added bkg_images and cleaned up directory

Author: maxjfeldman

scripts/image_analysis/nir_sv/bkgrd_ave_z2500.png

@@ -3,7 +3,7 @@
 # This is a computational pipeline which uses the plantcv module to sharpen, filter and analyze NIR images
 # Pipeline designed for use with Setaria plants at zoom X2500
 # The strategy/methodology is adopted from the textbook "Digital Image Processing" by Gonzalez and Woods
-# Version 0.2 Max Feldman 4.6.14
+# Version 0.9 Max Feldman 7.29.14
 
 import argparse
 import scipy
@@ -37,11 +37,30 @@ def main():
     if args.debug:
       print("Analyzing your image dude...")
     # Read image
+    device = 0
     img = cv2.imread(args.image, flags=0)
+    path, img_name = os.path.split(args.image)
+    # Read in image which is average of average of backgrounds
+    img_bkgrd = cv2.imread("bkgrd_ave_z2500.png", flags=0)
+    
+    # NIR images for burnin2 are up-side down. This may be fixed in later experiments
+    img =  ndimage.rotate(img, 180)
+    img_bkgrd =  ndimage.rotate(img_bkgrd, 180)
+    
+    # Subtract the image from the image background to make the plant more prominent
+    device, bkg_sub_img = pcv.image_subtract(img, img_bkgrd, device, args.debug)
+    if args.debug:
+        pcv.plot_hist(bkg_sub_img, 'bkg_sub_img')
+    device, bkg_sub_thres_img = pcv.binary_threshold(bkg_sub_img, 145, 255, 'dark', device, args.debug)
+    bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 30, 220)
+    if args.debug:
+        cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
+    
+    #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
+
     # if a region of interest is specified read it in
     roi = cv2.imread(args.roi)
-    # Pipeline step
-    device = 0
+
 
     # Start by examining the distribution of pixel intensity values
     if args.debug:
@@ -107,6 +126,7 @@ def main():
     kern[1,0:3]=1
     kern[0:3,1]=1
 
+    
     # Perform erosion with 4 small kernels
     device, e1_img = pcv.erode(tr_es_img, kern1, 1, device, args.debug)
     device, e2_img = pcv.erode(tr_es_img, kern2, 1, device, args.debug)
@@ -119,17 +139,18 @@ def main():
     device, c1234_img = pcv.logical_or(c123_img, e4_img, device, args.debug)
 
     # Perform dilation
-    device, dil_img = pcv.dilate(c1234_img, kern, 1, device, args.debug)
-    
+    # device, dil_img = pcv.dilate(c1234_img, kern, 1, device, args.debug)
+    device, comb_img = pcv.logical_or(c1234_img, bkg_sub_thres_img, device, args.debug)
+
     # Get masked image
     # The dilated image may contain some pixels which are not plant
-    device, masked_erd = pcv.apply_mask(img, c1234_img, 'black', device, args.debug)
-    device, masked_erd_dil = pcv.apply_mask(img, dil_img, 'black', device, args.debug)
+    device, masked_erd = pcv.apply_mask(img, comb_img, 'black', device, args.debug)
+    # device, masked_erd_dil = pcv.apply_mask(img, dil_img, 'black', device, args.debug)
 
     # Need to remove the edges of the image, we did that by generating a set of rectangles to mask the edges
     # img is (254 X 320)
     # mask for the bottom of the image
-    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (1,184), (318,252), device, args.debug)
+    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (120,184), (215,252), device, args.debug)
     # mask for the left side of the image
     device, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img, (1,1), (85,252), device, args.debug)
     # mask for the right side of the image
@@ -143,29 +164,34 @@ def main():
     device, bx1234_img = pcv.logical_or(bx123_img, box4_img, device, args.debug)
     device, inv_bx1234_img = pcv.invert(bx1234_img, device, args.debug)
 
-    
-    device, inv_box1_img = pcv.invert(box1_img, device, args.debug)
-    
-    # Lets try the reverse stategy
     # Make a ROI around the plant, include connected objects
     # Apply the box mask to the image
     # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
-    device, edge_masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
+
+    device, edge_masked_img = pcv.apply_mask(masked_erd, inv_bx1234_img, 'black', device, args.debug)
+
     device, roi_img, roi_contour, roi_hierarchy = pcv.rectangle_mask(img, (120,75), (200,184), device, args.debug)
+
     plant_objects, plant_hierarchy = cv2.findContours(edge_masked_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
-    
     device, roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi_contour, roi_hierarchy, plant_objects, plant_hierarchy, device, args.debug)
+    
 
     # Apply the box mask to the image
     # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
     device, masked_img = pcv.apply_mask(kept_mask, inv_bx1234_img, 'black', device, args.debug)
+    rgb = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
     # Generate a binary to send to the analysis function
     device, mask = pcv.binary_threshold(masked_img, 1, 255, 'light', device, args.debug)
+    mask3d = np.copy(mask)
+    plant_objects_2, plant_hierarchy_2 = cv2.findContours(mask3d,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
+    device, o, m = pcv.object_composition(rgb, roi_objects, hierarchy5, device, args.debug)
 
     ### Analysis ###
-    device, hist_header, hist_data, h_norm = pcv.analyze_NIR_intensity(img, args.image, mask, 256, device, args.debug, args.image)
-    #device, shape_header, shape_data, ori_img = pcv.analyze_object(img, args.img, roi_objects, mask, device, args.debug, args.image)
+    device, hist_header, hist_data, h_norm = pcv.analyze_NIR_intensity(img, args.image, mask, 256, device, args.debug, args.outdir + '/' + img_name)
+    device, shape_header, shape_data, ori_img = pcv.analyze_object(rgb, args.image, o, m, device, args.debug, args.outdir + '/' + img_name)
+    
     pcv.print_results(args.image, hist_header, hist_data)
+    pcv.print_results(args.image, shape_header, shape_data)
 
 if __name__ == '__main__':
     main()

scripts/image_analysis/nir_sv/bkgrd_ave_z3500.png

@@ -1,11 +1,10 @@
 #!/usr/bin/python
 
 # This is a computational pipeline which uses the plantcv module to sharpen, filter and analyze NIR images
-# Pipeline designed for use with Setaria plants at zoom X500
+# Pipeline designed for use with Setaria plants at zoom X3500
 # The strategy/methodology is adopted from the textbook "Digital Image Processing" by Gonzalez and Woods
 # Version 0.9 Max Feldman 7.29.14
 
-
 import argparse
 import scipy
 from scipy import ndimage
@@ -27,7 +26,6 @@ def options():
     parser.add_argument("-i", "--image", help="Input image file.", required=True)
     parser.add_argument("-m", "--roi", help="Input region of interest file.", required=False)
     parser.add_argument("-o", "--outdir", help="Output directory for image files.", required=False)
-    # parser.add_argument("-b", "--bkgrd", help="Image of average pixel intensity of background.", required=True)
     parser.add_argument("-D", "--debug", help="Turn on debug, prints intermediate images.", action="store_true")
     args = parser.parse_args()
     return args
@@ -44,27 +42,26 @@ def main():
     img = cv2.imread(args.image, flags=0)
     path, img_name = os.path.split(args.image)
     # Read in image which is average of average of backgrounds
-    img_bkgrd = cv2.imread("/home/mgehan/LemnaTec/ddpsc_image_git/docs/background_nir_z3500.png", flags=0)
+    img_bkgrd = cv2.imread("bkgrd_ave_z3500.png", flags=0)
 
     # NIR images for burnin2 are up-side down. This may be fixed in later experiments
-    img =  ndimage.rotate(img, 0)
-    img_bkgrd =  ndimage.rotate(img_bkgrd, 0)
+    img =  ndimage.rotate(img, 180)
+    img_bkgrd =  ndimage.rotate(img_bkgrd, 180)
 
     # Subtract the image from the image background to make the plant more prominent
     device, bkg_sub_img = pcv.image_subtract(img, img_bkgrd, device, args.debug)
     if args.debug:
         pcv.plot_hist(bkg_sub_img, 'bkg_sub_img')
-    device, bkg_sub_thres_img = pcv.binary_threshold(bkg_sub_img, 150, 255, 'dark', device, args.debug)
-    bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 50, 190)
+    device, bkg_sub_thres_img = pcv.binary_threshold(bkg_sub_img, 145, 255, 'dark', device, args.debug)
+    bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 30, 220)
     if args.debug:
         cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
-    
+
     #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
-    
+
     # if a region of interest is specified read it in
     roi = cv2.imread(args.roi)
-    
-    
+
     # Start by examining the distribution of pixel intensity values
     if args.debug:
       pcv.plot_hist(img, 'hist_img')
@@ -83,7 +80,7 @@ def main():
     device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
     if args.debug:
       pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')
-    
+      
     # Sobel filtering  
     # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
     device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, device, args.debug)
@@ -112,7 +109,7 @@ def main():
       pcv.plot_hist(edge_shrp_img, 'hist_edge_shrp_img')
 
     # Perform thresholding to generate a binary image
-    device, tr_es_img = pcv.binary_threshold(edge_shrp_img, 150, 255, 'dark', device, args.debug)
+    device, tr_es_img = pcv.binary_threshold(edge_shrp_img, 145, 255, 'dark', device, args.debug)
 
     # Prepare a few small kernels for morphological filtering
     kern = np.zeros((3,3), dtype=np.uint8)
@@ -129,6 +126,7 @@ def main():
     kern[1,0:3]=1
     kern[0:3,1]=1
 
+    
     # Perform erosion with 4 small kernels
     device, e1_img = pcv.erode(tr_es_img, kern1, 1, device, args.debug)
     device, e2_img = pcv.erode(tr_es_img, kern2, 1, device, args.debug)
@@ -151,13 +149,13 @@ def main():
 
     # Need to remove the edges of the image, we did that by generating a set of rectangles to mask the edges
     # img is (254 X 320)
-    
+
     # mask for the bottom of the image
-    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (75,212), (250,252), device, args.debug)
+    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (100,210), (230,252), device, args.debug)
     # mask for the left side of the image
-    device, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img, (1,1), (75,252), device, args.debug)
+    device, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img, (1,1), (85,252), device, args.debug)
     # mask for the right side of the image
-    device, box3_img, rect_contour3, hierarchy3 = pcv.rectangle_mask(img, (245,1), (318,252), device, args.debug)
+    device, box3_img, rect_contour3, hierarchy3 = pcv.rectangle_mask(img, (240,1), (318,252), device, args.debug)
     # mask the edges
     device, box4_img, rect_contour4, hierarchy4 = pcv.border_mask(img, (1,1), (318,252), device, args.debug)
 
@@ -167,36 +165,34 @@ def main():
     device, bx1234_img = pcv.logical_or(bx123_img, box4_img, device, args.debug)
     device, inv_bx1234_img = pcv.invert(bx1234_img, device, args.debug)
 
+
     # Make a ROI around the plant, include connected objects
     # Apply the box mask to the image
     # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
-    
     device, edge_masked_img = pcv.apply_mask(masked_erd, inv_bx1234_img, 'black', device, args.debug)
-    
-    device, roi_img, roi_contour, roi_hierarchy = pcv.rectangle_mask(img, (50,50), (280,215), device, args.debug)
-    
+    device, roi_img, roi_contour, roi_hierarchy = pcv.rectangle_mask(img, (100,75), (220,208), device, args.debug)
     plant_objects, plant_hierarchy = cv2.findContours(edge_masked_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
+    
     device, roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi_contour, roi_hierarchy, plant_objects, plant_hierarchy, device, args.debug)
 
+      
     # Apply the box mask to the image
+    # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
     device, masked_img = pcv.apply_mask(kept_mask, inv_bx1234_img, 'black', device, args.debug)
     rgb = cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
-    
+
     # Generate a binary to send to the analysis function
     device, mask = pcv.binary_threshold(masked_img, 1, 255, 'light', device, args.debug)
     mask3d = np.copy(mask)
     plant_objects_2, plant_hierarchy_2 = cv2.findContours(mask3d,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
     device, o, m = pcv.object_composition(rgb, roi_objects, hierarchy5, device, args.debug)
 
-    
     ### Analysis ###
     device, hist_header, hist_data, h_norm = pcv.analyze_NIR_intensity(img, args.image, mask, 256, device, args.debug, args.outdir + '/' + img_name)
-    
     device, shape_header, shape_data, ori_img = pcv.analyze_object(rgb, args.image, o, m, device, args.debug, args.outdir + '/' + img_name)
+    
     pcv.print_results(args.image, hist_header, hist_data)
     pcv.print_results(args.image, shape_header, shape_data)
 
-    
-    
 if __name__ == '__main__':
     main()

scripts/image_analysis/nir_sv/bkgrd_ave_z500.png

@@ -44,11 +44,11 @@ def main():
     img = cv2.imread(args.image, flags=0)
     path, img_name = os.path.split(args.image)
     # Read in image which is average of average of backgrounds
-    img_bkgrd = cv2.imread("background.png", flags=0)
+    img_bkgrd = cv2.imread("bkgrd_ave_z500.png", flags=0)
 
     # NIR images for burnin2 are up-side down. This may be fixed in later experiments
-    img =  ndimage.rotate(img, 0)
-    img_bkgrd =  ndimage.rotate(img_bkgrd, 0)
+    img =  ndimage.rotate(img, 180)
+    img_bkgrd =  ndimage.rotate(img_bkgrd, 180)
 
     # Subtract the image from the image background to make the plant more prominent
     device, bkg_sub_img = pcv.image_subtract(img, img_bkgrd, device, args.debug)
@@ -58,12 +58,12 @@ def main():
     bkg_sub_thres_img = cv2.inRange(bkg_sub_img, 50, 190)
     if args.debug:
         cv2.imwrite('bkgrd_sub_thres.png', bkg_sub_thres_img)
-    
+  
     #device, bkg_sub_thres_img = pcv.binary_threshold_2_sided(img_bkgrd, 50, 190, device, args.debug)
-    
+
     # if a region of interest is specified read it in
     roi = cv2.imread(args.roi)
-    
+
 
     # Start by examining the distribution of pixel intensity values
     if args.debug:
@@ -83,7 +83,7 @@ def main():
     device, lp_shrp_img = pcv.image_subtract(img, lp_img, device, args.debug)
     if args.debug:
       pcv.plot_hist(lp_shrp_img, 'hist_lp_shrp')
-    
+
     # Sobel filtering  
     # 1st derivative sobel filtering along horizontal axis, kernel = 1, unscaled)
     device, sbx_img = pcv.sobel_filter(img, 1, 0, 1, 1, device, args.debug)
@@ -112,7 +112,7 @@ def main():
       pcv.plot_hist(edge_shrp_img, 'hist_edge_shrp_img')
 
     # Perform thresholding to generate a binary image
-    device, tr_es_img = pcv.binary_threshold(edge_shrp_img, 132, 255, 'dark', device, args.debug)
+    device, tr_es_img = pcv.binary_threshold(edge_shrp_img, 145, 255, 'dark', device, args.debug)
 
     # Prepare a few small kernels for morphological filtering
     kern = np.zeros((3,3), dtype=np.uint8)
@@ -153,7 +153,7 @@ def main():
     # img is (254 X 320)
 
     # mask for the bottom of the image
-    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (128,245), (192,252), device, args.debug)
+    device, box1_img, rect_contour1, hierarchy1 = pcv.rectangle_mask(img, (128,226), (192,252), device, args.debug)
     # mask for the left side of the image
     device, box2_img, rect_contour2, hierarchy2 = pcv.rectangle_mask(img, (1,1), (75,252), device, args.debug)
     # mask for the right side of the image
@@ -167,13 +167,15 @@ def main():
     device, bx1234_img = pcv.logical_or(bx123_img, box4_img, device, args.debug)
     device, inv_bx1234_img = pcv.invert(bx1234_img, device, args.debug)
 
+    
+   
     # Make a ROI around the plant, include connected objects
     # Apply the box mask to the image
     # device, masked_img = pcv.apply_mask(masked_erd_dil, inv_bx1234_img, 'black', device, args.debug)
-    
+
     device, edge_masked_img = pcv.apply_mask(masked_erd, inv_bx1234_img, 'black', device, args.debug)
-    
-    device, roi_img, roi_contour, roi_hierarchy = pcv.rectangle_mask(img, (120,75), (200,245), device, args.debug)
+
+    device, roi_img, roi_contour, roi_hierarchy = pcv.rectangle_mask(img, (120,75), (200,226), device, args.debug)
 
     plant_objects, plant_hierarchy = cv2.findContours(edge_masked_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
     device, roi_objects, hierarchy5, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi_contour, roi_hierarchy, plant_objects, plant_hierarchy, device, args.debug)
@@ -191,7 +193,7 @@ def main():
 
     ### Analysis ###
     device, hist_header, hist_data, h_norm = pcv.analyze_NIR_intensity(img, args.image, mask, 256, device, args.debug, args.outdir + '/' + img_name)
-    
+ 
     device, shape_header, shape_data, ori_img = pcv.analyze_object(rgb, args.image, o, m, device, args.debug, args.outdir + '/' + img_name)
     pcv.print_results(args.image, hist_header, hist_data)
     pcv.print_results(args.image, shape_header, shape_data)