def test_plantcv_triangle_threshold(): img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) device, thresholded = pcv.triangle_auto_threshold(0, img1, 255, "light", 10, debug=None) thresholdedavg = np.average(thresholded) imgavg = np.average(img1) assert thresholdedavg > imgavg
def main(): # Initialize device device = 0 # Parse command-line options args = options() # Read image img, path, filename = pcv.readimage(filename=args.image, debug=args.debug) # Convert RGB to LAB and extract the Blue-Yellow channel device, blue_channel = pcv.rgb2gray_lab(img=img, channel="b", device=device, debug=args.debug) # Threshold the blue image using the triangle autothreshold method device, blue_tri = pcv.triangle_auto_threshold(device=device, img=blue_channel, maxvalue=255, object_type="light", xstep=1, debug=args.debug) # Extract core plant region from the image to preserve delicate plant features during filtering device += 1 plant_region = blue_tri[0:1750, 600:2080] if args.debug is not None: pcv.print_image(filename=str(device) + "_extract_plant_region.png", img=plant_region) # Use a Gaussian blur to disrupt the strong edge features in the cabinet device, blur_gaussian = pcv.gaussian_blur(device=device, img=blue_tri, ksize=(3, 3), sigmax=0, sigmay=None, debug=args.debug) # Threshold the blurred image to remove features that were blurred device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian, threshold=250, maxValue=255, object_type="light", device=device, debug=args.debug) # Add the plant region back in to the filtered image device += 1 blur_thresholded[0:1750, 600:2080] = plant_region if args.debug is not None: pcv.print_image(filename=str(device) + "_replace_plant_region.png", img=blur_thresholded) # Fill small noise device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded), mask=np.copy(blur_thresholded), size=50, device=device, debug=args.debug) # Apply a small median blur to break up pot edges device, med_blur = pcv.median_blur(img=np.copy(blue_fill_50), ksize=3, device=device, debug=args.debug) # Define an ROI for the barcode label device, label_roi, label_hierarchy = pcv.define_roi(img=img, shape="rectangle", device=device, roi=None, roi_input="default", debug=args.debug, adjust=True, x_adj=1100, y_adj=1350, w_adj=-1070, h_adj=-590) # Identify all remaining contours in the binary image device, contours, hierarchy = pcv.find_objects(img=img, mask=np.copy(med_blur), device=device, debug=args.debug) # Remove contours completely contained within the label region of interest device, remove_label_mask = remove_countors_roi(mask=med_blur, contours=contours, hierarchy=hierarchy, roi=label_roi, device=device, debug=args.debug) # Identify objects device, contours, contour_hierarchy = pcv.find_objects( img=img, mask=remove_label_mask, device=device, debug=args.debug) # Define ROI device, roi, roi_hierarchy = pcv.define_roi(img=img, shape="rectangle", device=device, roi=None, roi_input="default", debug=args.debug, adjust=True, x_adj=565, y_adj=0, w_adj=-490, h_adj=-600) # Decide which objects to keep device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects( img=img, roi_type="partial", roi_contour=roi, roi_hierarchy=roi_hierarchy, object_contour=contours, obj_hierarchy=contour_hierarchy, device=device, debug=args.debug) # If there are no contours left we cannot measure anything if len(roi_contours) > 0: # Object combine kept objects device, plant_contour, plant_mask = pcv.object_composition( img=img, contours=roi_contours, hierarchy=roi_contour_hierarchy, device=device, debug=args.debug) outfile = False if args.writeimg: outfile = args.outdir + "/" + filename # Find shape properties, output shape image (optional) device, shape_header, shape_data, shape_img = pcv.analyze_object( img=img, imgname=args.image, obj=plant_contour, mask=plant_mask, device=device, debug=args.debug, filename=outfile) # Shape properties relative to user boundary line (optional) device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound( img=img, imgname=args.image, obj=plant_contour, mask=plant_mask, line_position=690, device=device, debug=args.debug, filename=outfile) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, # output color analyzed images (optional) device, color_header, color_data, color_img = pcv.analyze_color( img=img, imgname=args.image, mask=plant_mask, bins=256, device=device, debug=args.debug, hist_plot_type=None, pseudo_channel="v", pseudo_bkg="img", resolution=300, filename=outfile) # Output shape and color data result = open(args.result, "a") result.write('\t'.join(map(str, shape_header)) + "\n") result.write('\t'.join(map(str, shape_data)) + "\n") for row in shape_img: result.write('\t'.join(map(str, row)) + "\n") result.write('\t'.join(map(str, color_header)) + "\n") result.write('\t'.join(map(str, color_data)) + "\n") result.write('\t'.join(map(str, boundary_header)) + "\n") result.write('\t'.join(map(str, boundary_data)) + "\n") result.write('\t'.join(map(str, boundary_img)) + "\n") for row in color_img: result.write('\t'.join(map(str, row)) + "\n") result.close()
def main(): # Initialize device device = 0 # Parse command-line options args = options() # Read image img, path, filename = pcv.readimage(filename=args.image, debug=args.debug) # Convert RGB to LAB and extract the Blue-Yellow channel device, blue_channel = pcv.rgb2gray_lab(img=img, channel="b", device=device, debug=args.debug) # Threshold the blue image using the triangle autothreshold method device, blue_tri = pcv.triangle_auto_threshold(device=device, img=blue_channel, maxvalue=255, object_type="light", xstep=1, debug=args.debug) # Extract core plant region from the image to preserve delicate plant features during filtering device += 1 plant_region = blue_tri[0:1750, 600:2080] if args.debug is not None: pcv.print_image(filename=str(device) + "_extract_plant_region.png", img=plant_region) # Use a Gaussian blur to disrupt the strong edge features in the cabinet device, blur_gaussian = pcv.gaussian_blur(device=device, img=blue_tri, ksize=(3, 3), sigmax=0, sigmay=None, debug=args.debug) # Threshold the blurred image to remove features that were blurred device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian, threshold=250, maxValue=255, object_type="light", device=device, debug=args.debug) # Add the plant region back in to the filtered image device += 1 blur_thresholded[0:1750, 600:2080] = plant_region if args.debug is not None: pcv.print_image(filename=str(device) + "_replace_plant_region.png", img=blur_thresholded) # Fill small noise device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded), mask=np.copy(blur_thresholded), size=50, device=device, debug=args.debug) # Identify objects device, contours, contour_hierarchy = pcv.find_objects(img=img, mask=blue_fill_50, device=device, debug=args.debug) # Define ROI device, roi, roi_hierarchy = pcv.define_roi(img=img, shape="rectangle", device=device, roi=None, roi_input="default", debug=args.debug, adjust=True, x_adj=565, y_adj=0, w_adj=-490, h_adj=-250) # Decide which objects to keep device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects( img=img, roi_type="partial", roi_contour=roi, roi_hierarchy=roi_hierarchy, object_contour=contours, obj_hierarchy=contour_hierarchy, device=device, debug=args.debug) # If there are no contours left we cannot measure anything if len(roi_contours) > 0: # Object combine kept objects device, plant_contour, plant_mask = pcv.object_composition( img=img, contours=roi_contours, hierarchy=roi_contour_hierarchy, device=device, debug=args.debug) outfile = False if args.writeimg: outfile = args.outdir + "/" + filename # Find shape properties, output shape image (optional) device, shape_header, shape_data, shape_img = pcv.analyze_object( img=img, imgname=args.image, obj=plant_contour, mask=plant_mask, device=device, debug=args.debug, filename=outfile) # Shape properties relative to user boundary line (optional) device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound( img=img, imgname=args.image, obj=plant_contour, mask=plant_mask, line_position=440, device=device, debug=args.debug, filename=outfile) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, # output color analyzed images (optional) device, color_header, color_data, color_img = pcv.analyze_color( img=img, imgname=args.image, mask=plant_mask, bins=256, device=device, debug=args.debug, hist_plot_type=None, pseudo_channel="v", pseudo_bkg="img", resolution=300, filename=outfile) # Output shape and color data result = open(args.result, "a") result.write('\t'.join(map(str, shape_header)) + "\n") result.write('\t'.join(map(str, shape_data)) + "\n") for row in shape_img: result.write('\t'.join(map(str, row)) + "\n") result.write('\t'.join(map(str, color_header)) + "\n") result.write('\t'.join(map(str, color_data)) + "\n") result.write('\t'.join(map(str, boundary_header)) + "\n") result.write('\t'.join(map(str, boundary_data)) + "\n") result.write('\t'.join(map(str, boundary_img)) + "\n") for row in color_img: result.write('\t'.join(map(str, row)) + "\n") result.close() # Find matching NIR image device, nirpath = pcv.get_nir(path=path, filename=filename, device=device, debug=args.debug) nir_rgb, nir_path, nir_filename = pcv.readimage(nirpath) nir_img = cv2.imread(nirpath, 0) # Make mask glovelike in proportions via dilation device, d_mask = pcv.dilate(plant_mask, kernel=1, i=0, device=device, debug=args.debug) # Resize mask prop2, prop1 = conv_ratio() device, nmask = pcv.resize(img=d_mask, resize_x=prop1, resize_y=prop2, device=device, debug=args.debug) # Convert the resized mask to a binary mask device, bmask = pcv.binary_threshold(img=nmask, threshold=0, maxValue=255, object_type="light", device=device, debug=args.debug) device, crop_img = crop_sides_equally(mask=bmask, nir=nir_img, device=device, debug=args.debug) # position, and crop mask device, newmask = pcv.crop_position_mask(img=nir_img, mask=crop_img, device=device, x=34, y=9, v_pos="top", h_pos="right", debug=args.debug) # Identify objects device, nir_objects, nir_hierarchy = pcv.find_objects(img=nir_rgb, mask=newmask, device=device, debug=args.debug) # Object combine kept objects device, nir_combined, nir_combinedmask = pcv.object_composition( img=nir_rgb, contours=nir_objects, hierarchy=nir_hierarchy, device=device, debug=args.debug) if args.writeimg: outfile = args.outdir + "/" + nir_filename # Analyze NIR signal data device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity( img=nir_img, rgbimg=nir_rgb, mask=nir_combinedmask, bins=256, device=device, histplot=False, debug=args.debug, filename=outfile) # Analyze the shape of the plant contour from the NIR image device, nshape_header, nshape_data, nir_shape = pcv.analyze_object( img=nir_img, imgname=nir_filename, obj=nir_combined, mask=nir_combinedmask, device=device, debug=args.debug, filename=outfile) # Write NIR data to co-results file coresult = open(args.coresult, "a") coresult.write('\t'.join(map(str, nhist_header)) + "\n") coresult.write('\t'.join(map(str, nhist_data)) + "\n") for row in nir_imgs: coresult.write('\t'.join(map(str, row)) + "\n") coresult.write('\t'.join(map(str, nshape_header)) + "\n") coresult.write('\t'.join(map(str, nshape_data)) + "\n") coresult.write('\t'.join(map(str, nir_shape)) + "\n") coresult.close()
# Pipeline step device = 0 # White balance device, corr_img = pcv.white_balance(device, img, debug, [1450, 400, 5, 5]) # Modify the 4 numbers in square brackets to draw a square that rests on a white color card, or white color marker. # Convert RBG to Grey using Blue-Yellow Channel device, b = pcv.rgb2gray_lab(corr_img, 'b', device, debug) # Channel 'b' should make the plant brighter over background. Otherwise, use a different channel. # Gaussian blur device, g_blur = pcv.gaussian_blur(device, b, (5,5), 0, None, debug) # Threshold the blue image device, img_thresh = pcv.triangle_auto_threshold(device, g_blur, 255, 'light', 20, args.debug) device, img_cnt = pcv.triangle_auto_threshold(device, g_blur, 255, 'light', 20, args.debug) # Modify the number 20 in both lines to an appropriate threshold that best separates plant from background # Fill small objects device, img_fill = pcv.fill(img_thresh, img_cnt, 50, device, args.debug) # Modify the number 50 to what best fills holes in the plant (white) and background (black) # Mask white-balanced image (here, based on blue channel) device, masked = pcv.apply_mask(corr_img, b_fill, 'white', device, debug) # Identify objects device, id_objects, obj_hierarchy = pcv.find_objects(masked, img_fill, device, args.debug) # Define region of interest device, roi1, roi_hierarchy = pcv.define_roi(masked, 'rectangle', device, None, 'default', debug, True,
def test_plantcv_triangle_threshold(): img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1) device, thresholded = pcv.triangle_auto_threshold(0, img1, 255, "light", 10, debug=None) thresholdedavg = np.average(thresholded) imgavg = np.average(img1) assert thresholdedavg > imgavg