tools/visualization.py

import os
import random

import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import uuid

plt.rc('font', size=20)

colors = [
    [255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0],
    [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255],
    [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255],
    [255, 0, 255], [255, 0, 170], [255, 0, 85], [255, 0, 0]]


def plot_results(support_img, query_img, support_kp, support_w, query_kp, query_w, skeleton,
                 initial_proposals, prediction, radius=6, out_dir='./heatmaps'):
    img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
    if len(img_names) > 0:
        name_idx = max([int(img_name) for img_name in img_names]) + 1
    else:
        name_idx = 0

    h, w, c = support_img.shape
    prediction = prediction[-1].cpu().numpy() * h
    support_img = (support_img - np.min(support_img)) / (np.max(support_img) - np.min(support_img))
    query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))

    for id, (img, w, keypoint) in enumerate(zip([support_img, query_img],
                                                [support_w, query_w],
                                                [support_kp, prediction])):
        f, axes = plt.subplots()
        plt.imshow(img)
        for k in range(keypoint.shape[0]):
            if w[k] > 0:
                kp = keypoint[k, :2]
                c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
                patch = plt.Circle(kp, radius, color=c)
                axes.add_patch(patch)
                axes.text(kp[0], kp[1], k)
                plt.draw()
        for l, limb in enumerate(skeleton):
            kp = keypoint[:, :2]
            if l > len(colors) - 1:
                c = [x / 255 for x in random.sample(range(0, 255), 3)]
            else:
                c = [x / 255 for x in colors[l]]
            if w[limb[0]] > 0 and w[limb[1]] > 0:
                patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
                                   [kp[limb[0], 1], kp[limb[1], 1]],
                                   linewidth=6, color=c, alpha=0.6)
                axes.add_artist(patch)
        plt.axis('off')  # command for hiding the axis.
        name = 'support' if id == 0 else 'query'
        plt.savefig(f'./{out_dir}/{str(name_idx)}_{str(name)}.png', bbox_inches='tight', pad_inches=0)
        if id == 1:
            plt.show()
        plt.clf()
        plt.close('all')


def plot_query_results(query_img, query_w, skeleton, prediction, radius=6, out_dir='./heatmaps'):
    img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
    if len(img_names) > 0:
        name_idx = max([int(img_name) for img_name in img_names]) + 1
    else:
        name_idx = 0

    h, w, c = query_img.shape
    prediction = prediction[-1].cpu().numpy() * h
    query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))

    for id, (img, w, keypoint) in enumerate(zip([query_img],
                                                [query_w],
                                                [prediction])):
        f, axes = plt.subplots()
        plt.imshow(img)
        for k in range(keypoint.shape[0]):
            if w[k] > 0:
                kp = keypoint[k, :2]
                c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
                patch = plt.Circle(kp, radius, color=c)
                axes.add_patch(patch)
                axes.text(kp[0], kp[1], k)
                plt.draw()
        for l, limb in enumerate(skeleton):
            kp = keypoint[:, :2]
            if l > len(colors) - 1:
                c = [x / 255 for x in random.sample(range(0, 255), 3)]
            else:
                c = [x / 255 for x in colors[l]]
            if w[limb[0]] > 0 and w[limb[1]] > 0:
                patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
                                   [kp[limb[0], 1], kp[limb[1], 1]],
                                   linewidth=6, color=c, alpha=0.6)
                axes.add_artist(patch)
        plt.axis('off')  # command for hiding the axis.
        plt.savefig(f'./{out_dir}/{str(name_idx)}_query_out.png', bbox_inches='tight', pad_inches=0)
        plt.show()
        plt.clf()
        plt.close('all')
        return name_idx


def plot_modified_query(query_img, query_w, skeleton, prediction, modified_prediction, radius=6, out_dir='./heatmaps'):
    import math
    img_names = [img.split("_")[0] for img in os.listdir(out_dir) if str_is_int(img.split("_")[0])]
    if len(img_names) > 0:
        name_idx = max([int(img_name) for img_name in img_names]) + 1
    else:
        name_idx = 0

    h, w, c = query_img.shape
    prediction = prediction * h
    modified_prediction = modified_prediction * h
    # support_img = (support_img - np.min(support_img)) / (np.max(support_img) - np.min(support_img))
    query_img = (query_img - np.min(query_img)) / (np.max(query_img) - np.min(query_img))

    # for id, (img, w, keypoint) in enumerate(zip([support_img, query_img],
    #                                             [support_w, query_w],
    #                                             [support_kp, prediction])):
    for id, (img, w, keypoint, modified_keypoint) in enumerate(zip([query_img],
                                                [query_w],
                                                [prediction],
                                                [modified_prediction])):
        f, axes = plt.subplots()
        plt.imshow(img)
        for k in range(keypoint.shape[0]):
            if w[k] > 0:
                kp1 = keypoint[k, :2]
                kp2 = modified_keypoint[k, :2]
                dist = 20*math.dist(keypoint[k, :2], modified_keypoint[k, :2])/h
                kp = (kp1+kp2)/2
                # kp = keypoint[k, :2]
                c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
                patch = plt.Circle(kp, radius*dist, color=c)
                axes.add_patch(patch)
                axes.text(kp[0], kp[1], k)
                plt.draw()
        for l, limb in enumerate(skeleton):
            kp1 = keypoint[:, :2]
            kp2 = modified_keypoint[:, :2]
            kp = (kp1 + kp2) / 2
            if l > len(colors) - 1:
                c = [x / 255 for x in random.sample(range(0, 255), 3)]
            else:
                c = [x / 255 for x in colors[l]]
            if w[limb[0]] > 0 and w[limb[1]] > 0:
                patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
                                   [kp[limb[0], 1], kp[limb[1], 1]],
                                   linewidth=6, color=c, alpha=0.6)
                axes.add_artist(patch)
        plt.axis('off')  # command for hiding the axis.
        # name = 'support' if id == 0 else 'query'
        name = 'query'
        plt.savefig(f'./{out_dir}/{str(name_idx)}_query_out.png', bbox_inches='tight', pad_inches=0)
        plt.show()
        plt.clf()
        plt.close('all')
        return name_idx


def str_is_int(s):
    try:
        int(s)
        return True
    except ValueError:
        return False