PlantCV更新

PyPI

通过终端升级PlantCV:

pip install --upgrade

Conda

通过终端升级PlantCV:

conda upgrade -n plantcv plantcv

如果conda无法更新PlantCV，请尝试安装特定版本。例如，如果你的版本是v3.6.1，而你想要安装v3.6.2，你可以使用以下代码：

conda install -n plantcv plantcv=3.6.2

你可以通过bioconda查看最新的可用版本：

conda list plantcv

手动安装

如果你使用的是danforthcenter/plantcv代码库克隆的master 分支，常规升级步骤是更新本地库并重新安装包。

如果不确定是否从danforthcenter/plantcv代码库克隆的master 分支，可以尝试以下代码：

cd plantcv

git remote -v

# 你会看到如下内容:
# origin    https://github.com/danforthcenter/plantcv.git (fetch)

git status

# 你会看到如下内容:
# On branch master
# nothing to commit, working directory clean

如果以上内容正确显示，就可以通过下面的操作更新:

git pull

python setup.py install

# 如果需要权限请加sudo

如果你应将克隆后的plantcv库文件夹添加到你的PYTHONPATH那么只需要拉取操作就可以升级。具体依赖项详见以下安装说明。当使用PlantCV克隆时（而不是conda环境），更新可能需要在克隆完成后手动添加依赖项。

从 v1 版升级到 v2 版

安装工具方法在PlantCV v1中并不适用，所以用户需要将plantcv/lib路径添加到自定义的PYTHONPATH。在PlantCV v2中，plantcv library路径在lib中已经不存在，而是在库的main文件夹 (plantcv/plantcv)。如果你想要继续将plantcv添加到PYTHONPATH，你需要将‘lib’从路径中移除来进行更新。也可以在拉取新版本后移除lib文件夹。Git会自动移除 *.py ，但由于并不查询，*.pyc文件依旧会保留并且可以导入，会引起混乱。

针对 Linux/Unix， PYTHONPATH 可以编辑为 ~/.bash_profile, ~/.bashrc, ~/.profile, ~/.cshrc, ~/.zshrc, 等。

针对 Windows, 右键我的电脑或者此电脑，选择属性>高级系统设置>环境变量，编辑用户变量添加 PYTHONPATH。

同时要注意PlantCV并行方法也发生了改变，请查阅最新的并行处理文档

升级到 v3版

PlantCV v3新增功能的主要目标是使PlantCV更容易使用。我们希望您接受以下变更，如果您有任何疑问或者顾虑请随时在GitHub开一个讨论话题或者直接联系我们。

为了支持可选安装附属包，我们将PlantCV转换为 namespace package。为了实现新功能，已有函数已经移动到附属包中来保持导入。为了维持之前的操作，PlantCV 分析脚本只需要修改一下PlantCV导入句式。所以如果你之前操作如下:

import plantcv as pcv

那么之后的操作:

from plantcv import plantcv as pcv

PlantCV v3另外一个推出的功能是升级现有包的API。这是为了通过减少导入导出需要的设置（在不减少功能的情况下），让导入参数命名更加一致并且明确定义输入输出类型（例如，不再使用img，而对RGB、灰度或黑白图像分别使用 rgb_img, gray_img, 或 bin_img），让每个PlantCV函数更佳容易使用。

在PlantCV v3.0dev2以后的版本中，所有的函数已经重新设计可以利用全局变量等级来承接标准导入数值例如 debug和 device，从而这些值不需要再额外从其他函数导入导出。一个案例class Params ，由于params在PlantCV导入后已经自动生成，可以导入来设置全局默认值。例如，如果需要改变debug从None到'plot' 或 'print'，你现在只需要在代码或notebook最上方增加一行来改变所有下方函数的调用：

from plantcv import plantcv as pcv
pcv.params.debug = "plot"

因此，所有函数调用需要更新来移除 device输入和输出变量以及debug输入变量。例如：

from plantcv import plantcv as pcv
pcv.params.debug = "plot"

img, img_path, img_filename = pcv.readimage("image.png")

gray_img = pcv.rgb2gray_hsv(img, "s")

bin_img = pcv.threshold.binary(gray_img, 100, 255)

更多信息详见 Params 文档。

以下是所有从v3.0dev2之前版本到v3.0dev2之后版本函数调用的变更，以及v3.0发行版的升级。可以通过查看每个函数的帮助叶面来获取更多输入输出变量类型信息。

plantcv.acute

v3.0dev2之前: device, homolog_pts, start_pts, stop_pts, ptvals, chain, max_dist = plantcv.acute(obj, win, thresh, mask, device, debug=None)
v3.0dev2之后: homolog_pts, start_pts, stop_pts, ptvals, chain, max_dist = plantcv.acute(obj, win, thresh, mask)
v3.2之后: homolog_pts, start_pts, stop_pts, ptvals, chain, max_dist = plantcv.acute(obj, mask, win, thresh)

plantcv.acute_vertex

v3.0dev2之前: device, acute = plantcv.acute_vertex(obj, win, thresh, sep, img, device, debug=None)
v3.0dev2之后: acute = plantcv.acute_vertex(obj, win, thresh, sep, img)
v3.2之后: acute, analysis_image = plantcv.acute_vertex(img, obj, win, thresh, sep)

plantcv.adaptive_threshold

v3.0dev2之前: device, bin_img = plantcv.adaptive_threshold(img, maxValue, thres_type, object_type, device, debug=None)
v3.0dev2之后: Deprecated, see:
- bin_img = plantcv.threshold.gaussian(gray_img, max_value, object_type="light")
- bin_img = plantcv.threshold.mean(gray_img, max_value, object_type="light")

plantcv.analyze_bound

v3.0dev2之前: device, bound_header, bound_data, analysis_images = plantcv.analyze_bound(img, imgname, obj, mask, line_position, device, debug=None, filename=False)
v3.0dev2之后: Deprecated, see:
- analysis_images = plantcv.analyze_bound_horizontal(img, obj, mask, line_position, filename=False)

plantcv.analyze_bound_horizontal

v3.0dev2之前: device, bound_header, bound_data, analysis_images = plantcv.analyze_bound_horizontal(img, obj, mask, line_position, device, debug=None, filename=False)
v3.0dev2之后: bound_header, bound_data, analysis_images = plantcv.analyze_bound_horizontal(img, obj, mask, line_position, filename=False)
v3.0之后: bound_header, bound_data, analysis_images = plantcv.analyze_bound_horizontal(img, obj, mask, line_position)
v3.3之后: analysis_image = plantcv.analyze_bound_horizontal(img, obj, mask, line_position)

plantcv.analyze_bound_vertical

v3.0dev2之前: device, bound_header, bound_data, analysis_images = plantcv.analyze_bound_vertical(img, obj, mask, line_position, device, debug=None, filename=False)
v3.0dev2之后: bound_header, bound_data, analysis_images = plantcv.analyze_bound_vertical(img, obj, mask, line_position, filename=False)
v3.0.5之后: bound_header, bound_data, analysis_images = plantcv.analyze_bound_vertical(img, obj, mask, line_position)
v3.3之后: analysis_image = plantcv.analyze_bound_vertical(img, obj, mask, line_position)

plantcv.analyze_color

v3.0dev2之前: device, hist_header, hist_data, analysis_images = plantcv.analyze_color(img, imgname, mask, bins, device, debug=None, hist_plot_type=None, pseudo_channel='v', pseudo_bkg='img', resolution=300, filename=False)
v3.0dev2之后: hist_header, hist_data, analysis_images = plantcv.analyze_color(rgb_img, mask, bins, hist_plot_type=None, pseudo_channel='v', pseudo_bkg='img', filename=False)
v3.0之后: hist_header, hist_data, analysis_images = plantcv.analyze_color(rgb_img, mask, bins, hist_plot_type=None)
v3.3之后: analysis_image = plantcv.analyze_color(rgb_img, mask, hist_plot_type=None)

plantcv.analyze_nir_intensity

v3.0dev2之前: device, hist_header, hist_data, analysis_img = plantcv.analyze_NIR_intensity(img, rgbimg, mask, bins, device, histplot=False, debug=None, filename=False)
v3.0dev2之后: hist_header, hist_data, analysis_img = plantcv.analyze_nir_intensity(gray_img, mask, bins, histplot=False, filename=False)
v3.0之后: hist_header, hist_data, nir_hist = plantcv.analyze_nir_intensity(gray_img, mask, bins, histplot=False)
v3.3之后: nir_hist = plantcv.analyze_nir_intensity(gray_img, mask, bins, histplot=False)

plantcv.analyze_object

v3.0dev2之前: device, shape_header, shape_data, analysis_images = plantcv.analyze_object(img, imgname, obj, mask, device, debug=None, filename=False)
v3.0dev2之后: shape_header, shape_data, analysis_images = plantcv.analyze_object(img, obj, mask, filename=False)
v3.0之后: shape_header, shape_data, analysis_images = plantcv.analyze_object(img, obj, mask)
v3.3之后: analysis_image = plantcv.analyze_object(img, obj, mask)

plantcv.analyze_thermal_values

v3.5之前: NA
v3.5之后: thermal_histogram = plantcv.analyze_thermal_values(thermal_array, mask, histplot=False)

plantcv.apply_mask

v3.0dev2之前: device, masked_img = plantcv.apply_mask(img, mask, mask_color, device, debug=None)
v3.0dev2之后: masked_img = plantcv.apply_mask(rgb_img, mask, mask_color)
v3.7之后: masked_img = plantcv.apply_mask(img, mask, mask_color)

plantcv.auto_crop

v3.0dev2之前: device, cropped = plantcv.auto_crop(device, img, objects, padding_x=0, padding_y=0, color='black', debug=None)
v3.0dev2之后: cropped = plantcv.auto_crop(img, objects, padding_x=0, padding_y=0, color='black')
v3.2之后: cropped = plantcv.auto_crop(img, obj, padding_x=0, padding_y=0, color='black')

plantcv.background_subtraction

v3.0dev2之前: device, fgmask = plantcv.background_subtraction(background_image, foreground_image, device, debug=None)
v3.0dev2之后: fgmask = plantcv.background_subtraction(background_image, foreground_image)

plantcv.binary_threshold

v3.0dev2之前: device, bin_img = plantcv.binary_threshold(img, threshold, maxValue, object_type, device, debug=None)
v3.0dev2之后: Deprecated, see:
- bin_img = plantcv.threshold.binary(gray_img, threshold, max_value, object_type="light")

plantcv.canny_edge_detect

v3.2之前: NA
v3.2之后: bin_img = plantcv.canny_edge_detect(img, mask=None, sigma=1.0, low_thresh=None, high_thresh=None, thickness=1, mask_color=None, use_quantiles=False)

plantcv.closing

v3.3之前: NA
v3.3之后: filtered_img = plantcv.closing(gray_img, kernel=None)

plantcv.cluster_contour_splitimg

v3.0dev2之前: device, output_path = plantcv.cluster_contour_splitimg(device, img, grouped_contour_indexes, contours, hierarchy, outdir=None, file=None, filenames=None, debug=None)
v3.0dev2之后: output_path = plantcv.cluster_contour_splitimg(rgb_img, grouped_contour_indexes, contours, hierarchy, outdir=None, file=None, filenames=None)
v3.3之后: output_path, output_imgs, output_masks = plantcv.cluster_contour_splitimg(rgb_img, grouped_contour_indexes, contours, hierarchy, outdir=None, file=None, filenames=None)

plantcv.cluster_contours

v3.0dev2之前: device, grouped_contour_indexes, contours, roi_obj_hierarchy = plantcv.cluster_contours(device, img, roi_objects,roi_obj_hierarchy, nrow=1, ncol=1, debug=None)
v3.0dev2之后: grouped_contour_indexes, contours, roi_obj_hierarchy = plantcv.cluster_contours(img, roi_objects, roi_obj_hierarchy, nrow=1, ncol=1)
v3.2之后: grouped_contour_indexes, contours, roi_obj_hierarchy = plantcv.cluster_contours(img, roi_objects, roi_object_hierarchy, nrow=1, ncol=1, show_grid=False)

plantcv.color_palette

v3.0之前: NA
v3.0之后: colors = plantcv.color_palette(num)

plantcv.crop_position_mask

v3.0dev2之前: device, newmask = plantcv.crop_position_mask(img, mask, device, x, y, v_pos="top", h_pos="right", debug=None)
v3.0dev2之后: newmask = plantcv.crop_position_mask(img, mask, x, y, v_pos="top", h_pos="right")

plantcv.define_roi

v3.0dev2之前: device, contour, hierarchy = plantcv.define_roi(img, shape, device, roi=None, roi_input='default', debug=None, adjust=False, x_adj=0, y_adj=0, w_adj=0, h_adj=0)
v3.0dev2之后: Deprecated, see:
- roi_contour, roi_hierarchy = plantcv.roi.circle(img, x, y, r)
- roi_contour, roi_hierarchy = plantcv.roi.ellipse(img, x, y, r1, r2, angle)
- roi_contour, roi_hierarchy = plantcv.roi.from_binary_image(img, bin_img)
- roi_contour, roi_hierarchy = plantcv.roi.rectangle(img, x, y, h, w)

plantcv.dilate

v3.0dev2之前: device, dil_img = plantcv.dilate(img, kernel, i, device, debug=None)
v3.0dev2之后: dil_img = plantcv.dilate(gray_img, kernel, i)
v3.2之后: dil_img = plantcv.dilate(gray_img, ksize, i)

plantcv.distance_transform

v3.0dev2之前: device, norm_image = plantcv.distance_transform(img, distanceType, maskSize, device, debug=None)
v3.0dev2之后: norm_image = plantcv.distance_transform(bin_img, distance_type, mask_size)

plantcv.erode

v3.0dev2之前: device, er_img = plantcv.erode(img, kernel, i, device, debug=None)
v3.0dev2之后: er_img = plantcv.erode(gray_img, kernel, i)
v3.2之后: er_img = plantcv.erode(gray_img, ksize, i)

plantcv.fill

v3.0dev2之前: device, filtered_img = plantcv.fill(img, mask, size, device, debug=None)
v3.0dev2之后: filtered_img = plantcv.fill(bin_img, size)

plantcv.fill_holes

v3.3之前: NA
v3.3之后: filtered_img = plantcv.fill(bin_img)

plantcv.find_objects

v3.0dev2之前: device, objects, hierarchy = plantcv.find_objects(img, mask, device, debug=None)
v3.0dev2之后: objects, hierarchy = plantcv.find_objects(img, mask)

plantcv.flip

v3.0dev2之前: device, vh_img = plantcv.flip(img, direction, device, debug=None)
v3.0dev2之后: vh_img = plantcv.flip(img, direction)

plantcv.fluor_fvfm

v3.0dev2之前: device, hist_header, hist_data = plantcv.fluor_fvfm(fdark, fmin, fmax, mask, device, filename, bins=1000, debug=None)
v3.0dev2之后: hist_header, hist_data, hist_images = plantcv.fluor_fvfm(fdark, fmin, fmax, mask, filename, bins=256)
v3.0之后: hist_header, hist_data, analysis_images = plantcv.fluor_fvfm(fdark, fmin, fmax, mask, bins=256)
v3.3之后: analysis_images = plantcv.fluor_fvfm(fdark, fmin, fmax, mask, bins=256)

plantcv.gaussian_blur

v3.0dev2之前: device, img_gblur = plantcv.gaussian_blur(device, img, ksize, sigmax=0, sigmay=None, debug=None)
v3.0dev2之后: img_gblur = plantcv.gaussian_blur(img, ksize, sigmax=0, sigmay=None)
v3.2之后: img_gblur = plantcv.gaussian_blur(img, ksize, sigma_x=0, sigma_y=None)

plantcv.get_nir

v3.0dev2之前: device, nirpath = plantcv.get_nir(path, filename, device, debug=None)
v3.0dev2之后: nirpath = plantcv.get_nir(path, filename)

plantcv.hist_equalization

v3.0dev2之前: device, img_eh = plantcv.hist_equalization(img, device, debug=None)
v3.0dev2之后: img_eh = plantcv.hist_equalization(gray_img)

plantcv.hyperspectral.analyze_index

v3.7之前: NA
v3.7之后: plantcv.hyperspectral.analyze_index(index_array, mask)
v3.8之后: index_histogram = plantcv.hyperspectral.analyze_index(index_array, mask, histplot=False, bins=100, max_bin=0, min_bin=1)

plantcv.hyperspectral.analyze_spectral

v3.7之前: NA
v3.7之后: spectral_histogram = plantcv.hyperspectral.analyze_spectral(array, mask, histplot=True)

plantcv.hyperspectral.extract_index

v3.7之前: NA
v3.7之后: index_array = plantcv.hyperspectral.extract_index(array, index="NDVI", distance=20)
v3.8之后: DEPRECATED see plantcv.spectral_index

plantcv.image_add

v3.0dev2之前: device, added_img = plantcv.image_add(img1, img2, device, debug=None)
v3.0dev2之后: added_img = plantcv.image_add(gray_img1, gray_img2)

plantcv.image_subtract

v3.0之前: NA
v3.0之后: new_img = plantcv.image_subtract(gray_img1, gray_img2)

plantcv.invert

v3.0dev2之前: device, img_inv = plantcv.invert(img, device, debug=None)
v3.0dev2之后: img_inv = plantcv.invert(gray_img)

plantcv.landmark_reference_pt_dist

v3.0dev2之前: device, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b = plantcv.landmark_reference_pt_dist(points_r, centroid_r, bline_r, device, debug=None)
v3.0dev2之后: vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b = plantcv.landmark_reference_pt_dist(points_r, centroid_r, bline_r)
v3.2之后: landmark_header, landmark_data = plantcv.landmark_reference_pt_dist(points_r, centroid_r, bline_r)
v3.3之后: plantcv.landmark_reference_pt_dist(points_r, centroid_r, bline_r)

plantcv.laplace_filter

v3.0dev2之前: device, lp_filtered = plantcv.laplace_filter(img, k, scale, device, debug=None)
v3.0dev2之后: lp_filtered = plantcv.laplace_filter(gray_img, k, scale)
v3.2之后: lp_filtered = plantcv.laplace_filter(gray_img, ksize, scale)

plantcv.logical_and

v3.0dev2之前: device, merged = plantcv.logical_and(img1, img2, device, debug=None)
v3.0dev2之后: merged = plantcv.logical_and(bin_img1, bin_img2)

plantcv.logical_or

v3.0dev2之前: device, merged = plantcv.logical_or(img1, img2, device, debug=None)
v3.0dev2之后: merged = plantcv.logical_or(bin_img1, bin_img2)

plantcv.logical_xor

v3.0dev2之前: device, merged = plantcv.logical_xor(img1, img2, device, debug=None)
v3.0dev2之后: merged = plantcv.logical_xor(bin_img1, bin_img2)

plantcv.median_blur

v3.0dev2之前: device, img_mblur = plantcv.median_blur(img, ksize, device, debug=None)
v3.0dev2之后: img_mblur = plantcv.median_blur(gray_img, ksize)
v3.2之后: img_blur = plantcv.median_blur(gray_img, ksize) OR img_blur = plantcv.median_blur(gray_img, (ksize1, ksize2))

plantcv.morphology.check_cycles

v3.3之前: NA
v3.3之后: cycle_img = plantcv.morphology.check_cycles(skel_img)

plantcv.morphology.find_branch_pts

v3.3之前: NA
v3.3之后: branch_pts_img = plantcv.morphology.find_branch_pts(skel_img, mask=None)

plantcv.morphology.find_tips

v3.3之前: NA
v3.3之后: tip_img = plantcv.morphology.find_tips(skel_img, mask=None)

plantcv.morphology.prune

v3.3之前: NA
v3.3之后: pruned_img = plantcv.morphology.prune(skel_img, size)
v3.4之后: pruned_skeleton, segmented_img, segment_objects = plantcv.morphology.prune(skel_img, size=0, mask=None)

plantcv.morphology.segment_angle

v3.3之前: NA
v3.3之后: labeled_img = plantcv.morphology.segment_angle(segmented_img, objects)

plantcv.morphology.curvature

v3.3之前: NA
v3.3之后: labeled_img = plantcv.morphology.segment_curvature(segmented_img, objects)

plantcv.morphology.segment_euclidean_length

v3.3之前: NA
v3.3之后: labeled_img = plantcv.morphology.segment_euclidean_length(segmented_img, objects)

plantcv.morphology.segment_id

v3.3之前: NA
v3.3之后: segmented_img, labeled_img = plantcv.morphology.segment_id(skel_img, objects, mask=None)

plantcv.morphology.segment_path_length

v3.3之前: NA
v3.3之后: labeled_img = plantcv.morphology.segment_path_length(segmented_img, objects)

plantcv.morphology.segment_skeleton

v3.3之前: NA
v3.3之后: segmented_img, segment_objects = plantcv.morphology.segment_skeleton(skel_img, mask=None)

plantcv.morphology.segment_sort

v3.3之前: NA
v3.3之后: secondary_objects, primary_objects = plantcv.morphology.segment_sort(skel_img, objects, mask=None)

plantcv.morphology.segment_tangent_angle

v3.3之前: NA
v3.3之后: labeled_img = plantcv.morphology.segment_tangent_angle(segmented_img, objects, size)

plantcv.morphology.skeletontize

v3.3之前: NA
v3.3之后: skeleton = plantcv.morphology.skeletonize(mask)

plantcv.naive_bayes_classifier

v3.0dev2之前: device, masks = plantcv.naive_bayes_classifier(img, pdf_file, device, debug=None)**
v3.0dev2之后: masks = plantcv.naive_bayes_classifier(rgb_img, pdf_file)**

plantcv.object_composition

v3.0dev2之前: device, group, mask = plantcv.object_composition(img, contours, hierarchy, device, debug=None)
v3.0dev2之后: group, mask = plantcv.object_composition(img, contours, hierarchy)

plantcv.opening

v3.3之前: NA
v3.3之后: filtered_img = plantcv.opening(gray_img, kernel=None)

plantcv.otsu_auto_threshold

v3.0dev2之前: device, bin_img = plantcv.otsu_auto_threshold(img, maxValue, object_type, device, debug=None)
v3.0dev2之后: Deprecated, see:
- bin_img = plantcv.threshold.otsu(gray_img, max_value, object_type="light")

plantcv.output_mask

v3.0dev2之前: device, maskpath, analysis_images = plantcv.output_mask(device, img, mask, filename, outdir=None, mask_only=False, debug=None)
v3.0dev2之后: imgpath, maskpath, analysis_images = plantcv.output_mask(img, mask, filename, outdir=None, mask_only=False)

plantcv.plot_hist

v3.0dev2之前: bins, hist = plantcv.plot_hist(img, name=False)
v3.0dev2之后: bins, hist = plantcv.plot_hist(img, name=False)
v3.2之后: Deprecated, see:
- hist_header, hist_data, fig_hist = plantcv.visualize.histogram(gray_img, mask=None, bins=256)

plantcv.plot_image

v3.0dev2之前: plantcv.plot_image(img, cmap=None)
v3.0dev2之后: plantcv.plot_image(img, cmap=None)

plantcv.print_image

v3.0dev2之前: plantcv.print_image(img, filename)
v3.0dev2之后: plantcv.print_image(img, filename)

plantcv.print_results

v3.1之前: NA
v3.1之后: plantcv.print_results(filename)

plantcv.pseudocolor

v3.1之前: NA
v3.1之后: pseudo_img = plantcv.pseudocolor(gray_img, obj=None, mask=None, cmap=None, background="image", min_value=0, max_value=255, dpi=None, axes=True, colorbar=True)
v3.2之后: Deprecated, see:
- pseudo_img = plantcv.visualize.pseudocolor(gray_img, obj=None, mask=None, cmap=None, background="image", min_value=0, max_value=255, axes=True, colorbar=True)

plantcv.readbayer

v3.0之前: NA
v3.0之后: img, path, img_name = plantcv.readbayer(filename, bayerpattern = 'BG', alg = 'default')

plantcv.readimage

v3.0dev2之前: img, path, img_name = plantcv.readimage(filename, debug=None)
v3.0dev2之后: img, path, img_name = plantcv.readimage(filename, mode="native")

plantcv.rectangle_mask

v3.0dev2之前: device, img1, bnk, contour, hierarchy = plantcv.rectangle_mask(img, p1, p2, device, debug=None, color="black")
v3.0dev2之后: img1, bnk, contour, hierarchy = plantcv.rectangle_mask(img, p1, p2, color="black")

plantcv.report_size_marker_area

v3.0dev2之前: device, marker_header, marker_data, analysis_images = plantcv.report_size_marker_area(img, shape, device, debug, marker='define', x_adj=0, y_adj=0, w_adj=0, h_adj=0, base='white', objcolor='dark', thresh_channel=None, thresh=None, filename=False)
v3.0dev2之后: marker_header, marker_data, analysis_images = plantcv.report_size_marker_area(img, roi_contour, roi_hierarchy, marker='define', objcolor='dark', thresh_channel=None, thresh=None, filename=False)
v3.1之后: marker_header, marker_data, analysis_image = plantcv.report_size_marker_area(img, roi_contour, roi_hierarchy, marker='define', objcolor='dark', thresh_channel=None, thresh=None)
v3.3之后: analysis_image = plantcv.report_size_marker_area(img, roi_contour, roi_hierarchy, marker='define', objcolor='dark', thresh_channel=None, thresh=None)

plantcv.resize

v3.0dev2之前: device, reimg = plantcv.resize(img, resize_x, resize_y, device, debug=None)
v3.0dev2之后: reimg = plantcv.resize(img, resize_x, resize_y)

plantcv.rgb2gray

v3.0dev2之前: device, gray = plantcv.rgb2gray(img, device, debug=None)
v3.0dev2之后: gray = plantcv.rgb2gray(rgb_img)

plantcv.rgb2gray_hsv

v3.0dev2之前: device, gray = plantcv.rgb2gray_hsv(img, channel, device, debug=None)
v3.0dev2之后: gray = plantcv.rgb2gray_hsv(rgb_img, channel)

plantcv.rgb2gray_lab

v3.0dev2之前: device, gray = plantcv.rgb2gray_lab(img, channel, device, debug=None)
v3.0dev2之后: gray = plantcv.rgb2gray_lab(rgb_img, channel)

plantcv.roi.circle

v3.0dev之前1: NA
v3.0dev2之后: roi_contour, roi_hierarchy = plantcv.roi.circle(x, y, r, img)
v3.2之后: roi_contour, roi_hierarchy = plantcv.roi.circle(img, x, y, r)

plantcv.roi.ellipse

v3.0dev之前1: NA
v3.0dev2之后: roi_contour, roi_hierarchy = plantcv.roi.ellipse(x, y, r1, r2, angle, img)
v3.2之后: roi_contour, roi_hierarchy = plantcv.roi.ellipse(img, x, y, r1, r2, angle)

plantcv.roi.from_binary_image

v3.0dev之前1: NA
v3.0dev2之后: roi_contour, roi_hierarchy = plantcv.roi.from_binary_image(bin_img, img)
v3.2之后: roi_contour, roi_hierarchy = plantcv.roi.from_binary(img, bin_img)

plantcv.roi.rectangle

v3.0dev之前1: NA
v3.0dev2之后: roi_contour, roi_hierarchy = plantcv.roi.rectangle(x, y, h, w, img)
v3.2之后: roi_contour, roi_hierarchy = plantcv.roi.rectangle(img, x, y, h, w)

plantcv.roi.roi2mask

v3.8之前: NA
v3.8之后: mask = pcv.roi.roi2mask(img, contour)

plantcv.roi.multi

v3.1之前: NA
v3.1之后: roi_contours, roi_hierarchies = plantcv.roi.multi(img, coord, radius, spacing=None, nrows=None, ncols=None)

plantcv.roi_objects

v3.0dev2之前: device, kept_cnt, hierarchy, mask, obj_area = plantcv.roi_objects(img, roi_type, roi_contour, roi_hierarchy, object_contour, obj_hierarchy, device, debug=None)
v3.0dev2之后: kept_cnt, hierarchy, mask, obj_area = plantcv.roi_objects(img, roi_type, roi_contour, roi_hierarchy, object_contour, obj_hierarchy)
v3.3之后: kept_cnt, hierarchy, mask, obj_area = plantcv.roi_objects(img, roi_contour, roi_hierarchy, object_contour, obj_hierarchy,roi_type='partial')

plantcv.rotate

v3.0dev2之前: device, rotated_img = plantcv.rotate(img, rotation_deg, crop, device, debug=None)
v3.0dev2之后: rotated_img = plantcv.rotate(img, rotation_deg, crop)

plantcv.rotate_img

v3.0dev2之前: device, rotated_img = plantcv.rotate_img(img, rotation_deg, device, debug=None)
v3.0dev2之后: Deprecated, see:
- rotated_img = plantcv.rotate(img, rotation_deg, crop)

plantcv.scale_features

v3.0dev2之前: device, rescaled, centroid_scaled, boundary_line_scaled = plantcv.scale_features(obj, mask, points, boundary_line, device, debug=None)
v3.0dev2之后: rescaled, centroid_scaled, boundary_line_scaled = plantcv.scale_features(obj, mask, points, boundary_line)
v3.2之后: rescaled, centroid_scaled, boundary_line_scaled = plantcv.scale_features(obj, mask, points, line_position)

plantcv.scharr_filter

v3.0dev2之前: device, sr_img = plantcv.scharr_filter(img, dX, dY, scale, device, debug=None)
v3.0dev2之后: sr_img = plantcv.scharr_filter(gray_img, dx, dy, scale)

plantcv.shift_img

v3.0dev2之前: device, adjusted_img = plantcv.shift_img(img, device, number, side="right", debug=None)
v3.0dev2之后: adjusted_img = plantcv.shift_img(img, number, side="right")

plantcv.sobel_filter

v3.0dev2之前: device, sb_img = plantcv.sobel_filter(img, dx, dy, k, device, debug=None)
v3.0dev2之后: sb_img = plantcv.sobel_filter(gray_img, dx, dy, k)
v3.2之后: sb_img = plantcv.sobel_filer(gray_img, dx, dy, ksize)

plantcv.spectral_index.ndvi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.ndvi(hsi, distance=20)

plantcv.spectral_index.gdvi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.gdvi(hsi, distance=20)

plantcv.spectral_index.savi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.savi(hsi, distance=20)

plantcv.spectral_index.pri(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.pri(hsi, distance=20)

plantcv.spectral_index.ari(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.ari(hsi, distance=20)

plantcv.spectral_index.ci_rededge(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.ci_rededge(hsi, distance=20)

plantcv.spectral_index.cri550(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.cri550(hsi, distance=20)

plantcv.spectral_index.cri700(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.cri700(hsi, distance=20)

plantcv.spectral_index.egi(rgb_img)

v3.8之后: array = plantcv.spectral_index.egi(rgb_img)

plantcv.spectral_index.evi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.evi(hsi, distance=20)

plantcv.spectral_index.mari(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.mari(hsi, distance=20)

plantcv.spectral_index.mcari(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.mcari(hsi, distance=20)

plantcv.spectral_index.mtci(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.mtci(hsi, distance=20)

plantcv.spectral_index.ndre(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.ndre(hsi, distance=20)

plantcv.spectral_index.psnd_chla(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.psnd_chla(hsi, distance=20)

plantcv.spectral_index.psnd_chlb(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.psnd_chlb(hsi, distance=20)

plantcv.spectral_index.psnd_car(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.psnd_car(hsi, distance=20)

plantcv.spectral_index.psri(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.psri(hsi, distance=20)

plantcv.spectral_index.pssr_chla(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.pssr_chla(hsi, distance=20)

plantcv.spectral_index.pssr_chlb(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.pssr_chlb(hsi, distance=20)

plantcv.spectral_index.pssr_car(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.pssr_car(hsi, distance=20)

plantcv.spectral_index.rgri(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.rgri(hsi, distance=20)

plantcv.spectral_index.rvsi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.rvsi(hsi, distance=20)

plantcv.spectral_index.sipi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.sipi(hsi, distance=20)

plantcv.spectral_index.sr(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.sr(hsi, distance=20)

plantcv.spectral_index.vari(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.vari(hsi, distance=20)

plantcv.spectral_index.vi_green(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.vi_green(hsi, distance=20)

plantcv.spectral_index.wi(hsi, distance=20)

v3.8之后: array = plantcv.spectral_index.wi(hsi, distance=20)

plantcv.stdev_filter

v3.9之前: NA
v3.9之后: filtered_img = plantcv.stdev_filter(img, kszie, borders="nearest")

plantcv.threshold.binary

v3.0dev2之前: NA
v3.0dev2之后: bin_img = plantcv.threshold.binary(gray_img, threshold, max_value, object_type="light")

plantcv.threshold.custom_range

v3.3之前: NA
v3.3之后: mask, masked_img = plantcv.threshold.custom_range(rgb_img, lower_thresh, upper_thresh, channel='RGB')**
v3.8之后: mask, masked_img = plantcv.threshold.custom_range(img, lower_thresh, upper_thresh, channel='RGB')**

plantcv.threshold.gaussian

v3.0dev2之前: NA
v3.0dev2之后: bin_img = plantcv.threshold.gaussian(gray_img, max_value, object_type="light")

plantcv.threshold.mean

v3.0dev2之前: NA
v3.0dev2之后: bin_img = plantcv.threshold.mean(gray_img, max_value, object_type="light")

plantcv.threshold.otsu

v3.0dev2之前: NA
v3.0dev2之后: bin_img = plantcv.threshold.otsu(gray_img, max_value, object_type="light")

plantcv.threshold.saturation

v3.8之前: NA
v3.8之后: bin_img = plantcv.threshold.saturation(*rgb_img, threshold=255, channel="any")

plantcv.threshold.texture_filter

v3.0之前: NA
v3.0之后: bin_img = plantcv.threshold.texture_filter(gray_img, ksize, threshold, offset=3, texture_method='dissimilarity', borders='nearest', max_value=255)

plantcv.threshold.triangle

v3.0dev2之前: NA
v3.0dev2之后: bin_img = plantcv.threshold.triangle(gray_img, max_value, object_type="light", xstep=1)

plantcv.transform.apply_transformation_matrix

v3.0dev之前1: NA
v3.0dev2之后: corrected_img = plantcv.transform.apply_transformation_matrix(source_img, target_img, transformation_matrix)

plantcv.transform.calc_transformation_matrix

v3.0dev之前1: NA
v3.0dev2之后: determinant, transformation_matrix = plantcv.transform.calc_transformation_matrix(matrix_m, matrix_b)

plantcv.transform.correct_color

v3.0dev之前1: NA
v3.0dev2之后: target_matrix, source_matrix, transformation_matrix, corrected_img = plantcv.transform.correct_color(target_img, target_mask, source_img, source_mask, output_directory)

plantcv.transform.create_color_card_mask

v3.0之前: NA
v3.0之后: mask = pcv.transform.create_color_card_mask(rgb_img, radius, start_coord, spacing, nrows, ncols, exclude=[])

plantcv.transform.find_color_card

v3.0之前: NA
v3.0之后: df, start_coord, spacing = plantcv.transform.find_color_card(rgb_img, threshold='adaptgauss', threshvalue=125, blurry=False, background='dark')
v3.3之后: df, start_coord, spacing = plantcv.transform.find_color_card(rgb_img, threshold_type='adaptgauss', threshvalue=125, blurry=False, background='dark')
v3.9之后: df, start_coord, spacing = plantcv.transform.find_color_card(rgb_img, threshold_type='adaptgauss', threshvalue=125, blurry=False, background='dark', record_chip_size='median')

plantcv.transform.get_color_matrix

v3.0dev之前1: NA
v3.0dev2之后: headers, color_matrix = plantcv.transform.get_color_matrix(rgb_img, mask)

plantcv.transform.get_matrix_m

v3.0dev之前1: NA
v3.0dev2之后: matrix_a, matrix_m, matrix_b = plantcv.transform.get_matrix_m(target_matrix, source_matrix)

plantcv.transform.load_matrix

v3.0dev之前1: NA
v3.0dev2之后: matrix = plantcv.transform.load_matrix(filename)

plantcv.transform.nonuniform_illumination

v3.5之前: NA
v3.5之后: corrected_img = plantcv.transform.nonuniform_illumination(img, ksize)

plantcv.transform.quick_color_check

v3.0之前: NA
v3.0之后: plantcv.transform.quick_color_check(target_matrix, source_matrix, num_chips)

plantcv.transform.save_matrix

v3.0dev之前1: NA
v3.0dev2之后: plantcv.transform.save_matrix(matrix, filename)

plantcv.triangle_auto_threshold

v3.0dev2之前: device, bin_img = plantcv.triangle_auto_threshold(device, img, maxvalue, object_type, xstep=1, debug=None)
v3.0dev2之后: Deprecated, see:
- bin_img = plantcv.threshold.triangle(gray_img, max_value, object_type="light", xstep=1)

plantcv.visualize.colorize_masks

v3.2之前: NA
v3.2之后: colored_img = pcv.visualize.colorize_masks(classes, colors)

plantcv.visualize.colorspaces

v3.8之前: NA
v3.8之后: plotting_img = pcv.visualize.colorspaces(rgb_img)

plantcv.visualize.histogram

v3.2之前: bins, hist = plantcv.plot_hist(img, name=False)
v3.2之后: hist_header, hist_data, fig_hist = plantcv.visualize.histogram(gray_img, mask=None, bins=256)
v3.3之后: hist_header, hist_data, fig_hist = plantcv.visualize.histogram(gray_img, mask=None, bins=256, color='red', title=None)
v3.5之后: fig_hist = plantcv.visualize.histogram(gray_img, mask=None, bins=256, color='red', title=None)

plantcv.visualize.pseudocolor

v3.2之前: pseudo_img = plantcv.pseudocolor(gray_img, obj=None, mask=None, cmap=None, background="image", min_value=0, max_value=255, dpi=None, axes=True, colorbar=True)
v3.2之后: pseudo_img = plantcv.visualize.pseudocolor(gray_img, obj=None, mask=None, cmap=None, background="image", min_value=0, max_value=255, dpi=None, axes=True, colorbar=True)
v3.3之后: pseudo_img = plantcv.visualize.pseudocolor(gray_img, obj=None, mask=None, cmap=None, background="image", min_value=0, max_value=255, axes=True, colorbar=True)

plantcv.watershed_segmentation

v3.0dev2之前: device, watershed_header, watershed_data, analysis_images = plantcv.watershed_segmentation(device, img, mask, distance=10, filename=False, debug=None)
v3.0dev2之后: watershed_header, watershed_data, analysis_images = plantcv.watershed_segmentation(rgb_img, mask, distance=10, filename=False)
v3.1之后: watershed_header, watershed_data, analysis_images = plantcv.watershed_segmentation(rgb_img, mask, distance=10)
v3.3之后: analysis_image = plantcv.watershed_segmentation(rgb_img, mask, distance=10)

plantcv.white_balance

v3.0dev2之前: device, finalcorrected = plantcv.white_balance(device, img, mode='hist',debug=None, roi=None)
v3.0dev2之后: finalcorrected = plantcv.white_balance(img, mode='hist', roi=None)

plantcv.within_frame

v3.3之前: NA
v3.3之后: in_bounds = plantcv.within_frame(mask)
v3.8之后: in_bounds = plantcv.within_frame(mask, border_width=1)

plantcv.x_axis_pseudolandmarks

v3.0dev2之前: device, top, bottom, center_v = plantcv.x_axis_pseudolandmarks(obj, mask, img, device, debug=None)
v3.0dev2之后: top, bottom, center_v = plantcv.x_axis_pseudolandmarks(obj, mask, img)
v3.2之后: top, bottom, center_v = plantcv.x_axis_pseudolandmarks(img, obj, mask)

plantcv.y_axis_pseudolandmarks

v3.0dev2之前: device, left, right, center_h = plantcv.y_axis_pseudolandmarks(obj, mask, img, device, debug=None)
v3.0dev2之后: left, right, center_h = plantcv.y_axis_pseudolandmarks(obj, mask, img)
v3.2之后: left, right, center_h = plantcv.y_axis_pseudolandmarks(img, obj, mask)