image_analysis_plantcv/start_here_single_plant_notebook_2023.ipynb

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3 (ipykernel)","language":"python"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%matplotlib widget\nimport os\nimport argparse\nimport matplotlib\nimport numpy as np\n\nfrom plantcv import plantcv as pcv\npcv.__version__","metadata":{"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"'3.14.2'"},"metadata":{}}]},{"cell_type":"code","source":"class options:\n    def __init__(self):        \n        self.image = \"imgs/VIS_SV_0_z500_h1_g0_e82_380123_0.png\" \n        self.debug = \"plot\"\n        self.writeimg = False\n        self.result = \"data_output\"\n        self.outdir = \"img_output\"\nargs = options()\npcv.params.debug_outdir = args.outdir\npcv.params.debug = args.debug","metadata":{"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"#read in image","metadata":{"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"# Update params related to plotting so we can see better \n# visualize color space\n","metadata":{"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"#### Create a mask and begin to clean it ####\n# Change color space for plant extraction\n","metadata":{"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"#visualize histogram\n\n","metadata":{"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"# Threshold\n","metadata":{"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# Fill in small objects \n","metadata":{"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"#find objects\n","metadata":{"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"# define region of interest\n\n","metadata":{"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"# find objects in ROI\n","metadata":{"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"# Object combine kept objects\n\n# Inputs:\n#   img - RGB or grayscale image data for plotting \n#   contours - Contour list \n#   hierarchy - Contour hierarchy array \n","metadata":{"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"############### Analysis ################ \n  \n# Find shape properties, data gets stored to an Outputs class automatically\n\n# Inputs:\n#   img - RGB or grayscale image data \n#   obj- Single or grouped contour object\n#   mask - Binary image mask to use as mask for moments analysis \n#   label - Optional label parameter, modifies the variable name of observations recorded. (default `label=\"default\"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)\n\n","metadata":{"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"code","source":"# Shape properties relative to user boundary line (optional)\n\n# Inputs:\n#   img - RGB or grayscale image data \n#   obj - Single or grouped contour object \n#   mask - Binary mask of selected contours \n#   line_position - Position of boundary line (a value of 0 would draw a line \n#                   through the bottom of the image) \n#   label - Optional label parameter, modifies the variable name of observations recorded. (default `label=\"default\"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)\n\n","metadata":{"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"# Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)\n\n# Inputs:\n#   rgb_img - RGB image data\n#   mask - Binary mask of selected contours \n#   colorspaces - 'all' (default), 'rgb', 'lab', or 'hsv'\n#                 This is the data to be printed to the SVG histogram file  \n#   label - Optional label parameter, modifies the variable name of observations recorded. (default `label=\"default\"`)\n\n","metadata":{"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"# pcv.print_results(filename=args.result)\n","metadata":{"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}