def plant_cv(img): counter = 0 debug = None counter, s = pcv.rgb2gray_hsv(img, 's', counter, debug) counter, s_thresh = pcv.binary_threshold(s, 145, 255, 'light', counter, debug) counter, s_mblur = pcv.median_blur(s_thresh, 5, counter, debug) # Convert RGB to LAB and extract the Blue channel counter, b = pcv.rgb2gray_lab(img, 'b', counter, debug) # Threshold the blue image counter, b_thresh = pcv.binary_threshold(b, 145, 255, 'light', counter, debug) counter, b_cnt = pcv.binary_threshold(b, 145, 255, 'light', counter, debug) # Join the thresholded saturation and blue-yellow images counter, bs = pcv.logical_or(s_mblur, b_cnt, counter, debug) counter, masked = pcv.apply_mask(img, bs, 'white', counter, debug) #---------------------------------------- # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels counter, masked_a = pcv.rgb2gray_lab(masked, 'a', counter, debug) counter, masked_b = pcv.rgb2gray_lab(masked, 'b', counter, debug) # Threshold the green-magenta and blue images counter, maskeda_thresh = pcv.binary_threshold(masked_a, 115, 255, 'dark', counter, debug) counter, maskeda_thresh1 = pcv.binary_threshold(masked_a, 135, 255, 'light', counter, debug) counter, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', counter, debug) # Join the thresholded saturation and blue-yellow images (OR) counter, ab1 = pcv.logical_or(maskeda_thresh, maskedb_thresh, counter, debug) counter, ab = pcv.logical_or(maskeda_thresh1, ab1, counter, debug) counter, ab_cnt = pcv.logical_or(maskeda_thresh1, ab1, counter, debug) # Fill small objects counter, ab_fill = pcv.fill(ab, ab_cnt, 200, counter, debug) # Apply mask (for vis images, mask_color=white) counter, masked2 = pcv.apply_mask(masked, ab_fill, 'white', counter, debug) zeros = np.zeros(masked2.shape[:2], dtype="uint8") merged = cv2.merge([zeros, ab_fill, zeros]) return merged, masked2
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 generateMask(input, output, maskType=MASK_TYPES['BW']): pcv.params.debug = True #set debug mode # pcv.params.debug_outdir="./output.txt" #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', 'envi', or 'csv' img, path, filename = pcv.readimage(filename=input, mode='rgb') 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(rgb_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(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) if maskType == MASK_TYPES['BW']: pcv.print_image(ab, filename=output) return (True, None) # Fill small objects ab_fill = pcv.fill(bin_img=ab, size=200) # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(img=masked, mask=ab_fill, mask_color='black') if maskType == MASK_TYPES['COLORED']: pcv.print_image(masked2, filename=output) return (True, None) return (False, 'Unknown mask type.') """
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 analyze(image_name): # Read image img, path, filename = pcv.readimage(filename=image_name) # 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(rgb_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=0, y=500, h=2000, w=2000) # 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 ################ # Find shape properties shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask) # Color color_histogram = pcv.analyze_color(rgb_img=img, mask=kept_mask) # Access data stored out from analyze_object plant_observations = pcv.outputs.observations return plant_observations
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)
# Threshold the blue channel image b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light') pcv.print_image(img=b_thresh, filename="upload/output_imgs/Extracted_img.jpg") pcv.print_image(img=b, filename="upload/output_imgs/Gray_img.jpg") # In[9]: # 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(img=bs, filename="upload/output_imgs/BSature_img.jpg") # In[10]: # Appy Mask (for VIS images, mask_color='white') # Inputs: # rgb_img - RGB image data # mask - Binary mask image data # mask_color - 'white' or 'black' rgb_img = img masked = pcv.apply_mask(rgb_img, mask=bs, mask_color='white') pcv.print_image(img=masked, filename="upload/output_imgs/VIS_img.jpg") # In[11]:
def main(): # Get options args = options() # Set variables pcv.params.debug = args.debug # Replace the hard-coded debug with the debug flag img_file = args.image # Replace the hard-coded input image with image flag ############### Image read-in ################ # Read target image img, path, filename = pcv.readimage(filename = img_file, mode = "rgb") ############### Find scale and crop ################ # find colour card in the image to be analysed df, start, space = pcv.transform.find_color_card(rgb_img = img) if int(start[0]) < 2000: img = imutils.rotate_bound(img, -90) rotated = 1 df, start, space = pcv.transform.find_color_card(rgb_img = img) else: rotated = 0 #if img.shape[0] > 6000: # rotated = 1 #else: rotated = 0 img_mask = pcv.transform.create_color_card_mask(rgb_img = img, radius = 10, start_coord = start, spacing = space, ncols = 4, nrows = 6) # write the spacing of the colour card to file as size marker with open(r'size_marker.csv', 'a') as f: writer = csv.writer(f) writer.writerow([filename, space[0]]) # define a bounding rectangle around the colour card x_cc,y_cc,w_cc,h_cc = cv2.boundingRect(img_mask) x_cc = int(round(x_cc - 0.3 * w_cc)) y_cc = int(round(y_cc - 0.3 * h_cc)) h_cc = int(round(h_cc * 1.6)) w_cc = int(round(w_cc * 1.6)) # crop out colour card start_point = (x_cc, y_cc) end_point = (x_cc+w_cc, y_cc+h_cc) colour = (0, 0, 0) thickness = -1 crop_img = cv2.rectangle(img, start_point, end_point, colour, thickness) ############### Fine segmentation ################ # Threshold A and B channels of the LAB colourspace and the Hue channel of the HSV colourspace l_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[70,0,0], upper_thresh=[255,255,255], channel='LAB') a_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,0], upper_thresh=[255,145,255], channel='LAB') b_thresh, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,123], upper_thresh=[255,255,255], channel='LAB') h_thresh_low, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[0,0,0], upper_thresh=[130,255,255], channel='HSV') h_thresh_high, _ = pcv.threshold.custom_range(img=crop_img, lower_thresh=[150,0,0], upper_thresh=[255,255,255], channel='HSV') h_thresh = pcv.logical_or(h_thresh_low, h_thresh_high) # Join the thresholded images to keep only consensus pixels ab = pcv.logical_and(b_thresh, a_thresh) lab = pcv.logical_and(l_thresh, ab) labh = pcv.logical_and(lab, h_thresh) # Fill small objects labh_clean = pcv.fill(labh, 200) # Dilate to close broken borders #labh_dilated = pcv.dilate(labh_clean, 4, 1) labh_dilated = labh_clean # Apply mask (for VIS images, mask_color=white) masked = pcv.apply_mask(crop_img, labh_dilated, "white") # Identify objects contours, hierarchy = pcv.find_objects(crop_img, labh_dilated) # Define ROI if rotated == 1: roi_height = 3000 roi_lwr_bound = y_cc + (h_cc * 0.5) - roi_height roi_contour, roi_hierarchy= pcv.roi.rectangle(x=1000, y=roi_lwr_bound, h=roi_height, w=2000, img=crop_img) else: roi_height = 1500 roi_lwr_bound = y_cc + (h_cc * 0.5) - roi_height roi_contour, roi_hierarchy= pcv.roi.rectangle(x=2000, y=roi_lwr_bound, h=roi_height, w=2000, img=crop_img) # Decide which objects to keep filtered_contours, filtered_hierarchy, mask, area = pcv.roi_objects(img = crop_img, roi_type = 'partial', roi_contour = roi_contour, roi_hierarchy = roi_hierarchy, object_contour = contours, obj_hierarchy = hierarchy) # Combine kept objects obj, mask = pcv.object_composition(crop_img, filtered_contours, filtered_hierarchy) ############### Analysis ################ outfile=False if args.writeimg==True: outfile_black=args.outdir+"/"+filename+"_black" outfile_white=args.outdir+"/"+filename+"_white" outfile_analysed=args.outdir+"/"+filename+"_analysed" # analyse shape shape_img = pcv.analyze_object(crop_img, obj, mask) pcv.print_image(shape_img, outfile_analysed) # analyse colour colour_img = pcv.analyze_color(crop_img, mask, 'hsv') # keep the segmented plant for visualisation picture_mask = pcv.apply_mask(crop_img, mask, "black") pcv.print_image(picture_mask, outfile_black) picture_mask = pcv.apply_mask(crop_img, mask, "white") pcv.print_image(picture_mask, outfile_white) # print out results pcv.outputs.save_results(filename=args.result, outformat="json")
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 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 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=127, max_value=255, object_type='dark') 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 = 1600 h = 1200 pot = 230 #340 roi1, roi_hierarchy = pcv.roi.rectangle(img=masked2, x=(2472 - w) / 2, y=(3296 - h - pot), 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) # Find shape properties, output shape image (optional) shape_imgs = pcv.analyze_object(img=img, obj=obj, mask=mask) if args.writeimg == True: pcv.print_image(img=shape_imgs, filename="{}_shape.png".format(args.result[:-5])) pcv.print_image(img=masked2, filename="{}_obj_on_img.png".format(args.result[:-5])) # 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 process_image(image_path, threshold): # Read image img, _, _ = pcv.readimage(filename=image_path) # If image is not in Portrait rotate to ensure center of mass splits the plant correctly if img.shape[0] < img.shape[1]: img = np.array(Image.fromarray(img).transpose(Image.ROTATE_270)) # 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) # 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=160, max_value=255, object_type='light') # 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(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(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=300, y=300, h=img.shape[0] - 600, w=img.shape[1] - 600) # Decide which objects to keep roi_objects, hierarchy3, _, _ = 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 ---------------- # Find shape properties, output shape image (optional) _ = pcv.analyze_object(img=img, obj=obj, mask=mask, label="default") x, y = pcv.outputs.observations['default']['center_of_mass']['value'] width = pcv.outputs.observations['default']['width']['value'] height = pcv.outputs.observations['default']['height']['value'] x = int(x) y = int(y) top = max(int(y - (height / 2)), 0) bottom = min(int(y + (height / 2)), img.shape[0]) left = max(int(x - (width / 2)), 0) right = min(int(x + (width / 2)), img.shape[1]) left_half = mask[top:bottom, left:x] right_half = mask[top:bottom, x:right] left_half_count = cv2.countNonZero(left_half) right_half_count = cv2.countNonZero(right_half) left_percent = (left_half_count / (left_half_count + right_half_count)) right_percent = 1 - left_percent print('Left Percentage: {:.2%} Right Percentage: {:.2%}'.format( left_percent, right_percent)) if abs(left_percent - right_percent) > threshold: print('Rotate plant') else: print('Do NOT rotate plant')
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)
# Threshold the blue channel image b_thresh = pcv.threshold.binary(gray_img=b, threshold=160, max_value=255, object_type='light') pcv.print_image(img=b_thresh, filename="upload/output_imgs2/Extracted_img.jpg") pcv.print_image(img=b, filename="upload/output_imgs2/Gray_img.jpg") # In[39]: # 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(img=bs, filename="upload/output_imgs2/BSature_img.jpg") # In[40]: # Appy Mask (for VIS images, mask_color='white') # Inputs: # rgb_img - RGB image data # mask - Binary mask image data # mask_color - 'white' or 'black' rgb_img = img masked = pcv.apply_mask(rgb_img, mask=bs, mask_color='white') pcv.print_image(img=masked, filename="upload/output_imgs2/VIS_img.jpg") # In[41]:
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 h = pcv.rgb2gray_hsv(rgb_img=img, channel='h') # Threshold the saturation image h_thresh = pcv.threshold.binary(gray_img=h, threshold=85, max_value=255, object_type='dark') # Median Blur h_mblur = pcv.median_blur(gray_img=h_thresh, ksize=20) h_cnt = pcv.median_blur(gray_img=h_thresh, ksize=20) # Convert RGB to LAB and extract the Blue channel b = pcv.rgb2gray_lab(rgb_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=h_mblur, bin_img2=b_cnt) # Apply Mask (for VIS images, mask_color=white) masked = pcv.apply_mask(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(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=400, y=400, 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=600) # 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=h, mask=kept_mask, cmap='jet') # Write shape and color data to results file pcv.print_results(filename=args.result)
def plantCVProcess(img, x, y, w, h): # 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(rgb_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(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(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=x, y=y, 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) ############### Analysis ################ # 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=mask, hist_plot_type='all') # Pseudocolor the grayscale image pseudocolored_img = pcv.visualize.pseudocolor(gray_img=s, mask=mask, cmap='jet') return print_results()
def shoot(): 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)
def test(true_positive_file, test_parameters): hue_lower_tresh = test_parameters[0] hue_higher_tresh = test_parameters[1] saturation_lower_tresh = test_parameters[2] saturation_higher_tresh = test_parameters[3] value_lower_tresh = test_parameters[4] value_higher_tresh = test_parameters[5] green_lower_tresh = test_parameters[6] green_higher_tresh = test_parameters[7] red_lower_tresh = test_parameters[8] red_higher_thresh = test_parameters[9] blue_lower_tresh = test_parameters[10] blue_higher_tresh = test_parameters[11] blur_k = test_parameters[12] fill_k = test_parameters[13] class args: #image = "C:\\Users\\RensD\\OneDrive\\studie\\Master\\The_big_project\\top_perspective\\0214_2018-03-07 08.55 - 26_cam9.png" image = true_positive_file outdir = "C:\\Users\\RensD\\OneDrive\\studie\\Master\\The_big_project\\top_perspective\\output" debug = debug_setting result = "results.txt" # 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') 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') #print("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') #print("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') #print("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') #print("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') #print("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') #print("filtered on blue") ###____________________________________ Blur to minimize try: s_mblur = pcv.median_blur(gray_img = masked_old, ksize = blur_k) s = pcv.rgb2gray_hsv(rgb_img=s_mblur, channel='v') mask, masked_image = pcv.threshold.custom_range(rgb_img=s, lower_thresh=[0], upper_thresh=[254], channel='gray') except: print("failed blur step") try: mask = pcv.fill(mask, fill_k) except: pass masked = pcv.apply_mask(rgb_img=masked, mask = mask, mask_color = 'white') ###_____________________________________ 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=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') 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_fill = pcv.fill(bin_img=ab, size=200) #print("filled") # Apply mask (for VIS images, mask_color=white) masked2 = pcv.apply_mask(rgb_img=masked, mask=ab_fill, mask_color='white') id_objects, obj_hierarchy = pcv.find_objects(masked, ab_fill) # Let's just take the largest roi1, roi_hierarchy= pcv.roi.rectangle(img=masked, x=0, y=0, h=960, w=1280) # Currently hardcoded 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=roi_type) obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) if use_mask == True: return(mask) else: masked2 = pcv.apply_mask(rgb_img=masked, mask=mask, mask_color='white') return(masked2)
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(): # 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)
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(rgb_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(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') cv2.imwrite("masked.jpeg", masked) # 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(img=masked, mask=ab_fill, mask_color='white') cv2.imwrite("masked2.jpeg", masked2)
# Collect the cropped image cropped_img = image[int(roi[1]):int(roi[1] + roi[3]), int(roi[0]):int(roi[0] + roi[2])] # Convert RGB to HSV and extract saturation channel # The HSV value can be changed to be h, s, or v depending on the colour of the flower saturation_img = pcv.rgb2gray_hsv(cropped_img, 'h') # Threshold the saturation img # Depending on the output of the saturation image, the value can be light or dark # Light or dark is what defines what part of the image its to be removed saturation_thresh = pcv.threshold.binary(saturation_img, 85, 255, 'light') # Apply median blur saturation_mblur = pcv.median_blur(saturation_thresh, 5) # Convert RGB to LAB and extract blue channel # Like the HSV function this can be l, a, or b depending on the colour of the flower blue_channel_img = pcv.rgb2gray_lab(cropped_img, 'l') blue_channel_cnt = pcv.threshold.binary(blue_channel_img, 160, 255, 'light') # Join thresholded saturated and blue channel imgs blue_saturated = pcv.logical_or(saturation_mblur, blue_channel_cnt) # Apply black colour mask masked = pcv.apply_mask(cropped_img, blue_saturated, 'black') # Save image cv2.imwrite('tmp/' + image, masked)
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 segmentation(imgW, imgNIR, shape): # VIS example from PlantCV with few modifications # Higher value = more strict selection s_threshold = 165 b_threshold = 200 # Read image img = imread(imgW) #img = cvtColor(img, COLOR_BGR2RGB) imgNIR = imread(imgNIR) #imgNIR = cvtColor(imgNIR, COLOR_BGR2RGB) #img, path, img_filename = pcv.readimage(filename=imgW, mode="native") #imgNIR, pathNIR, imgNIR_filename = pcv.readimage(filename=imgNIR, mode="native") # 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=s_threshold, 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(rgb_img=img, channel='b') # Threshold the blue image ORIGINAL 160 b_thresh = pcv.threshold.binary(gray_img=b, threshold=b_threshold, max_value=255, object_type='light') b_cnt = pcv.threshold.binary(gray_img=b, threshold=b_threshold, max_value=255, object_type='light') # 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(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 # 115 # 135 # 128 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(img=masked, mask=ab_fill, mask_color='white') # Identify objects id_objects, obj_hierarchy = pcv.find_objects(img=masked2, mask=ab_fill) # Define ROI height = shape[0] width = shape[1] roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, 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') # Object combine kept objects obj, mask = pcv.object_composition(img=img, contours=roi_objects, hierarchy=hierarchy3) # Filling holes in the mask, works great for alive plants, not so good for dead plants filled_mask = pcv.fill_holes(mask) final = pcv.apply_mask(img=imgNIR, mask=mask, mask_color='white') pcv.print_image(final, "./segment/segment-temp.png")
def main(): # Get options #args = options() parser = argparse.ArgumentParser( description="Imaging processing with opencv") parser.add_argument("-i", "--image", help="Input image file.", required=True) parser.add_argument("-o", "--outdir", help="Output directory for image files.", required=False) parser.add_argument("-r", "--result", help="result file.", required=False) parser.add_argument("-w", "--writeimg", help="write out images.", default=False, action="store_true") parser.add_argument("-f", "--fileout", help="output mask file path", required=True) parser.add_argument( "-D", "--debug", help= "can be set to 'print' or None (or 'plot' if in jupyter) prints intermediate images.", default=None) args = parser.parse_args() 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) img = cv2.resize(img, (1280, 960), interpolation=cv2.INTER_AREA) # 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=35, 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(rgb_img=img, channel='b') # Threshold the blue image b_thresh = pcv.threshold.binary(gray_img=b, threshold=180, max_value=255, object_type='light') b_cnt = pcv.threshold.binary(gray_img=b, threshold=180, 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=95, 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=0, y=0, h=img.shape[0], w=img.shape[1]) # 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) count = 0 [rows, cols] = mask.shape for i in range(rows): for j in range(cols): if mask[i, j] > 128: count += 1 re = float(count) / (rows * cols) text = "rec_rate:" + str(round(re, 4)) cv2.putText(mask, text, (40, 50), cv2.FONT_HERSHEY_PLAIN, 2.0, 255, 2) #cv2.imshow("tt",mask) #cv2.waitKey(0) cv2.imwrite(args.fileout, mask) print(str(round(re, 4)))