mirror of
https://github.com/20kaushik02/CSE515_MWDB_Project.git
synced 2025-12-06 09:34:07 +00:00
838 lines
222 KiB
Plaintext
838 lines
222 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pymongo import MongoClient\n",
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"\n",
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"# Connect to local MongoDB database\n",
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"client = MongoClient(\"mongodb://localhost:27017\")\n",
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"\n",
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"db = client[\"phase_2_madhura\"]\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create/access feature descriptor collection\n",
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"fd_collection = db[\"fd_collection_madhura\"]\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import cv2\n",
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"import numpy as np\n",
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"from scipy.stats import skew\n",
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"\n",
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"import torch\n",
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"import torchvision.transforms as transforms\n",
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"\n",
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"import torchvision.datasets as datasets\n",
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"\n",
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"import os\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"%matplotlib inline"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"root_directory = \"C:/Users/Shubhi/OneDrive/Desktop/Fall 2023/MWDB/phase1\"\n",
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"# Load as tensors of shape (channels, (img_shape))\n",
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"def datasetTransform(image):\n",
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" \n",
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" return transforms.Compose(\n",
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" [\n",
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" transforms.ToTensor() # ToTensor by default scales to [0,1] range, the input range for ResNet\n",
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" ]\n",
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" )(image)\n",
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"\n",
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"\n",
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"dataset = datasets.Caltech101(\n",
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" root=root_directory,\n",
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" download=False, # True if you wish to download for first time\n",
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" transform=datasetTransform,\n",
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")\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Class transform to partition image into rows x cols grid\n",
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"\n",
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"class GridPartition:\n",
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" def __init__(self, rows, cols):\n",
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" self.rows = rows\n",
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" self.cols = cols\n",
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"\n",
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" def __call__(self, img):\n",
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" img_width, img_height = img.size()[1:] # first element is channel\n",
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" cell_width = img_width // self.cols\n",
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" cell_height = img_height // self.rows\n",
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"\n",
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" grids = []\n",
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" for i in range(self.rows):\n",
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" for j in range(self.cols):\n",
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" left = j * cell_width\n",
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" right = left + cell_width\n",
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"\n",
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" top = i * cell_height\n",
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" bottom = top + cell_height\n",
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"\n",
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" # Slice out\n",
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" grid = img[:, left:right, top:bottom]\n",
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" grids.append(grid)\n",
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"\n",
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" return grids\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_color_moments(image):\n",
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" image = np.array(image) # Convert tensor to NumPy array\n",
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" moments = []\n",
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"\n",
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" for channel in range(3): # Iterate over RGB channels\n",
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" channel_data = image[:, :, channel]\n",
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" mean = np.mean(channel_data)\n",
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" std_dev = np.std(channel_data)\n",
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"\n",
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" # Avoiding NaN values\n",
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" skew_cubed = np.mean((channel_data - mean) ** 3)\n",
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" if skew_cubed > 0:\n",
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" skew = math.pow(skew_cubed, float(1) / 3)\n",
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" elif skew_cubed < 0:\n",
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" skew = -math.pow(abs(skew_cubed), float(1) / 3)\n",
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" else:\n",
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" skew = 0\n",
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"\n",
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" moments.append([mean, std_dev, skew])\n",
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"\n",
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" return moments\n",
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"\n",
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"\n",
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"# Iterate over grid cells and return as 1-d array for easier resizing by torch\n",
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"def compute_color_moments_for_grid(grid):\n",
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" color_moments = [compute_color_moments(grid_cell) for grid_cell in grid]\n",
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" return np.array(color_moments).flatten()\n",
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"\n",
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"\n",
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"def combine_color_moments(grid_color_moments):\n",
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" return torch.Tensor(grid_color_moments).view(\n",
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" 10, 10, 3, 3\n",
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" ) # resize as needed: 10x10 grid, 3 channels per cell, 3 moments per channel\n",
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"\n",
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"\n",
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"CM_transform = transforms.Compose(\n",
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" [\n",
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" transforms.Resize((100, 300)), # resize to H:W=100:300\n",
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" GridPartition(\n",
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" rows=10, cols=10\n",
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" ), # partition into grid of 10 rows, 10 columns as a list\n",
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" compute_color_moments_for_grid,\n",
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" combine_color_moments,\n",
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" ]\n",
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")\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def compute_gradient_histogram(grid_cell):\n",
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" histograms = []\n",
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"\n",
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" # Convert grid cell to NumPy array\n",
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" grid_array = np.array(grid_cell, dtype=np.float32)\n",
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" grid_array = grid_array.reshape(\n",
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" grid_array.shape[1], grid_array.shape[2]\n",
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" ) # ignore extra dimension\n",
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"\n",
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" # Compute the gradient using first-order central differences\n",
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" dx = cv2.Sobel(\n",
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" grid_array, cv2.CV_32F, dx=1, dy=0, ksize=1\n",
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" ) # first order x derivative = [-1, 0, 1]\n",
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" dy = cv2.Sobel(\n",
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" grid_array, cv2.CV_32F, dx=0, dy=1, ksize=1\n",
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" ) # first order y derivative = [-1, 0, 1]^T\n",
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"\n",
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" # Compute magnitude and direction of gradients\n",
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" magnitude = np.sqrt(dx**2 + dy**2)\n",
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" direction = np.arctan2(dy, dx) * 180 / np.pi # in degrees\n",
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"\n",
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" # Compute HOG - 9 bins, counted across the range of -180 to 180 degrees, weighted by gradient magnitude\n",
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" histogram, _ = np.histogram(direction, bins=9, range=(-180, 180), weights=magnitude)\n",
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"\n",
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" histograms.append(histogram)\n",
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"\n",
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" return histograms\n",
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"\n",
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"\n",
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"def compute_histograms_for_grid(grid):\n",
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" histograms = [compute_gradient_histogram(grid_cell) for grid_cell in grid]\n",
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" return np.array(histograms).flatten()\n",
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"\n",
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"\n",
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"def combine_histograms(grid_histograms):\n",
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" return torch.Tensor(grid_histograms).view(10, 10, 9)\n",
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"\n",
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"\n",
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"HOG_transform = transforms.Compose(\n",
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" [\n",
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" transforms.Grayscale(num_output_channels=1), # grayscale transform\n",
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" transforms.Resize((100, 300)), # resize to H:W=100:300\n",
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" GridPartition(\n",
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" rows=10, cols=10\n",
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" ), # partition into grid of 10 rows, 10 columns as a list\n",
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" compute_histograms_for_grid,\n",
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" combine_histograms,\n",
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" ]\n",
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")\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import torchvision.models as models\n",
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"\n",
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"# Load model\n",
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"model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)\n",
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"\n",
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"# use GPU (Nvidia)\n",
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"if torch.cuda.is_available():\n",
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" dev = torch.device(\"cuda\")\n",
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" torch.cuda.empty_cache()\n",
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"else:\n",
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" dev = torch.device(\"cpu\")\n",
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"\n",
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"model = model.to(dev)\n",
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"model.eval() # switch to inference mode - important! since we're using pre-trained model\n",
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"\n",
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"\n",
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"# Feature extractor for all layers at once\n",
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"class FeatureExtractor(torch.nn.Module):\n",
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" def __init__(self, model, layers):\n",
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" super().__init__()\n",
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" self.model = model\n",
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" self.layers = layers\n",
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" self._features = {layer: None for layer in layers} # store layer outputs here\n",
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"\n",
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" # Create hooks for all specified layers at once\n",
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" for layer_id in layers:\n",
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" layer = dict(self.model.named_modules())[\n",
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" layer_id\n",
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" ] # get actual layer in the model\n",
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" layer.register_forward_hook(\n",
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" self.save_outputs_hook(layer_id)\n",
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" ) # register feature extractor hook on layer\n",
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"\n",
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" # Hook to save output of layer\n",
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" def save_outputs_hook(self, layer_id):\n",
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" def fn(_module, _input, output):\n",
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" self._features[layer_id] = output\n",
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" return fn\n",
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"\n",
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" # Forward pass returns extracted features\n",
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" def forward(self, input):\n",
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" _ = self.model(input)\n",
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" return self._features\n",
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"\n",
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"\n",
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"def resnet_extractor(image):\n",
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" resized_image = (\n",
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" torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())\n",
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" .view(1, 3, 224, 224)\n",
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" .to(dev)\n",
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" )\n",
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"\n",
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" #complete_resnet_features = model.predict(image)\n",
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" \n",
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" # Attach all hooks on model and extract features\n",
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" resnet_features = FeatureExtractor(model=model, layers=[\"avgpool\", \"layer3\", \"fc\"])\n",
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" features = resnet_features(resized_image)\n",
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"\n",
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" avgpool_2048 = features[\"avgpool\"]\n",
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" # Reshape the vector into row pairs of elements and average across rows\n",
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" avgpool_1024_fd = torch.mean(avgpool_2048.view(-1, 2), axis=1)\n",
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"\n",
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" layer3_1024_14_14 = features[\"layer3\"]\n",
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" # Reshape the vector into 1024 rows of 196 elements and average across rows\n",
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" layer3_1024_fd = torch.mean(layer3_1024_14_14.view(1024, -1), axis=1)\n",
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"\n",
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" fc_1000_fd = features[\"fc\"].view(1000)\n",
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"\n",
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"\n",
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" return (\n",
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" avgpool_1024_fd.detach().cpu().tolist(),\n",
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" layer3_1024_fd.detach().cpu().tolist(),\n",
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" fc_1000_fd.detach().cpu().tolist(),\n",
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" #complete_resnet_features.detach().cpu().tolist(),\n",
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" )\n"
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torchvision.models as models\n",
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"import torch\n",
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"import numpy as np\n",
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"from torchvision import transforms\n",
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"\n",
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"# Load model\n",
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"model = models.resnet50(pretrained=True)\n",
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"\n",
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"# use GPU (Nvidia)\n",
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"if torch.cuda.is_available():\n",
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" dev = torch.device(\"cuda\")\n",
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"else:\n",
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" dev = torch.device(\"cpu\")\n",
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"\n",
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"model = model.to(dev)\n",
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"model.eval() # switch to inference mode - important! since we're using pre-trained model\n",
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"\n",
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"def complete_resnet_extractor(image):\n",
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" #resized_image = transforms.Resize((224, 224))(image)\n",
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" #normalized_image = transforms.ToTensor()(resized_image).unsqueeze(0).to(dev)\n",
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" resized_image = (\n",
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" torch.Tensor(np.array(transforms.Resize((224, 224))(image)).flatten())\n",
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" .view(1, 3, 224, 224)\n",
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" .to(dev)\n",
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" )\n",
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"\n",
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" with torch.no_grad():\n",
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" features = model(resized_image)\n",
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"\n",
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" return features.detach().cpu().tolist()\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def get_all_fd(image_id):\n",
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" img, label = dataset[image_id]\n",
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"\n",
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" img_shape = np.array(img).shape\n",
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"\n",
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" if img_shape[0] >= 3:\n",
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" true_channels = 3\n",
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" else:\n",
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" # stacking the grayscale channel on itself thrice to get RGB dimensions\n",
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" img = torch.tensor(np.stack((np.array(img[0, :, :]),) * 3, axis=0))\n",
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" true_channels = 1\n",
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"\n",
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" cm_fd = CM_transform(img).tolist()\n",
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" hog_fd = HOG_transform(img).tolist()\n",
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" avgpool_1024_fd, layer3_1024_fd, fc_1000_fd = resnet_extractor(img)\n",
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" resnet_fd = complete_resnet_extractor(img)\n",
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"\n",
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" return {\n",
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" \"image_id\": image_id,\n",
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" \"true_label\": label,\n",
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" \"true_channels\": true_channels,\n",
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" \"cm_fd\": cm_fd,\n",
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" \"hog_fd\": hog_fd,\n",
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" \"avgpool_fd\": avgpool_1024_fd,\n",
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" \"layer3_fd\": layer3_1024_fd,\n",
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" \"fc_fd\": fc_1000_fd,\n",
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" \"resnet_fd\": resnet_fd,\n",
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" }\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Feature Extraction for full Database"
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\nstart = 0\\nstop = len(dataset)\\nstep = 2 # even-numbered image IDs only\\n\\nfor idx in range(start, stop, step):\\n image_fd = get_all_fd(idx)\\n\\n # Store to collection (update if existing)\\n fd_collection.update_one(\\n {\"image_id\": idx},\\n {\"$set\": image_fd},\\n upsert=True,\\n )\\n'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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"start = 0\n",
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"stop = len(dataset)\n",
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"step = 2 # even-numbered image IDs only\n",
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"\n",
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"for idx in range(start, stop, step):\n",
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" image_fd = get_all_fd(idx)\n",
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"\n",
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" # Store to collection (update if existing)\n",
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" fd_collection.update_one(\n",
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" {\"image_id\": idx},\n",
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" {\"$set\": image_fd},\n",
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" upsert=True,\n",
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" )\n",
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"\"\"\"\n"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"\n",
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"def euclidean_distance_measure(img_1_fd, img_2_fd):\n",
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" img_1_fd_reshaped = img_1_fd.flatten()\n",
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" img_2_fd_reshaped = img_2_fd.flatten()\n",
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"\n",
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" # Calculate Euclidean distance\n",
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" return math.dist(img_1_fd_reshaped, img_2_fd_reshaped)\n"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def cosine_distance_measure(img_1_fd, img_2_fd):\n",
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" img_1_fd_reshaped = img_1_fd.flatten()\n",
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" img_2_fd_reshaped = img_2_fd.flatten()\n",
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"\n",
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" # Calculate dot product\n",
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" dot_product = np.dot(img_1_fd_reshaped, img_2_fd_reshaped.T)\n",
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"\n",
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" # Calculate magnitude (L2 norm) of the feature descriptor\n",
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" magnitude1 = np.linalg.norm(img_1_fd_reshaped)\n",
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" magnitude2 = np.linalg.norm(img_2_fd_reshaped)\n",
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"\n",
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" # Calculate cosine distance (similarity is higher => distance should be lower, so subtract from 1)\n",
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" cosine_similarity = dot_product / (magnitude1 * magnitude2)\n",
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" return 1 - cosine_similarity\n"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from scipy.stats import pearsonr\n",
|
|
"\n",
|
|
"def pearson_distance_measure(img_1_fd, img_2_fd):\n",
|
|
" # Replace nan with 0 (color moments)\n",
|
|
" img_1_fd_reshaped = img_1_fd.flatten()\n",
|
|
" img_2_fd_reshaped = img_2_fd.flatten()\n",
|
|
"\n",
|
|
" # Invert and scale in half to fit the actual range [-1, 1] into the new range [0, 1]\n",
|
|
" # such that lower distance implies more similarity\n",
|
|
" return 0.5 * (1 - pearsonr(img_1_fd_reshaped, img_2_fd_reshaped).statistic)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"valid_feature_models = [\"cm\", \"hog\", \"avgpool\", \"layer3\", \"fc\", \"resnet\"]\n",
|
|
"valid_distance_measures = {\n",
|
|
" \"euclidean\": euclidean_distance_measure,\n",
|
|
" \"cosine\": cosine_distance_measure,\n",
|
|
" \"pearson\": pearson_distance_measure,\n",
|
|
"}\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#funtion to display similar images\n",
|
|
"def show_similar_images(target_image_id, k, feature_model, distance_measure, save_plots=False):\n",
|
|
" print(\n",
|
|
" \"Showing {} similar images for image ID {}, using {} for {} feature descriptor...\".format(\n",
|
|
" k, target_image_id, distance_measure.__name__, feature_model\n",
|
|
" )\n",
|
|
" )\n",
|
|
"\n",
|
|
" # store target_image itself\n",
|
|
" min_dists = {target_image_id: 0}\n",
|
|
"\n",
|
|
" if target_image_id % 2 == 0:\n",
|
|
" # Get target image's feature descriptors from database\n",
|
|
" target_image = fd_collection.find_one({\"image_id\": target_image_id})\n",
|
|
" else:\n",
|
|
" # Calculate target image's feature descriptors\n",
|
|
" target_image = get_all_fd(target_image_id)\n",
|
|
"\n",
|
|
" target_image_fd = np.array(target_image[feature_model + \"_fd\"])\n",
|
|
"\n",
|
|
" assert (\n",
|
|
" feature_model in valid_feature_models\n",
|
|
" ), \"feature_model should be one of \" + str(valid_feature_models)\n",
|
|
"\n",
|
|
" assert (\n",
|
|
" distance_measure in valid_distance_measures.values()\n",
|
|
" ), \"distance_measure should be one of \" + str(list(valid_distance_measures.keys()))\n",
|
|
"\n",
|
|
" # only RGB for non RGB images\n",
|
|
" if feature_model != \"hog\":\n",
|
|
" all_images = fd_collection.find({\"true_channels\": 3})\n",
|
|
" else:\n",
|
|
" all_images = fd_collection.find()\n",
|
|
"\n",
|
|
" for cur_img in all_images:\n",
|
|
" cur_img_id = cur_img[\"image_id\"]\n",
|
|
" # skip target itself\n",
|
|
" if cur_img_id == target_image_id:\n",
|
|
" continue\n",
|
|
" cur_img_fd = np.array(cur_img[feature_model + \"_fd\"])\n",
|
|
" cur_dist = distance_measure(\n",
|
|
" cur_img_fd,\n",
|
|
" target_image_fd,\n",
|
|
" )\n",
|
|
"\n",
|
|
" # store first k images irrespective of distance (so that we store no more than k minimum distances)\n",
|
|
" if len(min_dists) < k + 1:\n",
|
|
" min_dists[cur_img_id] = cur_dist\n",
|
|
"\n",
|
|
" # if lower distance:\n",
|
|
" elif cur_dist < max(min_dists.values()):\n",
|
|
" # add to min_dists\n",
|
|
" min_dists.update({cur_img_id: cur_dist})\n",
|
|
" # remove greatest distance by index\n",
|
|
" min_dists.pop(max(min_dists, key=min_dists.get))\n",
|
|
"\n",
|
|
" min_dists = dict(sorted(min_dists.items(), key=lambda item: item[1]))\n",
|
|
"\n",
|
|
" fig, axs = plt.subplots(1, k + 1, figsize=(32, 12))\n",
|
|
" for idx, (img_id, distance) in enumerate(min_dists.items()):\n",
|
|
" cur_img, _cur_label = dataset[img_id]\n",
|
|
" axs[idx].imshow(transforms.ToPILImage()(cur_img))\n",
|
|
" if idx == 0:\n",
|
|
" axs[idx].set_title(f\"Target image\")\n",
|
|
" else:\n",
|
|
" axs[idx].set_title(f\"Distance: {round(distance, 3)}\")\n",
|
|
" axs[idx].axis(\"off\")\n",
|
|
"\n",
|
|
" if save_plots:\n",
|
|
" plt.savefig(\n",
|
|
" f\"Plots/Image_{target_image_id}_{feature_model}_{distance_measure.__name__}_k{k}.png\"\n",
|
|
" )\n",
|
|
"\n",
|
|
" plt.show()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'\\nselected_image_id = int(input(\"Enter image ID: \"))\\nsample_image, sample_label = dataset[selected_image_id]\\nplt.imshow(transforms.ToPILImage()(sample_image))\\nplt.show()\\n\\nk = int(input(\"Enter value of k: \"))\\nif k < 1:\\n raise ValueError(\"k should be positive integer\")\\n\\nselected_feature_model = str(\\n input(\"Enter feature model - one of \" + str(valid_feature_models))\\n)\\n\\nselected_distance_measure = valid_distance_measures[str(\\n input(\"Enter distance measure - one of \" + str(list(valid_distance_measures.keys())))\\n)]\\nshow_similar_images(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)\\n'"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#TASK 0\n",
|
|
"\"\"\"\n",
|
|
"selected_image_id = int(input(\"Enter image ID: \"))\n",
|
|
"sample_image, sample_label = dataset[selected_image_id]\n",
|
|
"plt.imshow(transforms.ToPILImage()(sample_image))\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"k = int(input(\"Enter value of k: \"))\n",
|
|
"if k < 1:\n",
|
|
" raise ValueError(\"k should be positive integer\")\n",
|
|
"\n",
|
|
"selected_feature_model = str(\n",
|
|
" input(\"Enter feature model - one of \" + str(valid_feature_models))\n",
|
|
")\n",
|
|
"\n",
|
|
"selected_distance_measure = valid_distance_measures[str(\n",
|
|
" input(\"Enter distance measure - one of \" + str(list(valid_distance_measures.keys())))\n",
|
|
")]\n",
|
|
"show_similar_images(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)\n",
|
|
"\"\"\"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'\\ndataset_label = int(input(\"Enter label no. (between 0 to 100): \"))\\nif dataset_label < 0 or dataset_label>100:\\n raise ValueError(\"dataset_label should be between 0 and 100 only\")\\nselected_image_id=-1\\nquery = {\"true_label\": dataset_label} \\ncursor = fd_collection.find(query).limit(1)\\nselected_image_id=cursor[0][\"image_id\"]\\n\\nsample_image, sample_label = dataset[selected_image_id]\\nplt.imshow(transforms.ToPILImage()(sample_image))\\nplt.show()\\n\\nk = int(input(\"Enter value of k: \"))\\nif k < 1:\\n raise ValueError(\"k should be positive integer\")\\n\\nselected_feature_model = str(\\n input(\"Enter feature model - one of \" + str(valid_feature_models))\\n)\\n\\nselected_distance_measure = valid_distance_measures[str(\\n input(\"Enter distance measure - one of \" + str(list(valid_distance_measures.keys())))\\n)]\\n\\nshow_similar_images(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)\\n'"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"#TASK 1-we'll be using pranav's code (mean method)\n",
|
|
"\"\"\"\n",
|
|
"dataset_label = int(input(\"Enter label no. (between 0 to 100): \"))\n",
|
|
"if dataset_label < 0 or dataset_label>100:\n",
|
|
" raise ValueError(\"dataset_label should be between 0 and 100 only\")\n",
|
|
"selected_image_id=-1\n",
|
|
"query = {\"true_label\": dataset_label} \n",
|
|
"cursor = fd_collection.find(query).limit(1)\n",
|
|
"selected_image_id=cursor[0][\"image_id\"]\n",
|
|
"\n",
|
|
"sample_image, sample_label = dataset[selected_image_id]\n",
|
|
"plt.imshow(transforms.ToPILImage()(sample_image))\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"k = int(input(\"Enter value of k: \"))\n",
|
|
"if k < 1:\n",
|
|
" raise ValueError(\"k should be positive integer\")\n",
|
|
"\n",
|
|
"selected_feature_model = str(\n",
|
|
" input(\"Enter feature model - one of \" + str(valid_feature_models))\n",
|
|
")\n",
|
|
"\n",
|
|
"selected_distance_measure = valid_distance_measures[str(\n",
|
|
" input(\"Enter distance measure - one of \" + str(list(valid_distance_measures.keys())))\n",
|
|
")]\n",
|
|
"\n",
|
|
"show_similar_images(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)\n",
|
|
"\"\"\"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Displaying 5 similar labels for image ID 1, using cosine_distance_measure for resnet feature descriptor...\n",
|
|
"Label of target image: 0\n",
|
|
"Label: 1 ; distance: 0.08507879924049078\n",
|
|
"Label: 83 ; distance: 0.39863399244364184\n",
|
|
"Label: 96 ; distance: 0.4092512330836007\n",
|
|
"Label: 100 ; distance: 0.42865075274386166\n",
|
|
"Label: 31 ; distance: 0.4400374717970962\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#TASK 2a\n",
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"\n",
|
|
"#funtion to display similar labels\n",
|
|
"def show_similar_labels(target_image_id, k, feature_model, distance_measure, save_plots=False):\n",
|
|
" print(\n",
|
|
" \"Displaying {} similar labels for image ID {}, using {} for {} feature descriptor...\".format(\n",
|
|
" k, target_image_id, distance_measure.__name__, feature_model\n",
|
|
" #k, target_image_id, distance_measure, feature_model\n",
|
|
" )\n",
|
|
" )\n",
|
|
"\n",
|
|
" # store target_image itself\n",
|
|
" min_dists = {target_image_id: 0}\n",
|
|
" \n",
|
|
"\n",
|
|
" if target_image_id % 2 == 0:\n",
|
|
" # Get target image's feature descriptors from database\n",
|
|
" target_image = fd_collection.find_one({\"image_id\": target_image_id})\n",
|
|
" else:\n",
|
|
" # Calculate target image's feature descriptors\n",
|
|
" target_image = get_all_fd(target_image_id)\n",
|
|
" \n",
|
|
" #cursor = fd_collection.find({\"image_id\": target_image_id})\n",
|
|
" #print(\"cursor\", cursor)\n",
|
|
" label=target_image[\"true_label\"]\n",
|
|
" print(\"Label of target image: \", label)\n",
|
|
" label_dict = {target_image_id: label}\n",
|
|
" \n",
|
|
" target_image_fd = np.array(target_image[feature_model + \"_fd\"])\n",
|
|
"\n",
|
|
" assert (\n",
|
|
" feature_model in valid_feature_models\n",
|
|
" ), \"feature_model should be one of \" + str(valid_feature_models)\n",
|
|
"\n",
|
|
" assert (\n",
|
|
" distance_measure in valid_distance_measures.values()\n",
|
|
" ), \"distance_measure should be one of \" + str(list(valid_distance_measures.keys()))\n",
|
|
"\n",
|
|
" # only RGB for non RGB images\n",
|
|
" if feature_model != \"hog\":\n",
|
|
" all_images = fd_collection.find({\"true_channels\": 3})\n",
|
|
" else:\n",
|
|
" all_images = fd_collection.find()\n",
|
|
"\n",
|
|
" for cur_img in all_images:\n",
|
|
" cur_img_id = cur_img[\"image_id\"]\n",
|
|
" # skip target itself\n",
|
|
" if cur_img_id == target_image_id:\n",
|
|
" continue\n",
|
|
" cur_img_fd = np.array(cur_img[feature_model + \"_fd\"])\n",
|
|
" cur_dist = distance_measure(\n",
|
|
" cur_img_fd,\n",
|
|
" target_image_fd,\n",
|
|
" )\n",
|
|
" cursor = fd_collection.find({\"image_id\": cur_img_id})\n",
|
|
" label=cursor[0][\"true_label\"]\n",
|
|
"\n",
|
|
" # store first k images irrespective of distance (so that we store no more than k minimum distances)\n",
|
|
" if len(min_dists) < k + 1 and label not in label_dict.values():\n",
|
|
" min_dists[cur_img_id] = cur_dist\n",
|
|
" label_dict[cur_img_id] = label\n",
|
|
"\n",
|
|
" # if lower distance:\n",
|
|
" elif cur_dist < max(min_dists.values()) and label not in label_dict.values():\n",
|
|
" # add to min_dists\n",
|
|
" min_dists.update({cur_img_id: cur_dist})\n",
|
|
" label_dict.update({cur_img_id: label})\n",
|
|
" # remove greatest distance by index\n",
|
|
" pop_key=max(min_dists, key=min_dists.get)\n",
|
|
" min_dists.pop(pop_key)\n",
|
|
" label_dict.pop(pop_key)\n",
|
|
"\n",
|
|
" min_dists = dict(sorted(min_dists.items(), key=lambda item: item[1]))\n",
|
|
"\n",
|
|
" for image_id in min_dists.keys():\n",
|
|
" if image_id==target_image_id:\n",
|
|
" continue\n",
|
|
" else:\n",
|
|
" print(\"Label: \", label_dict[image_id], \"; distance: \", min_dists[image_id])\n",
|
|
" \n",
|
|
"#---------------------------------------------------------------------------------------------------------------------------\n",
|
|
"\n",
|
|
"selected_image_id = int(input(\"Enter image ID: \"))\n",
|
|
"sample_image, sample_label = dataset[selected_image_id]\n",
|
|
"plt.imshow(transforms.ToPILImage()(sample_image))\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"k = int(input(\"Enter value of k: \"))\n",
|
|
"if k < 1:\n",
|
|
" raise ValueError(\"k should be positive integer\")\n",
|
|
"\"\"\"\n",
|
|
"selected_feature_model = str(\n",
|
|
" input(\"Enter feature model - one of \" + str(valid_feature_models))\n",
|
|
")\n",
|
|
"\n",
|
|
"selected_distance_measure = valid_distance_measures[str(\n",
|
|
" input(\"Enter distance measure - one of \" + str(list(valid_distance_measures.keys())))\n",
|
|
")]\n",
|
|
"show_similar_labels(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"#TASK 2b\n",
|
|
"\n",
|
|
"selected_feature_model = \"resnet\"\n",
|
|
"selected_distance_measure = valid_distance_measures[\"cosine\"]\n",
|
|
"show_similar_labels(selected_image_id, k, selected_feature_model, selected_distance_measure, save_plots=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
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