检测图像中的圣诞树，不用深度学习，好家伙，还可以怎么搞！

from PIL import Image
import numpy as np
import scipy
import matplotlib.colors as colors
from sklearn.cluster import DBSCAN
from math import ceil,sqrt




'''
Inputs:
    
    rgbimg: M,N,3 numpy 包含 uint(0-255) color image
    
    hueleftthr: Scalar constant to maximum  hue in  yellow-green region
    
    huerightthr: Scalar constant to maximum allowed hue in blue-purple region
    
    satthr: Scalar constant to select minimum allow saturation
    
    valthre: Scalar constant to select minimum allow value
    
    monothr: Scalar constant to select minimum allow monochrome
    
    maxpoints: Scalar constant maximum number of pixels  to forward to the DBSCAN clustering algoritm

    proxthresh: Proximity threshold to use for DBSCAN, as da fraction of the diagonal size of thre image
                接近阈值占图像对角线尺寸


Outputs:
    
    borderseg: [K,2,2] Nested list containing K pairs of x- and y- pixel values for drawimg the tree border
    
    X:  [P,2] List of pixels that passed the threshold step
    
    labels: [Q,2] List of cluster labels for points in  Xslice(see below)
    
    Xslice: [Q,2] Reduced list of pixels to be passed to DBSCAN


'''

'''实现脚本'''

def findtree(rgbimg,
             hueleftthr = 0.2,
             huerightthr = 0.95,
             satthr =0.7,
             valthr = 0.7,
             monothr = 220,
             maxpoints = 5000,
             proxthresh = 0.04):
    # 将 RGB 图像转化为 灰度图
    grayimg = np.asarray(Image.fromarray(rgbimg).convert('L'))

    # 将 rbg => hsv(float [0,1.0])
    hsvimg = colors.rgb_to_hsv(rgbimg.astype(float)/255)

    # 二值化阈值图像初始化

    binimg = np.zeros((rgbimg.shape[0],rgbimg.shape[1]))

    #1， heu < 0.2 or hue > 0.95(red or yellow)
    #2， saturated and bright both greater than 0.7
    # 满足以上条件被认为是圣诞树上的灯
    boolidx = np.logical_and(
        np.logical_and(
            np.logical_or((hsvimg[:,:,0]<hueleftthr),
            (hsvimg[:,:,0]>huerightthr)),
            (hsvimg[:,:,1]>satthr)),
            (hsvimg[:,:,2]>valthr))


    # 找到满足 hsv 标准的像素，赋值为255
    binimg[np.where(boolidx)] = 255
    # 添加像素来满足garay brightness 条件
    binimg[np.where(grayimg>monothr)] = 255

    # 用 DBSCAN 聚类算法分割这些点
    X = np.transpose(np.where(binimg==255))
    Xslice = X
    nsample = len(Xslice)

    if nsample > maxpoints:
        # 确保样本数不超过 DNSCAN 算法最大限度
        Xslice = X[range(0,nsample,int(ceil(float(nsample/maxpoints))))] # 将样本每隔几个采样一次

    # 将 DNSCAN 阈值接近像素单位，并运行 DBSCAN
    pixproxthr = proxthresh * sqrt(binimg.shape[0]**2 + binimg.shape[1]**2) # 对角巷长*proxthresh
    db = DBSCAN(eps = pixproxthr,min_samples=10).fit(Xslice) # 拟合样本
    labels = db.labels_.astype(int)

    # 寻找最大聚类
    unique_labels = set(labels)
    maxclustpt = 0

    for k in unique_labels:
        class_numbers = [index[0] for index in np.argwhere(labels==k)]
        if(len(class_numbers) > maxclustpt):
            points = Xslice[class_numbers]
            hull = scipy.spatial.ConvexHull(points) # 建立凸包
            maxclustpt = len(class_numbers)
            borderseg = [[points[simplex,0], points[simplex,1]] for simplex in hull.simplices]


    return borderseg,X,labels,Xslice

'''
@author:zeroing
@wx公众号：小张Python

'''

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from findtree import findtree
import os

path_dir = 'D:/ceshi_11/findtree'

path_list = [os.path.join(path_dir,str(i)) for i in os.listdir(path_dir)]


# 初始化figure size

fgsz = (16,8)

figthresh = plt.figure(figsize = fgsz,facecolor ='w')
figclust  = plt.figure(figsize = fgsz,facecolor ='w')
figcltwo = plt.figure(figsize = fgsz,facecolor = 'w')
figborder = plt.figure(figsize = fgsz,facecolor = 'w')
figorigin = plt.figure(figsize = fgsz,facecolor = 'w')


# 每张图设置一个 窗口名
figthresh.canvas.set_window_title('Thresholded HSV and Monochrome Brightness')
figclust.canvas.set_window_title('DBSCAN Clusters (Raw Pixel Output)')
figcltwo.canvas.set_window_title('DBSCAN Clusters (Slightly Dilated for Display)')
figborder.canvas.set_window_title('Trees with Borders')
figorigin.canvas.set_window_title("Original Image")


for ii,name in enumerate(path_list):
    # 打开图片
    rgbimg = np.asarray(Image.open(str(name)))

    # 运行脚本找到 bordeseg,X,Labels,Xslce
    borderseg,X,labels,Xslice = findtree(rgbimg)

    # 展示阈值分割后的图像
    axthresh =  figthresh.add_subplot(2,3,ii+1)
    axthresh.set_xticks([])
    axthresh.set_yticks([])
    binimg = np.zeros((rgbimg.shape[0],rgbimg.shape[1]))
    for v,h in X:
        binimg[v,h] = 255 # 初步筛选之后坐标点

    axthresh.imshow(binimg,interpolation = 'nearest',cmap = 'Greys')

    # Display color-coded clusters
    axclust = figclust.add_subplot(2,3,ii+1)
    axclust.set_xticks([])
    axclust.set_yticks([])
    axcltwo = figcltwo.add_subplot(2,3,ii+1)
    axcltwo.set_xticks([])
    axcltwo.set_yticks([])
    axcltwo.imshow(binimg,interpolation = 'nearest',cmap = 'Greys')

    clustimg = np.ones(rgbimg.shape)
    unique_labels = set(labels)
    # 为每个聚类生成单个颜色
    plcol = cm.rainbow_r(np.linspace(0,1,len(unique_labels)))
    print('plcol',plcol)
    for lbl,pix in zip(labels,Xslice):
        for col,unqlbl in zip(plcol,unique_labels):
            if lbl == unqlbl:
                # -1 表示无聚类成员
                if lbl == -1:
                    col = [0.0,0.0,0.0,1.0]
                for ij in range(3):
                    clustimg[pix[0],pix[1],ij] = col[ij]
            # 扩张 图像，用于更好展示
                axcltwo.plot(pix[1],pix[0],'o',markerfacecolor= col,markersize = 1,markeredgecolor = col)

    axclust.imshow(clustimg)
    axcltwo.set_xlim(0,binimg.shape[1]-1)
    axcltwo.set_ylim(binimg.shape[0],-1)

    # 在原图树边缘进行绘制

    axborder = figborder.add_subplot(2,3,ii+1)
    axborder.set_axis_off()
    axborder.imshow(rgbimg,interpolation ='nearest')
    for vseg,hseg in borderseg:
        axborder.plot(hseg,vseg,'g-',lw =3)
    axborder.set_xlim(0,binimg.shape[1]-1)
    axborder.set_ylim(binimg.shape[0],-1)


    # 保存原图
    origin_fig1 = figorigin.add_subplot(2, 3, ii + 1)
    origin_fig1.set_axis_off()
    origin_fig1.imshow(rgbimg, interpolation='nearest')
    axborder.set_xlim(0, binimg.shape[1] - 1)
    axborder.set_ylim(binimg.shape[0], -1)


    # axborder.savefig("D:/ceshi_11/findtree/final_")

    print(name,'Sucessfully find it !!!!!!!!')

plt.show()