def write_output(args, i): """Write Outputs for a single ROI Parameters ---------- args : dict commandline arguments and image metadata i : int ROI number Returns ------- Notes ----- Side effect of printing a json file to disk """ # Here I will name the results file with the ROI ID combined with the original result filename basename, ext = os.path.splitext(args.result) filename = basename + "-roi" + str(i) + ext # Save the existing metadata to the new file with open(filename, "w") as r: json.dump(args.metadata, r) pcv.print_results(filename=filename) # The results are saved, now clear out the observations so the next loop adds new ones for the next plant pcv.outputs.clear()
def main(): # Get options args = options() # Set variables device = 0 pcv.params.debug = args.debug img_file = args.image # Read image img, path, filename = pcv.readimage(filename=img_file, mode='rgb') # Process saturation channel from HSV colour space s = pcv.rgb2gray_hsv(rgb_img=img, channel='s') lp_s = pcv.laplace_filter(s, 1, 1) shrp_s = pcv.image_subtract(s, lp_s) s_eq = pcv.hist_equalization(shrp_s) s_thresh = pcv.threshold.binary(gray_img=s_eq, threshold=215, max_value=255, object_type='light') s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5) # Process green-magenta channel from LAB colour space b = pcv.rgb2gray_lab(rgb_img=img, channel='a') b_lp = pcv.laplace_filter(b, 1, 1) b_shrp = pcv.image_subtract(b, b_lp) b_thresh = pcv.threshold.otsu(b_shrp, 255, object_type='dark') # Create and apply mask bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh) filled = pcv.fill_holes(bs) masked = pcv.apply_mask(img=img, mask=filled, mask_color='white') # Extract colour channels from masked image masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a') masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b') # Threshold the green-magenta and blue images maskeda_thresh = pcv.threshold.binary(gray_img=masked_a, threshold=115, max_value=255, object_type='dark') maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a, threshold=140, max_value=255, object_type='light') maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light') # Join the thresholded saturation and blue-yellow images (OR) ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1) # Produce and apply a mask opened_ab = pcv.opening(gray_img=ab) ab_fill = pcv.fill(bin_img=ab, size=200) closed_ab = pcv.closing(gray_img=ab_fill) masked2 = pcv.apply_mask(img=masked, mask=bs, mask_color='white') # Identify objects id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill) # Define region of interest (ROI) roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=250, y=100, h=200, w=200) # Decide what objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) ############### Analysis ################ outfile = False if args.writeimg == True: outfile = args.outdir + "/" + filename # Analyze the plant analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask) color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') top_x, bottom_x, center_v_x = pcv.x_axis_pseudolandmarks(img=img, obj=obj, mask=mask) top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask) # Print results of the analysis pcv.print_results(filename=args.result) pcv.output_mask(img, kept_mask, filename, outdir=args.outdir, mask_only=True)
def main(): # Set variables args = options() pcv.params.debug = args.debug # Read and rotate image img, path, filename = pcv.readimage(filename=args.image) img = pcv.rotate(img, -90, False) # Create mask from LAB b channel l = pcv.rgb2gray_lab(rgb_img=img, channel='b') l_thresh = pcv.threshold.binary(gray_img=l, threshold=115, max_value=255, object_type='dark') l_mblur = pcv.median_blur(gray_img=l_thresh, ksize=5) # Apply mask to image masked = pcv.apply_mask(img=img, mask=l_mblur, mask_color='white') ab_fill = pcv.fill(bin_img=l_mblur, size=50) # Extract plant object from image id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=ab_fill) roi1, roi_hierarchy = pcv.roi.rectangle(img=masked, x=150, y=270, h=100, w=100) roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) ############### Analysis ################ # Analyze shape properties analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask) boundary_image2 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=370) # Analyze colour properties color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') # Analyze shape independent of size top_x, bottom_x, center_v_x = pcv.x_axis_pseudolandmarks(img=img, obj=obj, mask=mask) top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask) # Print results pcv.print_results(filename='{}'.format(args.result)) pcv.print_image(img=color_histogram, filename='{}_color_hist.jpg'.format(args.outdir)) pcv.print_image(img=kept_mask, filename='{}_mask.jpg'.format(args.outdir))
def main(): # Initialize options args = options() # Set PlantCV debug mode to input debug method pcv.params.debug = args.debug # Use PlantCV to read in the input image. The function outputs an image as a NumPy array, the path to the file, # and the image filename img, path, filename = pcv.readimage(filename=args.image) # ## Segmentation # ### Saturation channel # Convert the RGB image to HSV colorspace and extract the saturation channel s = pcv.rgb2gray_hsv(rgb_img=img, channel='s') # Use a binary threshold to set an inflection value where all pixels in the grayscale saturation image below the # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white) s_thresh = pcv.threshold.binary(gray_img=s, threshold=80, max_value=255, object_type='light') # ### Blue-yellow channel # Convert the RGB image to LAB colorspace and extract the blue-yellow channel b = pcv.rgb2gray_lab(rgb_img=img, channel='b') # Use a binary threshold to set an inflection value where all pixels in the grayscale blue-yellow image below the # threshold get set to zero (pure black) and all pixels at or above the threshold get set to 255 (pure white) b_thresh = pcv.threshold.binary(gray_img=b, threshold=134, max_value=255, object_type='light') # ### Green-magenta channel # Convert the RGB image to LAB colorspace and extract the green-magenta channel a = pcv.rgb2gray_lab(rgb_img=img, channel='a') # In the green-magenta image the plant pixels are darker than the background. Setting object_type="dark" will # invert the image first and then use a binary threshold to set an inflection value where all pixels in the # grayscale green-magenta image below the threshold get set to zero (pure black) and all pixels at or above the # threshold get set to 255 (pure white) a_thresh = pcv.threshold.binary(gray_img=a, threshold=122, max_value=255, object_type='dark') # Combine the binary images for the saturation and blue-yellow channels. The "or" operator returns a binary image # that is white when a pixel was white in either or both input images bs = pcv.logical_or(bin_img1=s_thresh, bin_img2=b_thresh) # Combine the binary images for the combined saturation and blue-yellow channels and the green-magenta channel. # The "or" operator returns a binary image that is white when a pixel was white in either or both input images bsa = pcv.logical_or(bin_img1=bs, bin_img2=a_thresh) # The combined binary image labels plant pixels well but the background still has pixels labeled as foreground. # Small white noise (salt) in the background can be removed by filtering white objects in the image by size and # setting a size threshold where smaller objects can be removed bsa_fill1 = pcv.fill(bin_img=bsa, size=15) # Fill small noise # Before more stringent size filtering is done we want to connect plant parts that may still be disconnected from # the main plant. Use a dilation to expand the boundary of white regions. Ksize is the size of a box scanned # across the image and i is the number of times a scan is done bsa_fill2 = pcv.dilate(gray_img=bsa_fill1, ksize=3, i=3) # Remove small objects by size again but use a higher threshold bsa_fill3 = pcv.fill(bin_img=bsa_fill2, size=250) # Use the binary image to identify objects or connected components. id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=bsa_fill3) # Because the background still contains pixels labeled as foreground, the object list contains background. # Because these images were collected in an automated system the plant is always centered in the image at the # same position each time. Define a region of interest (ROI) to set the area where we expect to find plant # pixels. PlantCV can make simple ROI shapes like rectangles, circles, etc. but here we use a custom ROI to fit a # polygon around the plant area roi_custom, roi_hier_custom = pcv.roi.custom(img=img, vertices=[[1085, 1560], [1395, 1560], [1395, 1685], [1890, 1744], [1890, 25], [600, 25], [615, 1744], [1085, 1685]]) # Use the ROI to filter out objects found outside the ROI. When `roi_type = "cutto"` objects outside the ROI are # cropped out. The default `roi_type` is "partial" which allows objects to overlap the ROI and be retained roi_objects, hierarchy, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi_custom, roi_hierarchy=roi_hier_custom, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='cutto') # Filter remaining objects by size again to remove any remaining background objects filled_mask1 = pcv.fill(bin_img=kept_mask, size=350) # Use a closing operation to first dilate (expand) and then erode (shrink) the plant to fill in any additional # gaps in leaves or stems filled_mask2 = pcv.closing(gray_img=filled_mask1) # Remove holes or dark spot noise (pepper) in the plant binary image filled_mask3 = pcv.fill_holes(filled_mask2) # With the clean binary image identify the contour of the plant id_objects, obj_hierarchy = pcv.find_objects(img=img, mask=filled_mask3) # Because a plant or object of interest may be composed of multiple contours, it is required to combine all # remaining contours into a single contour before measurements can be done obj, mask = pcv.object_composition(img=img, contours=id_objects, hierarchy=obj_hierarchy) # ## Measurements PlantCV has several built-in measurement or analysis methods. Here, basic measurements of size # and shape are done. Additional typical modules would include plant height (`pcv.analyze_bound_horizontal`) and # color (`pcv.analyze_color`) shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask) # Save the shape image if requested if args.writeimg: outfile = os.path.join(args.outdir, filename[:-4] + "_shapes.png") pcv.print_image(img=shape_img, filename=outfile) # ## Morphology workflow # Update a few PlantCV parameters for plotting purposes pcv.params.text_size = 1.5 pcv.params.text_thickness = 5 pcv.params.line_thickness = 15 # Convert the plant mask into a "skeletonized" image where each path along the stem and leaves are a single pixel # wide skel = pcv.morphology.skeletonize(mask=mask) # Sometimes wide parts of leaves or stems are skeletonized in the direction perpendicular to the main path. These # "barbs" or "spurs" can be removed by pruning the skeleton to remove small paths. Pruning will also separate the # individual path segments (leaves and stem parts) pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=skel, size=30, mask=mask) pruned, segmented_img, segment_objects = pcv.morphology.prune(skel_img=pruned, size=3, mask=mask) # Leaf and stem segments above are separated but only into individual paths. We can sort the segments into stem # and leaf paths by identifying primary segments (stems; those that end in a branch point) and secondary segments # (leaves; those that begin at a branch point and end at a tip point) leaf_objects, other_objects = pcv.morphology.segment_sort(skel_img=pruned, objects=segment_objects, mask=mask) # Label the segment unique IDs segmented_img, labeled_id_img = pcv.morphology.segment_id(skel_img=pruned, objects=leaf_objects, mask=mask) # Measure leaf insertion angles. Measures the angle between a line fit through the stem paths and a line fit # through the first `size` points of each leaf path labeled_angle_img = pcv.morphology.segment_insertion_angle(skel_img=pruned, segmented_img=segmented_img, leaf_objects=leaf_objects, stem_objects=other_objects, size=22) # Save leaf angle image if requested if args.writeimg: outfile = os.path.join(args.outdir, filename[:-4] + "_leaf_insertion_angles.png") pcv.print_image(img=labeled_angle_img, filename=outfile) # ## Other potential morphological measurements There are many other functions that extract data from within the # morphology sub-package of PlantCV. For our purposes, we are most interested in the relative angle between each # leaf and the stem which we measure with `plantcv.morphology.segment_insertion_angle`. However, the following # cells show some of the other traits that we are able to measure from images that can be succesfully sorted into # primary and secondary segments. # Segment the plant binary mask using the leaf and stem segments. Allows for the measurement of individual leaf # areas # filled_img = pcv.morphology.fill_segments(mask=mask, objects=leaf_objects) # Measure the path length of each leaf (geodesic distance) # labeled_img2 = pcv.morphology.segment_path_length(segmented_img=segmented_img, objects=leaf_objects) # Measure the straight-line, branch point to tip distance (Euclidean) for each leaf # labeled_img3 = pcv.morphology.segment_euclidean_length(segmented_img=segmented_img, objects=leaf_objects) # Measure the curvature of each leaf (Values closer to 1 indicate that a segment is a straight line while larger # values indicate the segment has more curvature) # labeled_img4 = pcv.morphology.segment_curvature(segmented_img=segmented_img, objects=leaf_objects) # Measure absolute leaf angles (angle of linear regression line fit to each leaf object) Note: negative values # signify leaves to the left of the stem, positive values signify leaves to the right of the stem # labeled_img5 = pcv.morphology.segment_angle(segmented_img=segmented_img, objects=leaf_objects) # Measure leaf curvature in degrees # labeled_img6 = pcv.morphology.segment_tangent_angle(segmented_img=segmented_img, objects=leaf_objects, size=35) # Measure stem characteristics like stem angle and length # stem_img = pcv.morphology.analyze_stem(rgb_img=img, stem_objects=other_objects) # Remove unneeded observations (hack) _ = pcv.outputs.observations.pop("tips") _ = pcv.outputs.observations.pop("branch_pts") angles = pcv.outputs.observations["segment_insertion_angle"]["value"] remove_indices = [] for i, value in enumerate(angles): if value == "NA": remove_indices.append(i) remove_indices.sort(reverse=True) for i in remove_indices: _ = pcv.outputs.observations["segment_insertion_angle"]["value"].pop(i) # ## Save the results out to file for downsteam analysis pcv.print_results(filename=args.result)
obj=obj, mask=mask) # In[28]: top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask) # In[29]: # The print_results function will take the measurements stored when running any (or all) of these functions, format, # and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions # are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object, # fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file pcv.print_results(filename='upload/Results/VIS_results.txt') # In[30]: # Python program to convert text # file to JSON import json # the file to be converted to # json format filename = 'upload/Results/VIS_results.txt' # dictionary where the lines from # text will be stored dict1 = {}
def main(): # Get options args = options() pcv.params.debug = args.debug # set debug mode pcv.params.debug_outdir = args.outdir # set output directory # Read metadata with open(args.metadata, 'r', encoding='utf-8') as f: md = json.load(f) camera_label = md['camera_label'] # Read image img, path, filename = pcv.readimage(filename=args.image) # Convert RGB to HSV and extract the value channel s = pcv.rgb2gray_hsv(rgb_img=img, channel='v') # Threshold the saturation image removing highs and lows and join s_thresh_1 = pcv.threshold.binary(gray_img=s, threshold=10, max_value=255, object_type='light') s_thresh_2 = pcv.threshold.binary(gray_img=s, threshold=245, max_value=255, object_type='dark') s_thresh = pcv.logical_and(bin_img1=s_thresh_1, bin_img2=s_thresh_2) # Median Blur s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5) # Convert RGB to LAB and extract the Blue channel b = pcv.rgb2gray_lab(rgb_img=img, channel='b') # Threshold the blue image b_cnt = pcv.threshold.binary(gray_img=b, threshold=128, max_value=255, object_type='light') # Fill small objects b_fill = pcv.fill(b_cnt, 10) # Join the thresholded saturation and blue-yellow images bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_fill) # Apply Mask (for VIS images, mask_color=white) masked = pcv.apply_mask(rgb_img=img, mask=bs, mask_color='white') # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels # Threshold the green-magenta and blue images masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a') maskeda_thresh = pcv.threshold.binary(gray_img=masked_a, threshold=127, max_value=255, object_type='dark') # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels # Threshold the green-magenta and blue images masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b') maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light') # Join the thresholded saturation and blue-yellow images (OR) ab = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) # Fill small objects ab_fill = pcv.fill(bin_img=ab, size=200) # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white') # Identify objects id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill) # Define ROI W = 2472 H = 3296 if "far" in camera_label: # SIDE FAR w = 1600 h = 1200 pot = 230 #340 roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=(W - w) / 2, y=(H - h - pot), h=h, w=w) elif "lower" in camera_label: # SIDE LOWER w = 800 h = 2400 pot = 340 roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=1000 - w / 2, y=(H - h - pot), h=h, w=w) elif "upper" in camera_label: # SIDE UPPER w = 600 h = 800 pot = 550 roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=1400 - w / 2, y=(H - h - pot), h=h, w=w) elif "top" in camera_label: # TOP w = 450 h = 450 roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=(H - h) / 2, y=(W - w) / 2, h=h, w=w) # Decide which objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) #TODO: Update for plantCV metadata import for key in md.keys(): if str(md[key]).isdigit(): pcv.outputs.add_observation(variable=key, trait=key, method='', scale='', datatype=int, value=md[key], label='') else: pcv.outputs.add_observation(variable=key, trait=key, method='', scale='', datatype=str, value=md[key], label='') if obj is not None: # Find shape properties, output shape image (optional) shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask) # Shape properties relative to user boundary line (optional) #boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680) # Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional) color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') # Pseudocolor the grayscale image #pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=kept_mask, cmap='jet') #print(pcv.outputs.images) if args.writeimg == True: for idx, item in enumerate(pcv.outputs.images[0]): pcv.print_image(item, "{}_{}.png".format(args.result[:-5], idx)) # Write shape and color data to results file pcv.print_results(filename=args.result)
def silhouette_top(): "First we draw the picture from the 3D data" ######################################################################################################################################################################## x = [] y = [] z = [] image_top = Image.new("RGB", (width, height), color='white') draw = ImageDraw.Draw(image_top) data_3d = open(args.image, "r") orignal_file = args.image for line in data_3d: line = line.split(",") y.append(int(line[0])) x.append(int(line[1])) z.append(int(line[2])) i = 0 for point_x in x: point_y = y[i] draw.rectangle([point_x, point_y, point_x + 1, point_y + 1], fill="black") #rectange takes input [x0, y0, x1, y1] i += 1 image_top.save("top_temp.png") image_side = Image.new("RGB", (1280, 960), color='white') draw = ImageDraw.Draw(image_side) i = 0 for point_y in y: point_z = z[i] draw.rectangle([point_z, point_y, point_z + 1, point_y + 1], fill="black") #rectange takes input [x0, y0, x1, y1] i += 1 image_side.save("side_temp.png") ######################################################################################################################################################################## args.image = "top_temp.png" # Get options pcv.params.debug = args.debug #set debug mode pcv.params.debug_outdir = args.outdir #set output directory pcv.params.debug = args.debug # set debug mode pcv.params.debug_outdir = args.outdir # set output directory # Read image img, path, filename = pcv.readimage(filename=args.image) v = pcv.rgb2gray_hsv(rgb_img=img, channel='v') v_thresh, maskedv_image = pcv.threshold.custom_range(rgb_img=v, lower_thresh=[0], upper_thresh=[200], channel='gray') id_objects, obj_hierarchy = pcv.find_objects(img=maskedv_image, mask=v_thresh) # Define ROI roi1, roi_hierarchy = pcv.roi.rectangle(img=maskedv_image, x=0, y=0, h=height, w=width) # Decide which objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) outfile = args.outdir + "/" + filename # Shape properties relative to user boundary line (optional) boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680) new_im = Image.fromarray(boundary_img1) new_im.save("output//" + args.filename + "_top_boundary.png") # Find shape properties, output shape image (optional) shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask) new_im = Image.fromarray(shape_img) new_im.save("output//" + args.filename + "_top_shape.png") new_im.save("output//" + args.filename + "shape_img.png") GT = re.sub(pattern, replacement, files_names[file_counter]) pcv.outputs.add_observation(variable="genotype", trait="genotype", method="Regexed from the filename", scale=None, datatype=str, value=int(GT), label="GT") # Write shape and color data to results file pcv.print_results(filename=args.result) ########################################################################################################################################## args.image = "side_temp.png" # Get options pcv.params.debug = args.debug #set debug mode pcv.params.debug_outdir = args.outdir #set output directory pcv.params.debug = args.debug # set debug mode pcv.params.debug_outdir = args.outdir # set output directory # Read image img, path, filename = pcv.readimage(filename=args.image) v = pcv.rgb2gray_hsv(rgb_img=img, channel='v') v_thresh, maskedv_image = pcv.threshold.custom_range(rgb_img=v, lower_thresh=[0], upper_thresh=[200], channel='gray') id_objects, obj_hierarchy = pcv.find_objects(img=maskedv_image, mask=v_thresh) # Define ROI roi1, roi_hierarchy = pcv.roi.rectangle(img=maskedv_image, x=0, y=0, h=height, w=width) # Decide which objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) outfile = args.outdir + "/" + filename # Shape properties relative to user boundary line (optional) boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680) new_im = Image.fromarray(boundary_img1) new_im.save("output//" + args.filename + "_side_boundary.png") # Find shape properties, output shape image (optional) shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask) new_im = Image.fromarray(shape_img) new_im.save("output//" + args.filename + "_side_shape.png") GT = re.sub(pattern, replacement, files_names[file_counter]) pcv.outputs.add_observation(variable="genotype", trait="genotype", method="Regexed from the filename", scale=None, datatype=str, value=int(GT), label="GT") # Write shape and color data to results file pcv.print_results(filename=args.result_side)
obj=obj, mask=mask) # In[86]: top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask) # In[87]: # The print_results function will take the measurements stored when running any (or all) of these functions, format, # and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions # are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object, # fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file pcv.print_results(filename='upload/Results1/NIR_results.txt') # In[88]: # Python program to convert text # file to JSON import json # the file to be converted to # json format filename = 'upload/Results1/NIR_results.txt' # dictionary where the lines from # text will be stored dict1 = {}
def main(): # Get options args = options() if args.debug: pcv.params.debug = args.debug # set debug mode if args.debugdir: pcv.params.debug_outdir = args.debugdir # set debug directory os.makedirs(args.debugdir, exist_ok=True) # pixel_resolution # mm # see pixel_resolution.xlsx for calibration curve for pixel to mm translation pixelresolution = 0.052 # The result file should exist if plantcv-workflow.py was run if os.path.exists(args.result): # Open the result file results = open(args.result, "r") # The result file would have image metadata in it from plantcv-workflow.py, read it into memory metadata = json.load(results) # Close the file results.close() # Delete the file, we will create new ones os.remove(args.result) plantbarcode = metadata['metadata']['plantbarcode']['value'] print(plantbarcode, metadata['metadata']['timestamp']['value'], sep=' - ') else: # If the file did not exist (for testing), initialize metadata as an empty string metadata = "{}" regpat = re.compile(args.regex) plantbarcode = re.search(regpat, args.image).groups()[0] # read images and create mask img, _, fn = pcv.readimage(args.image) imagename = os.path.splitext(fn)[0] # create mask # taf=filters.try_all_threshold(s_img) ## remove background s_img = pcv.rgb2gray_hsv(img, 's') min_s = filters.threshold_minimum(s_img) thresh_s = pcv.threshold.binary(s_img, min_s, 255, 'light') rm_bkgrd = pcv.fill_holes(thresh_s) ## low greenness thresh_s = pcv.threshold.binary(s_img, min_s + 15, 255, 'dark') # taf = filters.try_all_threshold(s_img) c = pcv.logical_xor(rm_bkgrd, thresh_s) cinv = pcv.invert(c) cinv_f = pcv.fill(cinv, 500) cinv_f_c = pcv.closing(cinv_f, np.ones((5, 5))) cinv_f_c_e = pcv.erode(cinv_f_c, 2, 1) ## high greenness a_img = pcv.rgb2gray_lab(img, channel='a') # taf = filters.try_all_threshold(a_img) t_a = filters.threshold_isodata(a_img) thresh_a = pcv.threshold.binary(a_img, t_a, 255, 'dark') thresh_a = pcv.closing(thresh_a, np.ones((5, 5))) thresh_a_f = pcv.fill(thresh_a, 500) ## combined mask lor = pcv.logical_or(cinv_f_c_e, thresh_a_f) close = pcv.closing(lor, np.ones((2, 2))) fill = pcv.fill(close, 800) erode = pcv.erode(fill, 3, 1) fill2 = pcv.fill(erode, 1200) # dilate = pcv.dilate(fill2,2,2) mask = fill2 final_mask = np.zeros_like(mask) # Compute greenness # split color channels b, g, r = cv2.split(img) # print green intensity # g_img = pcv.visualize.pseudocolor(g, cmap='Greens', background='white', min_value=0, max_value=255, mask=mask, axes=False) # convert color channels to int16 so we can add them (values will be greater than 255 which is max of current uint8 format) g = g.astype('uint16') r = r.astype('uint16') b = b.astype('uint16') denom = g + r + b # greenness index out_flt = np.zeros_like(denom, dtype='float32') # divide green by sum of channels to compute greenness index with values 0-1 gi = np.divide(g, denom, out=out_flt, where=np.logical_and(denom != 0, mask > 0)) # find objects c, h = pcv.find_objects(img, mask) rc, rh = pcv.roi.multi(img, coord=[(1300, 900), (1300, 2400)], radius=350) # Turn off debug temporarily, otherwise there will be a lot of plots pcv.params.debug = None # Loop over each region of interest i = 0 rc_i = rc[i] for i, rc_i in enumerate(rc): rh_i = rh[i] # Add ROI number to output. Before roi_objects so result has NA if no object. pcv.outputs.add_observation(variable='roi', trait='roi', method='roi', scale='int', datatype=int, value=i, label='#') roi_obj, hierarchy_obj, submask, obj_area = pcv.roi_objects( img, roi_contour=rc_i, roi_hierarchy=rh_i, object_contour=c, obj_hierarchy=h, roi_type='partial') if obj_area == 0: print('\t!!! No object found in ROI', str(i)) pcv.outputs.add_observation( variable='plantarea', trait='plant area in sq mm', method='observations.area*pixelresolution^2', scale=pixelresolution, datatype="<class 'float'>", value=0, label='sq mm') else: # Combine multiple objects # ple plant objects within an roi together plant_object, plant_mask = pcv.object_composition( img=img, contours=roi_obj, hierarchy=hierarchy_obj) final_mask = pcv.image_add(final_mask, plant_mask) # Save greenness for individual ROI grnindex = np.mean(gi[np.where(plant_mask > 0)]) pcv.outputs.add_observation( variable='greenness_index', trait='mean normalized greenness index', method='g/sum(b+g+r)', scale='[0,1]', datatype="<class 'float'>", value=float(grnindex), label='/1') # Analyze all colors hist = pcv.analyze_color(img, plant_mask, 'all') # Analyze the shape of the current plant shape_img = pcv.analyze_object(img, plant_object, plant_mask) plant_area = pcv.outputs.observations['area'][ 'value'] * pixelresolution**2 pcv.outputs.add_observation( variable='plantarea', trait='plant area in sq mm', method='observations.area*pixelresolution^2', scale=pixelresolution, datatype="<class 'float'>", value=plant_area, label='sq mm') # end if-else # At this point we have observations for one plant # We can write these out to a unique results file # Here I will name the results file with the ROI ID combined with the original result filename basename, ext = os.path.splitext(args.result) filename = basename + "-roi" + str(i) + ext # Save the existing metadata to the new file with open(filename, "w") as r: json.dump(metadata, r) pcv.print_results(filename=filename) # The results are saved, now clear out the observations so the next loop adds new ones for the next plant pcv.outputs.clear() if args.writeimg and obj_area != 0: imgdir = os.path.join(args.outdir, 'shape_images', plantbarcode) os.makedirs(imgdir, exist_ok=True) pcv.print_image( shape_img, os.path.join(imgdir, imagename + '-roi' + str(i) + '-shape.png')) imgdir = os.path.join(args.outdir, 'colorhist_images', plantbarcode) os.makedirs(imgdir, exist_ok=True) pcv.print_image( hist, os.path.join(imgdir, imagename + '-roi' + str(i) + '-colorhist.png')) # end roi loop if args.writeimg: # save grnness image of entire tray imgdir = os.path.join(args.outdir, 'pseudocolor_images', plantbarcode) os.makedirs(imgdir, exist_ok=True) gi_img = pcv.visualize.pseudocolor(gi, obj=None, mask=final_mask, cmap='viridis', axes=False, min_value=0.3, max_value=0.6, background='black', obj_padding=0) gi_img = add_scalebar(gi_img, pixelresolution=pixelresolution, barwidth=20, barlocation='lower left') gi_img.set_size_inches(6, 6, forward=False) gi_img.savefig(os.path.join(imgdir, imagename + '-greenness.png'), bbox_inches='tight') gi_img.clf()
def main(): # Get options pcv.params.debug = args.debug #set debug mode pcv.params.debug_outdir = args.outdir #set output directory # Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb') # Inputs: # filename - Image file to be read in # mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv' img, path, filename = pcv.readimage(filename=args.image, mode='rgb') ### SELECTING THE PLANT ### Attempt 5 combineren # Parameters hue_lower_tresh = 22 # 24 hue_higher_tresh = 50 # 50 saturation_lower_tresh = 138 # 140 saturation_higher_tresh = 230 # 230 value_lower_tresh = 120 # 125 value_higher_tresh = 255 # 255 # RGB color space green_lower_tresh = 105 # 110 green_higher_tresh = 255 # 255 red_lower_tresh = 22 # 24 red_higher_thresh = 98 # 98 blue_lower_tresh = 85 # 85 blue_higher_tresh = 253 # 255 # CIELAB color space #lab_blue_lower_tresh = 0 # Blue yellow channel #lab_blue_higher_tresh = 255 s = pcv.rgb2gray_hsv(rgb_img=img, channel='h') mask, masked_image = pcv.threshold.custom_range( rgb_img=s, lower_thresh=[hue_lower_tresh], upper_thresh=[hue_higher_tresh], channel='gray') masked = pcv.apply_mask(rgb_img=img, mask=mask, mask_color='white') # Filtered on Hue s = pcv.rgb2gray_hsv(rgb_img=masked, channel='s') mask, masked_image = pcv.threshold.custom_range( rgb_img=s, lower_thresh=[saturation_lower_tresh], upper_thresh=[saturation_higher_tresh], channel='gray') masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') #filtered on saturation s = pcv.rgb2gray_hsv(rgb_img=masked, channel='v') mask, masked_image = pcv.threshold.custom_range( rgb_img=s, lower_thresh=[value_lower_tresh], upper_thresh=[value_higher_tresh], channel='gray') masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') #filtered on value mask, masked = pcv.threshold.custom_range( rgb_img=masked, lower_thresh=[0, green_lower_tresh, 0], upper_thresh=[255, green_higher_tresh, 255], channel='RGB') masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') #filtered on green mask, masked = pcv.threshold.custom_range( rgb_img=masked, lower_thresh=[red_lower_tresh, 0, 0], upper_thresh=[red_higher_thresh, 255, 255], channel='RGB') masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') #filtered on red mask_old, masked_old = pcv.threshold.custom_range( rgb_img=masked, lower_thresh=[0, 0, blue_lower_tresh], upper_thresh=[255, 255, blue_higher_tresh], channel='RGB') masked = pcv.apply_mask(rgb_img=masked_old, mask=mask_old, mask_color='white') #filtered on blue #b = pcv.rgb2gray_lab(rgb_img = masked, channel = 'b') # Converting toe CIElab blue_yellow image #b_thresh =pcv.threshold.binary(gray_img = b, threshold=lab_blue_lower_tresh, max_value = lab_blue_higher_tresh) ###_____________________________________ Now to identify objects masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a') masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b') # Threshold the green-magenta and blue images maskeda_thresh = pcv.threshold.binary( gray_img=masked_a, threshold=125, # original 115 max_value=255, object_type='dark') maskeda_thresh1 = pcv.threshold.binary( gray_img=masked_a, threshold=140, # original 135 max_value=255, object_type='light') maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light') ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1) # Fill small objects # Inputs: # bin_img - Binary image data # size - Minimum object area size in pixels (must be an integer), and smaller objects will be filled ab = pcv.median_blur(gray_img=ab, ksize=3) ab_fill = pcv.fill(bin_img=ab, size=1000) #print("filled") # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white') # ID the objects id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill) # Let's just take the largest roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=0, y=0, h=960, w=1280) # Currently hardcoded # Decide which objects to keep # Inputs: # img = img to display kept objects # roi_contour = contour of roi, output from any ROI function # roi_hierarchy = contour of roi, output from any ROI function # object_contour = contours of objects, output from pcv.find_objects function # obj_hierarchy = hierarchy of objects, output from pcv.find_objects function # roi_type = 'partial' (default, for partially inside), 'cutto', or # 'largest' (keep only largest contour) with HiddenPrints(): roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects # Inputs: # img - RGB or grayscale image data for plotting # contours - Contour list # hierarchy - Contour hierarchy array obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) #print("final plant") new_im = Image.fromarray(masked2) new_im.save("output//" + args.filename + "last_masked.png") ##################_________________ Analysis outfile = args.outdir + "/" + filename # Here come all the analyse functions. # pcv.acute_vertex(img, obj, 30, 15, 100) color_img = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type=None) #new_im = Image.fromarray(color_img) #new_im.save(args.filename + "color_img.png") # Find shape properties, output shape image (optional) # Inputs: # img - RGB or grayscale image data # obj- Single or grouped contour object # mask - Binary image mask to use as mask for moments analysis shape_img = pcv.analyze_object(img=img, obj=obj, mask=mask) new_im = Image.fromarray(shape_img) new_im.save("output//" + args.filename + "shape_img.png") # Shape properties relative to user boundary line (optional) # Inputs: # img - RGB or grayscale image data # obj - Single or grouped contour object # mask - Binary mask of selected contours # line_position - Position of boundary line (a value of 0 would draw a line # through the bottom of the image) boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680) new_im = Image.fromarray(boundary_img1) new_im.save("output//" + args.filename + "boundary_img.png") # Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional) # Inputs: # rgb_img - RGB image data # mask - Binary mask of selected contours # hist_plot_type - None (default), 'all', 'rgb', 'lab', or 'hsv' # This is the data to be printed to the SVG histogram file color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') #new_im = Image.fromarray(color_histogram) #new_im.save(args.filename + "color_histogram_img.png") # Pseudocolor the grayscale image # Inputs: # gray_img - Grayscale image data # obj - Single or grouped contour object (optional), if provided the pseudocolored image gets # cropped down to the region of interest. # mask - Binary mask (optional) # background - Background color/type. Options are "image" (gray_img, default), "white", or "black". A mask # must be supplied. # cmap - Colormap # min_value - Minimum value for range of interest # max_value - Maximum value for range of interest # dpi - Dots per inch for image if printed out (optional, if dpi=None then the default is set to 100 dpi). # axes - If False then the title, x-axis, and y-axis won't be displayed (default axes=True). # colorbar - If False then the colorbar won't be displayed (default colorbar=True) pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=kept_mask, cmap='jet') #new_im = Image.fromarray(pseudocolored_img) #new_im.save(args.filename + "pseudocolored.png") # Write shape and color data to results file pcv.print_results(filename=args.result)
def main(): # Get options args = options() pcv.params.debug = args.debug # set debug mode pcv.params.debug_outdir = args.outdir # set output directory # Read image img, path, filename = pcv.readimage(filename=args.image) # Convert RGB to HSV and extract the saturation channel s = pcv.rgb2gray_hsv(rgb_img=img, channel='s') # Threshold the saturation image s_thresh = pcv.threshold.binary(gray_img=s, threshold=85, max_value=255, object_type='light') # Median Blur s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5) s_cnt = pcv.median_blur(gray_img=s_thresh, ksize=5) # Convert RGB to LAB and extract the Blue channel b = pcv.rgb2gray_lab(gray_img=img, channel='b') # Threshold the blue image b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light') b_cnt = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light') # Fill small objects # b_fill = pcv.fill(b_thresh, 10) # Join the thresholded saturation and blue-yellow images bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_cnt) # Apply Mask (for VIS images, mask_color=white) masked = pcv.apply_mask(rgb_img=img, mask=bs, mask_color='white') # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a') masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b') # Threshold the green-magenta and blue images maskeda_thresh = pcv.threshold.binary(gray_img=masked_a, threshold=115, max_value=255, object_type='dark') maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a, threshold=135, max_value=255, object_type='light') maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light') # Join the thresholded saturation and blue-yellow images (OR) ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1) # Fill small objects ab_fill = pcv.fill(bin_img=ab, size=200) # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white') # Identify objects id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill) # Define ROI roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=100, y=100, h=200, w=200) # Decide which objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) ############### Analysis ################ outfile = False if args.writeimg == True: outfile = args.outdir + "/" + filename # Find shape properties, output shape image (optional) shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask) # Shape properties relative to user boundary line (optional) boundary_img1 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=1680) # Determine color properties: Histograms, Color Slices, output color analyzed histogram (optional) color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') # Pseudocolor the grayscale image pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=kept_mask, cmap='jet') # Write shape and color data to results file pcv.print_results(filename=args.result)
def main_side(): # Setting "args" # Get options pcv.params.debug = args.debug #set debug mode pcv.params.debug_outdir = args.outdir #set output directory # Read image (readimage mode defaults to native but if image is RGBA then specify mode='rgb') # Inputs: # filename - Image file to be read in # mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv' filename = args.image img = cv2.imread(args.image, flags=0) #img = pcv.invert(img) path, img_name = os.path.split(args.image) img_bkgrd = cv2.imread("background.png", flags=0) #print(img) #print(img_bkgrd) bkg_sub_img = pcv.image_subtract(img_bkgrd, img) bkg_sub_thres_img, masked_img = pcv.threshold.custom_range( rgb_img=bkg_sub_img, lower_thresh=[50], upper_thresh=[255], channel='gray') # Laplace filtering (identify edges based on 2nd derivative) # Inputs: # gray_img - Grayscale image data # ksize - Aperture size used to calculate the second derivative filter, # specifies the size of the kernel (must be an odd integer) # scale - Scaling factor applied (multiplied) to computed Laplacian values # (scale = 1 is unscaled) lp_img = pcv.laplace_filter(gray_img=img, ksize=1, scale=1) # Plot histogram of grayscale values pcv.visualize.histogram(gray_img=lp_img) # Lapacian image sharpening, this step will enhance the darkness of the edges detected lp_shrp_img = pcv.image_subtract(gray_img1=img, gray_img2=lp_img) # Plot histogram of grayscale values, this helps to determine thresholding value pcv.visualize.histogram(gray_img=lp_shrp_img) # Sobel filtering # 1st derivative sobel filtering along horizontal axis, kernel = 1) # Inputs: # gray_img - Grayscale image data # dx - Derivative of x to analyze # dy - Derivative of y to analyze # ksize - Aperture size used to calculate 2nd derivative, specifies the size of the kernel and must be an odd integer # NOTE: Aperture size must be greater than the largest derivative (ksize > dx & ksize > dy) sbx_img = pcv.sobel_filter(gray_img=img, dx=1, dy=0, ksize=1) # 1st derivative sobel filtering along vertical axis, kernel = 1) sby_img = pcv.sobel_filter(gray_img=img, dx=0, dy=1, ksize=1) # Combine the effects of both x and y filters through matrix addition # This will capture edges identified within each plane and emphasize edges found in both images # Inputs: # gray_img1 - Grayscale image data to be added to gray_img2 # gray_img2 - Grayscale image data to be added to gray_img1 sb_img = pcv.image_add(gray_img1=sbx_img, gray_img2=sby_img) # Use a lowpass (blurring) filter to smooth sobel image # Inputs: # gray_img - Grayscale image data # ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input, # (n, m) box if tuple input mblur_img = pcv.median_blur(gray_img=sb_img, ksize=1) # Inputs: # gray_img - Grayscale image data mblur_invert_img = pcv.invert(gray_img=mblur_img) # combine the smoothed sobel image with the laplacian sharpened image # combines the best features of both methods as described in "Digital Image Processing" by Gonzalez and Woods pg. 169 edge_shrp_img = pcv.image_add(gray_img1=mblur_invert_img, gray_img2=lp_shrp_img) # Perform thresholding to generate a binary image tr_es_img = pcv.threshold.binary(gray_img=edge_shrp_img, threshold=145, max_value=255, object_type='dark') # Do erosion with a 3x3 kernel (ksize=3) # Inputs: # gray_img - Grayscale (usually binary) image data # ksize - The size used to build a ksize x ksize # matrix using np.ones. Must be greater than 1 to have an effect # i - An integer for the number of iterations e1_img = pcv.erode(gray_img=tr_es_img, ksize=3, i=1) # Bring the two object identification approaches together. # Using a logical OR combine object identified by background subtraction and the object identified by derivative filter. # Inputs: # bin_img1 - Binary image data to be compared in bin_img2 # bin_img2 - Binary image data to be compared in bin_img1 comb_img = pcv.logical_or(bin_img1=e1_img, bin_img2=bkg_sub_thres_img) # Get masked image, Essentially identify pixels corresponding to plant and keep those. # Inputs: # rgb_img - RGB image data # mask - Binary mask image data # mask_color - 'black' or 'white' masked_erd = pcv.apply_mask(rgb_img=img, mask=comb_img, mask_color='black') # Need to remove the edges of the image, we did that by generating a set of rectangles to mask the edges # img is (1280 X 960) # mask for the bottom of the image # Inputs: # img - RGB or grayscale image data # p1 - Point at the top left corner of the rectangle (tuple) # p2 - Point at the bottom right corner of the rectangle (tuple) # color 'black' (default), 'gray', or 'white' # masked1, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img=img, p1=(500, 875), p2=(720, 960)) # mask the edges masked2, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img=img, p1=(1, 1), p2=(1279, 959)) bx12_img = pcv.logical_or(bin_img1=box1_img, bin_img2=box2_img) inv_bx1234_img = bx12_img # we dont invert inv_bx1234_img = bx12_img #inv_bx1234_img = pcv.invert(gray_img=bx12_img) edge_masked_img = pcv.apply_mask(rgb_img=masked_erd, mask=inv_bx1234_img, mask_color='black') #print("here we create a mask") mask, masked = pcv.threshold.custom_range(rgb_img=edge_masked_img, lower_thresh=[25], upper_thresh=[175], channel='gray') masked = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') #print("end") # Identify objects # Inputs: # img - RGB or grayscale image data for plotting # mask - Binary mask used for detecting contours id_objects, obj_hierarchy = pcv.find_objects(img=edge_masked_img, mask=mask) # Define ROI # Inputs: # img - RGB or grayscale image to plot the ROI on # x - The x-coordinate of the upper left corner of the rectangle # y - The y-coordinate of the upper left corner of the rectangle # h - The height of the rectangle # w - The width of the rectangle roi1, roi_hierarchy = pcv.roi.rectangle(img=edge_masked_img, x=100, y=100, h=800, w=1000) # Decide which objects to keep # Inputs: # img = img to display kept objects # roi_contour = contour of roi, output from any ROI function # roi_hierarchy = contour of roi, output from any ROI function # object_contour = contours of objects, output from pcv.find_objects function # obj_hierarchy = hierarchy of objects, output from pcv.find_objects function # roi_type = 'partial' (default, for partially inside), 'cutto', or # 'largest' (keep only largest contour) with HiddenPrints(): roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects( img=edge_masked_img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='largest') rgb_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # Inputs: # img - RGB or grayscale image data for plotting # contours - Contour list # hierarchy - Contour hierarchy array o, m = pcv.object_composition(img=rgb_img, contours=roi_objects, hierarchy=hierarchy5) ### Analysis ### outfile = False if args.writeimg == True: outfile = args.outdir + "/" + filename # Perform signal analysis # Inputs: # img - RGB or grayscale image data # obj- Single or grouped contour object # mask - Binary image mask to use as mask for moments analysis shape_img = pcv.analyze_object(img=img, obj=o, mask=m) new_im = Image.fromarray(shape_img) new_im.save("output//" + args.filename + "shape_img_side.png") # Inputs: # gray_img - 8 or 16-bit grayscale image data # mask - Binary mask made from selected contours # bins - Number of classes to divide the spectrum into # histplot - If True, plots the histogram of intensity values nir_hist = pcv.analyze_nir_intensity(gray_img=img, mask=kept_mask, bins=256, histplot=True) # Pseudocolor the grayscale image to a colormap # Inputs: # gray_img - Grayscale image data # obj - Single or grouped contour object (optional), if provided the pseudocolored image gets cropped down to the region of interest. # mask - Binary mask (optional) # background - Background color/type. Options are "image" (gray_img), "white", or "black". A mask must be supplied. # cmap - Colormap # min_value - Minimum value for range of interest # max_value - Maximum value for range of interest # dpi - Dots per inch for image if printed out (optional, if dpi=None then the default is set to 100 dpi). # axes - If False then the title, x-axis, and y-axis won't be displayed (default axes=True). # colorbar - If False then the colorbar won't be displayed (default colorbar=True) pseudocolored_img = pcv.visualize.pseudocolor(gray_img=img, mask=kept_mask, cmap='viridis') # Perform shape analysis # Inputs: # img - RGB or grayscale image data # obj- Single or grouped contour object # mask - Binary image mask to use as mask for moments analysis shape_imgs = pcv.analyze_object(img=rgb_img, obj=o, mask=m) # Write shape and nir data to results file pcv.print_results(filename=args.result)
def root(): uploaded_file = st.file_uploader("Choose an image...", type="jpg") if uploaded_file is not None: inp = Image.open(uploaded_file) inp.save('input.jpg') img, path, filename = pcv.readimage(filename='input.jpg') image = Image.open('input.jpg') st.image(image, caption='Original Image',use_column_width=True) # Convert RGB to HSV and extract the saturation channel # Inputs: # rgb_image - RGB image data # channel - Split by 'h' (hue), 's' (saturation), or 'v' (value) channel s = pcv.rgb2gray_hsv(rgb_img=img, channel='s') pcv.print_image(s, "plant/rgbtohsv.png") image = Image.open('plant/rgbtohsv.png') st.image(image, caption='RGB to HSV', use_column_width=True) s_thresh = pcv.threshold.binary(gray_img=s, threshold=85, max_value=255, object_type='light') pcv.print_image(s_thresh, "plant/binary_threshold.png") image = Image.open('plant/binary_threshold.png') st.image(image, caption='Binary Threshold',use_column_width=True) # Median Blur to clean noise # Inputs: # gray_img - Grayscale image data # ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input, # (n, m) box if tuple input s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5) pcv.print_image(s_mblur, "plant/Median_blur.png") image = Image.open('plant/Median_blur.png') st.image(image, caption='Median Blur',use_column_width=True) # An alternative to using median_blur is gaussian_blur, which applies # a gaussian blur filter to the image. Depending on the image, one # technique may be more effective than others. # Inputs: # img - RGB or grayscale image data # ksize - Tuple of kernel size # sigma_x - Standard deviation in X direction; if 0 (default), # calculated from kernel size # sigma_y - Standard deviation in Y direction; if sigmaY is # None (default), sigmaY is taken to equal sigmaX gaussian_img = pcv.gaussian_blur(img=s_thresh, ksize=(5, 5), sigma_x=0, sigma_y=None) # Convert RGB to LAB and extract the blue channel ('b') # Input: # rgb_img - RGB image data # channel- Split by 'l' (lightness), 'a' (green-magenta), or 'b' (blue-yellow) channel b = pcv.rgb2gray_lab(rgb_img=img, channel='b') b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light') # Join the threshold saturation and blue-yellow images with a logical or operation # Inputs: # bin_img1 - Binary image data to be compared to bin_img2 # bin_img2 - Binary image data to be compared to bin_img1 bs = pcv.logical_or(bin_img1=s_mblur, bin_img2=b_thresh) pcv.print_image(bs, "plant/threshold comparison.png") image = Image.open('plant/threshold comparison.png') st.image(image, caption='Threshold Comparision',use_column_width=True) # Appy Mask (for VIS images, mask_color='white') # Inputs: # img - RGB or grayscale image data # mask - Binary mask image data # mask_color - 'white' or 'black' masked = pcv.apply_mask(img=img, mask=bs, mask_color='white') pcv.print_image(masked, "plant/Apply_mask.png") image = Image.open('plant/Apply_mask.png') st.image(image, caption='Applied Mask',use_column_width=True) # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels masked_a = pcv.rgb2gray_lab(rgb_img=masked, channel='a') masked_b = pcv.rgb2gray_lab(rgb_img=masked, channel='b') # Threshold the green-magenta and blue images maskeda_thresh = pcv.threshold.binary(gray_img=masked_a, threshold=115, max_value=255, object_type='dark') maskeda_thresh1 = pcv.threshold.binary(gray_img=masked_a, threshold=135,max_value=255, object_type='light') maskedb_thresh = pcv.threshold.binary(gray_img=masked_b, threshold=128, max_value=255, object_type='light') pcv.print_image( maskeda_thresh, "plant/maskeda_thresh.png") pcv.print_image(maskeda_thresh1, "plant/maskeda_thresh1.png") pcv.print_image(maskedb_thresh, "plant/maskedb_thresh1.png") image = Image.open('plant/maskeda_thresh.png') st.image(image, caption='Threshold green-magneta and blue image',use_column_width=True) # Join the thresholded saturation and blue-yellow images (OR) ab1 = pcv.logical_or(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) ab = pcv.logical_or(bin_img1=maskeda_thresh1, bin_img2=ab1) # Opening filters out bright noise from an image. # Inputs: # gray_img - Grayscale or binary image data # kernel - Optional neighborhood, expressed as an array of 1's and 0's. If None (default), # uses cross-shaped structuring element. opened_ab = pcv.opening(gray_img=ab) # Depending on the situation it might be useful to use the # exclusive or (pcv.logical_xor) function. # Inputs: # bin_img1 - Binary image data to be compared to bin_img2 # bin_img2 - Binary image data to be compared to bin_img1 xor_img = pcv.logical_xor(bin_img1=maskeda_thresh, bin_img2=maskedb_thresh) # Fill small objects (reduce image noise) # Inputs: # bin_img - Binary image data # size - Minimum object area size in pixels (must be an integer), and smaller objects will be filled ab_fill = pcv.fill(bin_img=ab, size=200) # Closing filters out dark noise from an image. # Inputs: # gray_img - Grayscale or binary image data # kernel - Optional neighborhood, expressed as an array of 1's and 0's. If None (default), # uses cross-shaped structuring element. closed_ab = pcv.closing(gray_img=ab_fill) # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(img=masked, mask=ab_fill, mask_color='white') # Identify objects # Inputs: # img - RGB or grayscale image data for plotting # mask - Binary mask used for detecting contours id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill) # Define the region of interest (ROI) # Inputs: # img - RGB or grayscale image to plot the ROI on # x - The x-coordinate of the upper left corner of the rectangle # y - The y-coordinate of the upper left corner of the rectangle # h - The height of the rectangle # w - The width of the rectangle roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, x=50, y=50, h=100, w=100) # Decide which objects to keep # Inputs: # img = img to display kept objects # roi_contour = contour of roi, output from any ROI function # roi_hierarchy = contour of roi, output from any ROI function # object_contour = contours of objects, output from pcv.find_objects function # obj_hierarchy = hierarchy of objects, output from pcv.find_objects function # roi_type = 'partial' (default, for partially inside the ROI), 'cutto', or # 'largest' (keep only largest contour) roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img=img, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects # Inputs: # img - RGB or grayscale image data for plotting # contours - Contour list # hierarchy - Contour hierarchy array obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) ############### Analysis ################ # Find shape properties, data gets stored to an Outputs class automatically # Inputs: # img - RGB or grayscale image data # obj- Single or grouped contour object # mask - Binary image mask to use as mask for moments analysis analysis_image = pcv.analyze_object(img=img, obj=obj, mask=mask) pcv.print_image(analysis_image, "plant/analysis_image.png") image = Image.open('plant/analysis_image.png') st.image(image, caption='Analysis_image',use_column_width=True) # Shape properties relative to user boundary line (optional) # Inputs: # img - RGB or grayscale image data # obj - Single or grouped contour object # mask - Binary mask of selected contours # line_position - Position of boundary line (a value of 0 would draw a line # through the bottom of the image) boundary_image2 = pcv.analyze_bound_horizontal(img=img, obj=obj, mask=mask, line_position=370) pcv.print_image(boundary_image2, "plant/boundary_image2.png") image = Image.open('plant/boundary_image2.png') st.image(image, caption='Boundary Image',use_column_width=True) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) # Inputs: # rgb_img - RGB image data # mask - Binary mask of selected contours # hist_plot_type - None (default), 'all', 'rgb', 'lab', or 'hsv' # This is the data to be printed to the SVG histogram file color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask, hist_plot_type='all') # Print the histogram out to save it pcv.print_image(img=color_histogram, filename="plant/vis_tutorial_color_hist.jpg") image = Image.open('plant/vis_tutorial_color_hist.jpg') st.image(image, caption='Color Histogram',use_column_width=True) # Divide plant object into twenty equidistant bins and assign pseudolandmark points based upon their # actual (not scaled) position. Once this data is scaled this approach may provide some information # regarding shape independent of size. # Inputs: # img - RGB or grayscale image data # obj - Single or grouped contour object # mask - Binary mask of selected contours top_x, bottom_x, center_v_x = pcv.x_axis_pseudolandmarks(img=img, obj=obj, mask=mask) top_y, bottom_y, center_v_y = pcv.y_axis_pseudolandmarks(img=img, obj=obj, mask=mask) # The print_results function will take the measurements stored when running any (or all) of these functions, format, # and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions # are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object, # fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file pcv.print_results(filename='vis_tutorial_results.txt')
def main(): # Get options args = options() pcv.params.debug = args.debug #set debug mode pcv.params.debug_outdir = args.outdir #set output directory # Read image (converting fmax and track to 8 bit just to create a mask, use 16-bit for all the math) fmax, path, filename = pcv.readimage(args.fmax) # Threshold the image mask = pcv.threshold.binary(gray_img=fmax, threshold=20, max_value=255, object_type='light') mask = pcv.fill(mask, 100) # Identify objects id_objects, obj_hierarchy = pcv.find_objects(img=fmax, mask=mask) # Define ROI roi1, roi_hierarchy = pcv.roi.rectangle(img=mask, x=180, y=90, h=200, w=200) # Decide which objects to keep roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects( img=mask, roi_contour=roi1, roi_hierarchy=roi_hierarchy, object_contour=id_objects, obj_hierarchy=obj_hierarchy, roi_type='partial') # Object combine kept objects obj, masked = pcv.object_composition(img=mask, contours=roi_objects, hierarchy=hierarchy3) ################ Analysis ################ outfile = False if args.writeimg == True: outfile = args.outdir + "/" + filename # Find shape properties, output shape image (optional) shape_img = pcv.analyze_object(img=mask, obj=obj, mask=masked) # Fluorescence Measurement (read in 16-bit images) fmin = cv2.imread(args.fmin, -1) fmax = cv2.imread(args.fmax, -1) fvfm_images = pcv.fluor_fvfm(fdark=np.zeros_like(fmax), fmin=fmin, fmax=fmax, mask=kept_mask, bins=256) # Store the two images fv_img = fvfm_images[0] fvfm_hist = fvfm_images[1] fvfm = np.divide(fv_img, fmax, out=np.zeros_like(fmax, dtype='float'), where=np.logical_and(mask > 1, fmax > 0)) # Pseudocolor the Fv/Fm grayscale image that is calculated inside the fluor_fvfm function pseudocolored_img = pcv.visualize.pseudocolor(gray_img=fvfm, mask=kept_mask, cmap='viridis', min_value=0, max_value=1) # Write shape and nir data to results file pcv.print_results(filename=args.result)
def main(): # Create input arguments object args = options() # Set debug mode pcv.params.debug = args.debug # Open a single image img, imgpath, imgname = pcv.readimage(filename=args.image) # Visualize colorspaces all_cs = pcv.visualize.colorspaces(rgb_img=img) # Extract the Blue-Yellow ("b") channel from the LAB colorspace gray_img = pcv.rgb2gray_lab(rgb_img=img, channel="b") # Plot a histogram of pixel values for the Blue-Yellow ("b") channel. hist_plot = pcv.visualize.histogram(gray_img=gray_img) # Apply a binary threshold to the Blue-Yellow ("b") grayscale image. thresh_img = pcv.threshold.binary(gray_img=gray_img, threshold=140, max_value=255, object_type="light") # Apply a dilation with a 5x5 kernel and 3 iterations dil_img = pcv.dilate(gray_img=thresh_img, ksize=5, i=3) # Fill in small holes in the leaves closed_img = pcv.fill_holes(bin_img=dil_img) # Erode the plant pixels using a 5x5 kernel and 3 iterations er_img = pcv.erode(gray_img=closed_img, ksize=5, i=3) # Apply a Gaussian blur with a 5 x 5 kernel. blur_img = pcv.gaussian_blur(img=er_img, ksize=(5, 5)) # Set pixel values less than 255 to 0 blur_img[np.where(blur_img < 255)] = 0 # Fill/remove objects less than 300 pixels in area cleaned = pcv.fill(bin_img=blur_img, size=300) # Create a circular ROI roi, roi_str = pcv.roi.circle(img=img, x=1725, y=1155, r=400) # Identify objects in the binary image cnts, cnts_str = pcv.find_objects(img=img, mask=cleaned) # Filter objects by region of interest plant_cnt, plant_str, plant_mask, plant_area = pcv.roi_objects( img=img, roi_contour=roi, roi_hierarchy=roi_str, object_contour=cnts, obj_hierarchy=cnts_str) # Combine objects into one plant, mask = pcv.object_composition(img=img, contours=plant_cnt, hierarchy=plant_str) # Measure size and shape properties shape_img = pcv.analyze_object(img=img, obj=plant, mask=mask) if args.writeimg: pcv.print_image(img=shape_img, filename=os.path.join(args.outdir, "shapes_" + imgname)) # Analyze color properties color_img = pcv.analyze_color(rgb_img=img, mask=mask, hist_plot_type="hsv") if args.writeimg: pcv.print_image(img=color_img, filename=os.path.join(args.outdir, "histogram_" + imgname)) # Save the measurements to a file pcv.print_results(filename=args.result)
obj_hierarchy=roi_obj_hierarchy) # Combine objects together in each plant plant_contour, plant_mask = pcv.object_composition( img=img_copy, contours=filtered_contours, hierarchy=filtered_hierarchy) # Analyze the shape of each plant analysis_images = pcv.analyze_object(img=img_copy, obj=plant_contour, mask=plant_mask) # Save the image with shape characteristics img_copy = analysis_images # Print out a text file with shape data for each plant in the image pcv.print_results(filename='prefix_' + str(i) + '.txt') # Clear the measurements stored globally into the Ouptuts class pcv.outputs.clear() # Plot out the image with shape analysis on each plant in the image pcv.plot_image(img_copy) # To view and/or download the text file output (saved in JSON format)... # 1) To see the text file with data that got saved out, click “File” tab in top left corner. # 2) Click “Open…” # 3) Open the multi-plant text files # # Check out documentation on how to [convert JSON](https://plantcv.readthedocs.io/en/latest/tools/#convert-output-json-data-files-to-csv-tables) format output into table formatted output. Depending on the analysis steps a PlantCV user may have two CSV files (single value traits and multivalue traits). # # #
filled_img = fill_segments(mask=cropped_mask, objects=roi_objects) # Access data stored out from fill_segments segments_area = pcv.outputs.observations['segment_area']['value'] # In[116]: #Length # Measure path lengths of segments # Inputs: # segmented_img = Segmented image to plot lengths on # objects = List of contours labeled_img = pcv.morphology.segment_path_length(segmented_img=filled_img, objects=roi_objects) # In[117]: # Write morphological data to results file # The print_results function will take the measurements stored when running any (or all) of these functions, format, # and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions # are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object, # fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file pcv.print_results(filename=args.result) # In[100]: #CLEARS OUTPUTS #pcv.outputs.clear()
object, mask = pcv.object_composition(img, roi_objects, roi_obj_hierarchy) shape_img = pcv.analyze_object(img, object, mask) pcv.outputs.add_observation(variable='roi', trait='roi', method='roi', scale='int', datatype=int, value=i, label='#') filename = str(i) + "_" + args.result with open(filename, "w") as r: r.write(metadata) pcv.print_results(filename=filename) pcv.outputs.clear() #Print out a text file with shape data for each plant in the image <-- removed code #Old code would create a separate txt file for each plant in the picture #i.e. if you had 18 recognized plants you would get 18 text files #new code takes opens those 18 text files one by one and re-writes them into a "final file" #all the data from each text file gets put into one file and the 18 text files get removed except #the last one # pcv.print_results(filename = str(i)+ args.result) # with open(args.result, "a") as finalfile: # with open('prefix_' +str(i)+ '.txt') as tempfile: # for x in tempfile.readlines(): # finalfile.write(x) # finalfile.write("\n") # tempfile.close()