我很高兴你找到了答案。

我会建议一种可行的替代方法。不幸的是我不熟悉python，所以你需要找到如何在python（它的基本）中编码。

如果我有你的第一张图像HSV阈值，我会使用

请注意，我是从糟糕的HSV细分开始的。如果你尝试更好的结果可能会改善。另外，使用内核大小进行侵蚀和扩张。

In an effort to separate histologic slides into several layers based on color, I modified some widely distributed code (1) available through OpenCV's community. Our staining procedure marks different cell types of tissue cross sections with different colors (B cells are red, Macrophages are brown, background nuceli have a bluish color).

I'm interested in selecting only the magenta-colored and brown parts of the image.

Here's my attempt to create a mask for the magenta pigment:

    import cv2
    import numpy as np

    def mask_builder(filename,hl,hh,sl,sh,vl,vh):
        #load image, convert to hsv
        bgr = cv2.imread(filename)
        hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
        #set lower and upper bounds of range according to arguements
        lower_bound = np.array([hl,sl,vl],dtype=np.uint8)
        upper_bound = np.array([hh,sh,vh],dtype=np.uint8)
        return cv2.inRange(hsv, lower_bound,upper_bound)

    mask = mask_builder('sample 20 138 1.jpg', 170,180, 0,200, 0,230)
    cv2.imwrite('mask.jpg', mask)

So far a trial and error approach has produced poor results:

The can anyone suggest a smarter method to threshhold within the HSV colorspace? I've done my best to search for answers in previous posts, but it seems that these color ranges are particularly difficult to define due to the nature of the image.

References:

1. Separation with Colorspaces: http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html
2. python opencv color tracking
3. BGR separation: http://www.pyimagesearch.com/2014/08/04/opencv-python-color-detection/

UPDATE: I've found a working solution to my problem. I increased the lower bound of 'S' and 'V' by regular intervals using a simple FOR control structure, outputing the results for each test image and choosing the best. I found my lower bounds for S and V should be set at 100 and 125. This systematic method of trial and error produced better results:

I am happy you found your answer.

I will suggest an alternate method that might work. Unfortunately I am not proficient with python so you'll need to find out how to code that in python (its basic).

If I had the firs image you have after the HSV threshold, I would use morphological operations to get the information I want.

I would probably give it a go to "closing", but if it doesnt work I would first dilate, then fill and then erode the same amount firstly dilated.

Probably after this first step you'll need to delete the small "noise" blobs you have around and you'll get the image.

This is how it would be in Matlab (showing this mainly so you can see the results):

I=imread('http://i.stack.imgur.com/RlH4V.jpg');

I=I>230;                        % Create Black and white image (this is because in *** its a jpg)
ker=strel('square',3);          % Create a 3x3 square kernel

I1=imdilate(I,ker);             % Dilate
I2=imfill(I1,'holes');          % Close
I3=imerode(I2,ker);             % Erode

Ilabel=bwlabel(I3,8);            % Get a label per independent blob

% Get maximum area blob (you can do this with a for in python easily)
areas = regionprops(Ilabel,'Centroid','Area','PixelIdxList');
[~,index] = max([areas.Area]);   % Get the maximum area

Imask=Ilabel==index;             % Get the image with only the max area.



% Plot: This is just matlab code, no relevance

figure;
subplot(131)
title('Dialted')
imshow(I1);
subplot(132)
title('Closed')
imshow(I2);
subplot(133)
title('Eroded')
imshow(I3);


figure;
imshow(imread('http://i.stack.imgur.com/ZqrF9.jpg'))
hold on
h=imshow(bwperim(Imask));
set(h,'alphadata',Imask/2)

Note that I started from the "bad" HSV segmentation. If you try a better one the results may improve. Also, play with the kernel size for the erosion and dilation.