre\n",
|
||||||
|
" centre = np.mean(points, axis=0)\n",
|
||||||
|
" # Compute rotation matrix\n",
|
||||||
|
" rotation_matrix = np.array([[np.cos(np.radians(theta)), -np.sin(np.radians(theta)), 0],\n",
|
||||||
|
" [np.sin(np.radians(theta)), np.cos(np.radians(theta)), 0],\n",
|
||||||
|
" [0, 0, 1]])\n",
|
||||||
|
" # Translation matrix to origin\n",
|
||||||
|
" translation_to_origin = np.array([[1, 0, -centre[0]],\n",
|
||||||
|
" [0, 1, -centre[1]],\n",
|
||||||
|
" [0, 0, 1]])\n",
|
||||||
|
" # Translation matrix to original position\n",
|
||||||
|
" translation_to_centre = np.array([[1, 0, centre[0]],\n",
|
||||||
|
" [0, 1, centre[1]],\n",
|
||||||
|
" [0, 0, 1]])\n",
|
||||||
|
" # Combine transformations with data type float64 \n",
|
||||||
|
" combined_matrix = np.dot(np.dot(translation_to_centre, rotation_matrix), translation_to_origin).astype(np.float64)\n",
|
||||||
|
" return combined_matrix\n",
|
||||||
|
"\n",
|
||||||
|
" return 0"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "73b68192",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Test (Should output ALL PASS)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "1c0a88a6",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Restart and Run ALL for each submission"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"id": "132d734b",
|
||||||
|
"metadata": {
|
||||||
|
"scrolled": false
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"73 PASS\n",
|
||||||
|
"ALL PASS\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"n_pass = 0\n",
|
||||||
|
"for t in range(0, 365, 5):\n",
|
||||||
|
" if np.allclose(compute_rotation_matrix(points, t), rotation_matrices[int(t / 5)]):\n",
|
||||||
|
" n_pass = n_pass + 1\n",
|
||||||
|
"\n",
|
||||||
|
"print(n_pass, \"PASS\")\n",
|
||||||
|
"assert n_pass == len(rotation_matrices)\n",
|
||||||
|
"print(\"ALL PASS\")"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "0fac308a",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "what",
|
||||||
|
"language": "python",
|
||||||
|
"name": "what"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.8.16"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,187 @@
|
||||||
|
import cv2
|
||||||
|
import math
|
||||||
|
import torch
|
||||||
|
import pickle
|
||||||
|
import numpy as np
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
def im2single(im):
|
||||||
|
im = im.astype(np.float32) / 255
|
||||||
|
return im
|
||||||
|
|
||||||
|
def single2im(im):
|
||||||
|
im *= 255
|
||||||
|
im = im.astype(np.uint8)
|
||||||
|
return im
|
||||||
|
|
||||||
|
def load_interest_points(eval_file):
|
||||||
|
"""
|
||||||
|
This function is provided for development and debugging but cannot be used in
|
||||||
|
the final handin. It 'cheats' by generating interest points from known
|
||||||
|
correspondences. It will only work for the 3 image pairs with known
|
||||||
|
correspondences.
|
||||||
|
|
||||||
|
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')
|
||||||
|
scale_factor = 1.0
|
||||||
|
return d['x1'] * scale_factor, d['y1'] * scale_factor, d['x2'] * scale_factor, d['y2'] * scale_factor
|
||||||
|
|
||||||
|
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, (int(x), int(y)), 10, cur_color, -1)
|
||||||
|
return newImg
|
||||||
|
|
||||||
|
def conv3x3(in_planes, out_planes, stride=1):
|
||||||
|
"""
|
||||||
|
3x3 convolution with padding
|
||||||
|
"""
|
||||||
|
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||||
|
|
||||||
|
class BasicBlock(nn.Module):
|
||||||
|
expansion = 1
|
||||||
|
|
||||||
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||||
|
super(BasicBlock, self).__init__()
|
||||||
|
self.conv1 = conv3x3(inplanes, planes, stride)
|
||||||
|
self.bn1 = nn.BatchNorm2d(planes)
|
||||||
|
self.relu = nn.ReLU(inplace=True)
|
||||||
|
self.conv2 = conv3x3(planes, planes)
|
||||||
|
self.bn2 = nn.BatchNorm2d(planes)
|
||||||
|
self.downsample = downsample
|
||||||
|
self.stride = stride
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
out = self.conv1(x)
|
||||||
|
out = self.bn1(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
out = self.conv2(out)
|
||||||
|
out = self.bn2(out)
|
||||||
|
|
||||||
|
if self.downsample is not None:
|
||||||
|
residual = self.downsample(x)
|
||||||
|
else:
|
||||||
|
residual = x
|
||||||
|
|
||||||
|
out += residual
|
||||||
|
out = self.relu(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
class Bottleneck(nn.Module):
|
||||||
|
expansion = 4
|
||||||
|
|
||||||
|
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||||
|
super(Bottleneck, self).__init__()
|
||||||
|
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
|
||||||
|
self.bn1 = nn.BatchNorm2d(planes)
|
||||||
|
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
||||||
|
self.bn2 = nn.BatchNorm2d(planes)
|
||||||
|
self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
|
||||||
|
self.bn3 = nn.BatchNorm2d(planes*4)
|
||||||
|
self.relu = nn.ReLU(inplace=True)
|
||||||
|
self.downsample = downsample
|
||||||
|
self.stride = stride
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
out = self.conv1(x)
|
||||||
|
out = self.bn1(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
|
||||||
|
out = self.conv2(out)
|
||||||
|
out = self.bn2(out)
|
||||||
|
out = self.relu(out)
|
||||||
|
|
||||||
|
out = self.conv3(out)
|
||||||
|
out = self.bn3(out)
|
||||||
|
|
||||||
|
if self.downsample is not None:
|
||||||
|
residual = self.downsample(x)
|
||||||
|
else:
|
||||||
|
residual = x
|
||||||
|
|
||||||
|
out += residual
|
||||||
|
out = self.relu(out)
|
||||||
|
return out
|
||||||
|
|
||||||
|
class ResNet(nn.Module):
|
||||||
|
|
||||||
|
def __init__(self, block, layers, in_channels=3, channels=[16, 32, 64], num_classes=10, flatten=True):
|
||||||
|
super(ResNet, self).__init__()
|
||||||
|
self.name = "resnet"
|
||||||
|
self.flatten = flatten
|
||||||
|
self.channels = channels
|
||||||
|
self.inplanes = channels[0]
|
||||||
|
self.conv1 = nn.Conv2d(in_channels, channels[0], kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
|
||||||
|
self.bn1 = nn.BatchNorm2d(channels[0])
|
||||||
|
self.relu = nn.ReLU(inplace=True)
|
||||||
|
self.layer1 = self._make_layer(block, channels[0], layers[0])
|
||||||
|
self.layer2 = self._make_layer(block, channels[1], layers[1], stride=2)
|
||||||
|
self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
|
||||||
|
self.avgpool = nn.AdaptiveAvgPool2d(1) # global pooling
|
||||||
|
self.fc = nn.Linear(channels[2], num_classes) # global pooling
|
||||||
|
if flatten:
|
||||||
|
self.feature_size = channels[2]*block.expansion
|
||||||
|
|
||||||
|
for m in self.modules():
|
||||||
|
if isinstance(m, nn.Conv2d):
|
||||||
|
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||||
|
m.weight.data.normal_(0, math.sqrt(2. / n))
|
||||||
|
elif isinstance(m, nn.BatchNorm2d):
|
||||||
|
m.weight.data.fill_(1)
|
||||||
|
m.bias.data.zero_()
|
||||||
|
|
||||||
|
def _make_layer(self, block, planes, blocks, stride=1):
|
||||||
|
downsample = None
|
||||||
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||||
|
downsample = nn.Sequential(
|
||||||
|
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
|
||||||
|
nn.BatchNorm2d(planes * block.expansion)
|
||||||
|
)
|
||||||
|
|
||||||
|
layers = []
|
||||||
|
layers.append(block(self.inplanes, planes, stride, downsample))
|
||||||
|
self.inplanes = planes * block.expansion
|
||||||
|
for _ in range(1, blocks):
|
||||||
|
layers.append(block(self.inplanes, planes))
|
||||||
|
|
||||||
|
return nn.Sequential(*layers)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv1(x)
|
||||||
|
x = self.bn1(x)
|
||||||
|
x = self.relu(x)
|
||||||
|
|
||||||
|
x = self.layer1(x)
|
||||||
|
x = self.layer2(x)
|
||||||
|
x = self.layer3(x)
|
||||||
|
|
||||||
|
if self.flatten:
|
||||||
|
x = self.avgpool(x)
|
||||||
|
x = torch.flatten(x, 1)
|
||||||
|
x = self.fc(x)
|
||||||
|
return x
|
||||||
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Reference in New Issue