# Plotting utils

import glob
import math
import os
import random
from copy import copy
from pathlib import Path

import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import yaml
from PIL import Image
from scipy.signal import butter, filtfilt

from Fusion.utils.general import xywh2xyxy, xyxy2xywh
from Fusion.utils.metrics import fitness

# Settings
matplotlib.use('Agg')  # for writing to files only


def color_list():
    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
    def hex2rgb(h):
        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))

    return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]


def hist2d(x, y, n=100):
    # 2d histogram used in labels.png and evolve.png
    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
    return np.log(hist[xidx, yidx])


def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
    def butter_lowpass(cutoff, fs, order):
        nyq = 0.5 * fs
        normal_cutoff = cutoff / nyq
        return butter(order, normal_cutoff, btype='low', analog=False)

    b, a = butter_lowpass(cutoff, fs, order=order)
    return filtfilt(b, a, data)  # forward-backward filter


def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)


def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
    # Compares the two methods for width-height anchor multiplication
    # https://github.com/ultralytics/yolov3/issues/168
    x = np.arange(-4.0, 4.0, .1)
    ya = np.exp(x)
    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2

    fig = plt.figure(figsize=(6, 3), dpi=150)
    plt.plot(x, ya, '.-', label='YOLO')
    plt.plot(x, yb ** 2, '.-', label='YOLO ^2')
    plt.plot(x, yb ** 1.6, '.-', label='YOLO ^1.6')
    plt.xlim(left=-4, right=4)
    plt.ylim(bottom=0, top=6)
    plt.xlabel('input')
    plt.ylabel('output')
    plt.grid()
    plt.legend()
    fig.tight_layout()
    fig.savefig('comparison.png', dpi=200)


def output_to_target(output, width, height):
    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
    if isinstance(output, torch.Tensor):
        output = output.cpu().numpy()

    targets = []
    for i, o in enumerate(output):
        if o is not None:
            for pred in o:
                box = pred[:4]
                w = (box[2] - box[0]) / width
                h = (box[3] - box[1]) / height
                x = box[0] / width + w / 2
                y = box[1] / height + h / 2
                conf = pred[4]
                cls = int(pred[5])

                targets.append([i, cls, x, y, w, h, conf])

    targets = torch.Tensor(targets).cpu()
    return np.array(targets)


def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
    # Plot image grid with labels

    if isinstance(images, torch.Tensor):
        images = images.cpu().float().numpy()
    if isinstance(targets, torch.Tensor):
        targets = targets.cpu().numpy()

    # un-normalise
    if np.max(images[0]) <= 1:
        images *= 255

    img_format = fname[fname.find('.'):]
    if img_format == '.jpeg':
        # images = images[:, np.newaxis, :, :]
        images = np.repeat(images, 3, axis=1)

    tl = 2  # line thickness # 3
    tf = max(tl - 1, 1)  # font thickness
    bs, _, h, w = images.shape  # batch size, _, height, width

    bs = min(bs, max_subplots)  # limit plot images
    ns = np.ceil(bs ** 0.5)  # number of subplots (square)

    # Check if we should resize
    scale_factor = max_size / max(h, w)
    if scale_factor < 1:
        h = math.ceil(scale_factor * h)
        w = math.ceil(scale_factor * w)

    colors = color_list()  # list of colors
    # if img_format != '.jpeg':
    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
    # else:
    #     mosaic = np.full((int(ns * h), int(ns * w)), 255, dtype=np.uint8)  # init

    for i, img in enumerate(images):
        
        if i == max_subplots:  # if last batch has fewer images than we expect
            break

        block_x = int(w * (i // ns))
        block_y = int(h * (i % ns))

        # if img_format != '.jpeg':
        img = img.transpose(1, 2, 0) # DO NOT MOVE!
        img = img[..., :3] # only 3 channels are allowed

        if scale_factor < 1:
            img = cv2.resize(img, (w, h))

        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
        # else: mosaic[block_y:block_y + h, block_x:block_x + w] = img
        if len(targets) > 0:
            image_targets = targets[targets[:, 0] == i]
            boxes = xywh2xyxy(image_targets[:, 2:6]).T
            classes = image_targets[:, 1].astype('int')
            labels = image_targets.shape[1] == 6  # labels if no conf column
            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)

            boxes[[0, 2]] *= w
            boxes[[0, 2]] += block_x
            boxes[[1, 3]] *= h
            boxes[[1, 3]] += block_y
            for j, box in enumerate(boxes.T):
                cls = int(classes[j])
                color = colors[cls % len(colors)]
                cls = names[cls] if names else cls
                if labels or conf[j] > 0.25:  # 0.25 conf thresh
                    label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
                    plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)

        # Draw image filename labels
        if paths:
            label = Path(paths[i]).name[:40]  # trim to 40 char
            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
                        lineType=cv2.LINE_AA)

        # Image border
        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=2) # thickness=3

    if fname:
        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
        Image.fromarray(mosaic).save(fname)  # PIL save
    return mosaic


def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
    # Plot LR simulating training for full epochs
    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
    y = []
    for _ in range(epochs):
        scheduler.step()
        y.append(optimizer.param_groups[0]['lr'])
    plt.plot(y, '.-', label='LR')
    plt.xlabel('epoch')
    plt.ylabel('LR')
    plt.grid()
    plt.xlim(0, epochs)
    plt.ylim(0)
    plt.tight_layout()
    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)


def plot_test_txt():  # from utils.general import *; plot_test()
    # Plot test.txt histograms
    x = np.loadtxt('test.txt', dtype=np.float32)
    box = xyxy2xywh(x[:, :4])
    cx, cy = box[:, 0], box[:, 1]

    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
    ax.set_aspect('equal')
    plt.savefig('hist2d.png', dpi=300)

    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
    ax[0].hist(cx, bins=600)
    ax[1].hist(cy, bins=600)
    plt.savefig('hist1d.png', dpi=200)


def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
    # Plot targets.txt histograms
    x = np.loadtxt('targets.txt', dtype=np.float32).T
    s = ['x targets', 'y targets', 'width targets', 'height targets']
    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
    ax = ax.ravel()
    for i in range(4):
        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
        ax[i].legend()
        ax[i].set_title(s[i])
    plt.savefig('targets.jpg', dpi=200)


def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
    # Plot study.txt generated by test.py
    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
    ax = ax.ravel()

    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
    for f in ['study/study_coco_yolo%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
        x = np.arange(y.shape[1]) if x is None else np.array(x)
        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
        for i in range(7):
            ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
            ax[i].set_title(s[i])

        j = y[3].argmax() + 1
        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
                 label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))

    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')

    ax2.grid()
    ax2.set_xlim(0, 30)
    ax2.set_ylim(28, 50)
    ax2.set_yticks(np.arange(30, 55, 5))
    ax2.set_xlabel('GPU Speed (ms/img)')
    ax2.set_ylabel('COCO AP val')
    ax2.legend(loc='lower right')
    plt.savefig('study_mAP_latency.png', dpi=300)
    plt.savefig(f.replace('.txt', '.png'), dpi=300)


def plot_labels(labels, save_dir=''):
    # plot dataset labels
    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
    nc = int(c.max() + 1)  # number of classes

    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
    ax = ax.ravel()
    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
    ax[0].set_xlabel('classes')
    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
    ax[1].set_xlabel('x')
    ax[1].set_ylabel('y')
    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
    ax[2].set_xlabel('width')
    ax[2].set_ylabel('height')
    plt.savefig(Path(save_dir) / 'labels.png', dpi=200)
    plt.close()

    # seaborn correlogram
    try:
        import seaborn as sns
        import pandas as pd
        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
                     plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
                     diag_kws=dict(bins=50))
        plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
        plt.close()
    except Exception as e:
        pass


def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general import *; plot_evolution()
    # Plot hyperparameter evolution results in evolve.txt
    with open(yaml_file) as f:
        hyp = yaml.load(f, Loader=yaml.FullLoader)
    x = np.loadtxt('evolve.txt', ndmin=2)
    f = fitness(x)
    # weights = (f - f.min()) ** 2  # for weighted results
    plt.figure(figsize=(10, 12), tight_layout=True)
    matplotlib.rc('font', **{'size': 8})
    for i, (k, v) in enumerate(hyp.items()):
        y = x[:, i + 7]
        # mu = (y * weights).sum() / weights.sum()  # best weighted result
        mu = y[f.argmax()]  # best single result
        plt.subplot(6, 5, i + 1)
        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
        plt.plot(mu, f.max(), 'k+', markersize=15)
        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
        if i % 5 != 0:
            plt.yticks([])
        print('%15s: %.3g' % (k, mu))
    plt.savefig('evolve.png', dpi=200)
    print('\nPlot saved as evolve.png')


def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
    # Plot training 'results*.txt', overlaying train and val losses
    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
        ax = ax.ravel()
        for i in range(5):
            for j in [i, i + 5]:
                y = results[j, x]
                ax[i].plot(x, y, marker='.', label=s[j])
                # y_smooth = butter_lowpass_filtfilt(y)
                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])

            ax[i].set_title(t[i])
            ax[i].legend()
            ax[i].set_ylabel(f) if i == 0 else None  # add filename
        fig.savefig(f.replace('.txt', '.png'), dpi=200)


def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
    # Plot training 'results*.txt'
    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
    ax = ax.ravel()
    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
    if bucket:
        # os.system('rm -rf storage.googleapis.com')
        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
        files = ['%g.txt' % x for x in id]
        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/%g.txt' % (bucket, x) for x in id)
        os.system(c)
    else:
        files = glob.glob(str(Path(save_dir) / '*.txt')) + glob.glob('../../Downloads/results*.txt')
    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
    for fi, f in enumerate(files):
        try:
            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
            n = results.shape[1]  # number of rows
            x = range(start, min(stop, n) if stop else n)
            for i in range(10):
                y = results[i, x]
                if i in [0, 1, 2, 5, 6, 7]:
                    y[y == 0] = np.nan  # don't show zero loss values
                    # y /= y[0]  # normalize
                label = labels[fi] if len(labels) else Path(f).stem
                ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
                ax[i].set_title(s[i])
                # if i in [5, 6, 7]:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            print('Warning: Plotting error for %s; %s' % (f, e))

    fig.tight_layout()
    ax[1].legend()
    fig.savefig(Path(save_dir) / 'results.png', dpi=200)
