Lab_plt.py

#!/usr/bin/env python
import os
import sys
import cv2
import numpy as np
import colorsys
import matplotlib.pyplot as plt
import math
from scipy import stats
import matplotlib.patches as patches
import collections


def Lab_plt(IMG_DIR, p_title):
    files = os.listdir(IMG_DIR)
    files = sorted(files)
    fps = 24
    Lt = []
    Ll = []
    La = []
    Lb = []
    Lc = []
    if len(files) <= (fps * 120):
        ext_parm = fps
        time_scale = 1
        time_unit = "秒(sec)"
    else:
        ext_parm = (fps / 3)
        time_scale = 60
        time_unit = "分(min)"

    for i, file in enumerate(files):
        if not i % (fps / ext_parm) == 0:
            continue
        img_path = IMG_DIR + "/" + file
        img = cv2.imread(img_path)
        lab_img = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
        mean_l = int(np.mean(lab_img[:, :, 0]))
        mean_a = int(np.mean(lab_img[:, :, 1]))
        mean_b = int(np.mean(lab_img[:, :, 2]))
        mean_c = math.sqrt(mean_a**2 + mean_b**2)
        Lt.append(i / fps / time_scale)
        Ll.append(mean_l)
        La.append(mean_a)
        Lb.append(mean_b)
        Lc.append(mean_c)

    ##num = fps * int((i / fps / 8))
    num = int(len(Lt) / 24)
    b = np.ones(num) / num
    Ll2 = np.convolve(Ll, b, mode="same")
    La2 = np.convolve(La, b, mode="same")
    Lb2 = np.convolve(Lb, b, mode="same")
    Lc2 = np.convolve(Lc, b, mode="same")
    l_mean = np.mean(Ll)
    a_mean = np.mean(La)
    b_mean = np.mean(Lb)
    c_mean = np.mean(Lc)

    l_med = np.median(Ll)
    a_med = np.median(La)
    b_med = np.median(Lb)
    c_med = np.median(Lc)

    l_mode = stats.mode(Ll, axis=None)[0]
    a_mode = stats.mode(La, axis=None)[0]
    b_mode = stats.mode(Lb, axis=None)[0]
    c_mode = stats.mode(Lc, axis=None)[0]

    l_std = np.std(Ll)
    a_std = np.std(La)
    b_std = np.std(Lb)
    c_std = np.std(Lc)

    l_max = np.max(Ll)
    a_max = np.max(La)
    b_max = np.max(Lb)
    c_max = np.max(Lc)

    l_min = np.min(Ll)
    a_min = np.min(La)
    b_min = np.min(Lb)
    c_min = np.min(Lc)

    lc_std_count = np.count_nonzero(
        (Ll < l_mean + l_std)
        & (Ll > l_mean - l_std)
        & (Lc < c_mean + c_std)
        & (Lc > c_mean - c_std)
    )
    lc_std_ratio = (lc_std_count / len(Ll)) * 100

    os.mkdir(p_title)
    p_fname = p_title + "/" + p_title

    STAT_TXTF = p_fname + "stat.txt"
    f = open(STAT_TXTF, "w")
    f.write("duration:" + str(len(files) / fps / time_scale) + time_unit + "\n")
    f.write("l_mean:" + str(l_mean) + "\n")
    f.write("a_mean:" + str(a_mean) + "\n")
    f.write("b_mean:" + str(b_mean) + "\n")
    f.write("c_mean:" + str(c_mean) + "\n")
    f.write("l_med:" + str(l_med) + "\n")
    f.write("a_med:" + str(a_med) + "\n")
    f.write("b_med:" + str(b_med) + "\n")
    f.write("c_med:" + str(c_med) + "\n")
    f.write("l_mode:" + str(l_mode) + "\n")
    f.write("a_mode:" + str(a_mode) + "\n")
    f.write("b_mode:" + str(b_mode) + "\n")
    f.write("c_mode:" + str(c_mode) + "\n")
    f.write("l_std:" + str(l_std) + "\n")
    f.write("a_std:" + str(a_std) + "\n")
    f.write("b_std:" + str(b_std) + "\n")
    f.write("c_std:" + str(c_std) + "\n")
    f.write("l_max:" + str(l_max) + "\n")
    f.write("a_max:" + str(a_max) + "\n")
    f.write("b_max:" + str(b_max) + "\n")
    f.write("c_max:" + str(c_max) + "\n")
    f.write("l_min:" + str(l_min) + "\n")
    f.write("a_min:" + str(a_min) + "\n")
    f.write("b_min:" + str(b_min) + "\n")
    f.write("c_min:" + str(c_min) + "\n")
    f.write("lc_std_count:" + str(lc_std_count) + "\n")
    f.write("len(Ll):" + str(len(Ll)) + "\n")
    f.write("lc_std_ratio:" + str(lc_std_ratio) + "\n")
    f.close()

    plt.rcParams["font.family"] = "Noto Sans CJK JP"

    # --- plot
    fig = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax = fig.add_subplot(111, fc="w", xlabel=time_unit, ylabel="L*")
    ax.set_title(p_title + " L*a*b* / L*(明度)推移")
    ax.plot(Lt, Ll, label="L*")
    ax.plot(Lt, Ll2, "r", label="L*:Moving average")
    ax.axhline(y=l_mean, color="r", linestyle="dashed", linewidth=1)
    ax.text(-2, l_mean, "average:" + str(round(l_mean, 1)), ha="right", size=10)
    ax.axhline(y=l_mode, color="g", linestyle="dashed", linewidth=1)
    ax.text(-2, l_mode, "mode:" + str(round(l_mode, 1)), ha="right", size=10)
    # print(Ll2.index(max(Ll2)))

    ax.set_ylim(-5, 255)
    ax.set_yticks(np.arange(0, 255, step=25))
    ax.grid()
    ax.legend()
    fig.savefig(p_fname + "-Lab-l.png", facecolor=fig.get_facecolor())
    # plt.show()

    fig1 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax1 = fig1.add_subplot(111, fc="w", xlabel=time_unit, ylabel="a*")
    ax1.set_title(p_title + " L*a*b* / a*推移")
    ax1.plot(Lt, La, label="a*")
    ax1.plot(Lt, La2, "r", label="a*:Moving average")
    ax1.axhline(y=a_mean, color="r", linestyle="dashed", linewidth=1)
    ax1.text(-2, a_mean, "average:" + str(round(a_mean, 1)), ha="right", size=10)
    ax1.axhline(y=a_mode, color="g", linestyle="dashed", linewidth=1)
    ax1.text(-2, a_mode, "mode:" + str(round(a_mode, 1)), ha="right", size=10)
    ax1.set_ylim(-5, 255)
    ax1.set_yticks(np.arange(0, 255, step=25))
    ax1.grid()
    ax1.legend()
    fig1.savefig(p_fname + "-Lab-a.png", facecolor=fig.get_facecolor())
    # plt.show()

    fig2 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax2 = fig2.add_subplot(111, fc="w", xlabel=time_unit, ylabel="b*")
    ax2.set_title(p_title + " L*a*b* / b*推移")
    ax2.plot(Lt, Lb, label="b*")
    ax2.plot(Lt, Lb2, "r", label="b*:Moving average")
    ax2.axhline(y=b_mean, color="r", linestyle="dashed", linewidth=1)
    ax2.text(-2, b_mean, "average:" + str(round(b_mean, 1)), ha="right", size=10)
    ax2.axhline(y=b_mode, color="g", linestyle="dashed", linewidth=1)
    ax2.text(-2, b_mode, "mode:" + str(round(b_mode, 1)), ha="right", size=10)
    ax2.set_ylim(-5, 255)
    ax2.grid()
    ax2.legend()
    fig2.savefig(p_fname + "-Lab-b.png", facecolor=fig.get_facecolor())
    # plt.show()

    fig3 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax3 = fig3.add_subplot(111, fc="w", xlabel=time_unit, ylabel="c*")
    ax3.set_title(p_title + " L*a*b* / c*(彩度)推移")
    ax3.plot(Lt, Lc, label="c*")
    ax3.plot(Lt, Lc2, "r", label="c*:Moving average")
    ax3.axhline(y=c_mean, color="r", linestyle="dashed", linewidth=1)
    ax3.text(-2, c_mean, "average:" + str(round(c_mean, 1)), ha="right", size=10)
    ax3.axhline(y=c_mode, color="g", linestyle="dashed", linewidth=1)
    ax3.text(-2, c_mode, "mode:" + str(round(c_mode, 1)), ha="right", size=10)
    ax3.set_ylim(-5, 255)
    ax3.grid()
    ax3.legend()
    fig3.savefig(p_fname + "-Lab-c.png", facecolor=fig.get_facecolor())
    # plt.show()

    # --- scatter(tone)
    fig4 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=True
    )
    ax4 = fig4.add_subplot(111, fc="w", xlabel="c*(彩度)", ylabel="L*(明度)")
    ax4.set_title(p_title + " L*a*b* トーン:明度x彩度")
    ax4.scatter(Lc, Ll, s=1)

    ax4.axhline(y=l_mean, color="r", linestyle="dashed", linewidth=1)
    ax4.text(145, l_mean, "mean:" + str(round(l_mean, 1)), ha="right", size=10)
    ax4.axvline(x=c_mean, color="r", linestyle="dashed", linewidth=1)
    ax4.text(c_mean, -32, "mean:" + str(round(c_mean, 1)), ha="center", size=10)
    ax4.set_xlim([140, 240])
    ax4.set_ylim([-10, 255])

    ax4.axhline(y=l_mode, color="g", linestyle="dashed", linewidth=1)
    ax4.text(145, l_mode, "mode:" + str(round(l_mode, 1)), ha="right", size=10)
    ax4.axvline(x=c_mode, color="g", linestyle="dashed", linewidth=1)
    ax4.text(c_mode, -38, "mode:" + str(round(c_mode, 1)), ha="center", size=10)

    boxdic = {
        "facecolor": "lightgreen",
        "edgecolor": "lightgreen",
        "alpha": 0.5,
        "boxstyle": "Round",
        "linewidth": 1,
    }
    rec = patches.Rectangle(
        xy=(c_mean - c_std, l_mean - l_std),
        width=c_std * 2,
        height=l_std * 2,
        ec="red",
        fill=False,
    )
    ax4.text(
        c_mean + c_std,
        l_mean + l_std,
        str(round(lc_std_ratio, 1))
        + "%"
        + " (L* std:"
        + str(round(l_std, 1))
        + " c*std:"
        + str(round(c_std, 1))
        + ")",
        ha="left",
        size=8,
        color="red",
        bbox=boxdic,
    )
    ax4.add_patch(rec)

    ax4.grid()
    ax4.legend()
    fig4.savefig(p_fname + "-lab-tone.png", facecolor=fig.get_facecolor())
    # plt.show()

    # --- bar
    fig5 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax5 = fig5.add_subplot(111, fc="w", xlabel="L*", ylabel="出現数")
    ax5.set_title(p_title + " L*分布")
    c = collections.Counter(Ll)
    max_occur = c.most_common()[0][1]
    ax5.bar(list(c.keys()), list(c.values()))
    ax5.text(l_mode, max_occur, "max:" + str(max_occur), ha="right", size=10)
    ax5.axvline(x=l_mean, color="r", linestyle="dashed", linewidth=1)
    ax5.text(
        l_mean,
        max_occur / 16 * -1,
        "average:" + str(round(l_mean, 1)),
        ha="center",
        size=10,
    )
    ax5.axvline(x=l_mode, color="g", linestyle="dashed", linewidth=1)
    ax5.text(
        l_mode,
        max_occur / 16 * -1,
        "mode:" + str(round(l_mode, 1)),
        ha="center",
        size=10,
    )
    ax5.set_xlim(-5, 255)
    ax5.grid()
    ax5.legend()
    fig5.savefig(p_fname + "-Lab-l-bar.png", facecolor=fig.get_facecolor())
    # plt.show()

    fig6 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=False
    )
    ax6 = fig6.add_subplot(111, fc="w", xlabel="c*", ylabel="出現数")
    ax6.set_title(p_title + " c*分布")
    c = collections.Counter(Lc)
    max_occur = c.most_common()[0][1]
    ax6.bar(list(c.keys()), list(c.values()))
    ax6.text(c_mode, max_occur, "max:" + str(max_occur), ha="right", size=10)
    ax6.axvline(x=c_mean, color="r", linestyle="dashed", linewidth=1)
    ax6.text(
        c_mean,
        max_occur / 16 * -1,
        "average:" + str(round(c_mean, 1)),
        ha="center",
        size=10,
    )
    ax6.axvline(x=c_mode, color="g", linestyle="dashed", linewidth=1)
    ax6.text(
        c_mode,
        max_occur / 16 * -1,
        "mode:" + str(round(c_mode, 1)),
        ha="center",
        size=10,
    )
    ax6.grid()
    ax6.legend()
    fig6.savefig(p_fname + "-Lab-c-bar.png", facecolor=fig.get_facecolor())
    # plt.show()

    # --- scatter(tone-3d)
    fig7 = plt.figure(
        figsize=(16, 8), dpi=100, facecolor="lightgray", tight_layout=True
    )
    ax7 = fig7.add_subplot(
        111,
        fc="w",
        xlabel="c*(彩度)",
        ylabel="L*(明度)",
        zlabel=time_unit,
        projection="3d",
    )
    ct = 0
    while ct * 5 < 90:
        ax7.view_init(elev=10, azim=ct * 5)
        ct += 1
        ax7.set_title(p_title + " L*a*b* トーン:明度x彩度x時間")
        ax7.scatter(Lc, Ll, Lt, s=1, c="blue")
        ax7.grid()
        # ax7.legend()
        fig7.savefig(
            p_fname + "-lab-tone-3d" + str(ct).zfill(3) + ".png",
            facecolor=fig.get_facecolor(),
        )


if __name__ == "__main__":
    IMG_DIR = sys.argv[1]
    p_title = sys.argv[2]
    Lab_plt(IMG_DIR, p_title)