Removed edge detection // improved probabilistic transform

README.md

@@ -65,8 +65,6 @@ python main.py
 [general]
 multiprocessing = 1     # multiprocessing: true (1) or false (0). If true, cores - 2 will be used
 limit = 0               # limit the number of hdf files to be processed (will take the first ones)
-
-[path]
 hdf = ../data/granules      # root directory hdf products
 flights = ../data/flights   # root directory for flights
 output = ../data/output     # root directory for results (will create a folder per run)

common/config.py

@@ -99,6 +99,7 @@ def init_params():
         Key.dt_linegap: int(cp.get(_detect_key, 'line_gap')),
         Key.dt_cannysigma: int(cp.get(_detect_key, 'canny_sigma')),
         Key.dt_filter: cp.get(_detect_key, 'filter'),
+        Key.dt_thresh_prob: int(cp.get(_detect_key, 'thresh_prob')),
 
         # Detection - Straight
         Key.dt_thresh_hough: float(cp.get(_detect_key, 'thresh_hough')),

common/constants.py

@@ -120,6 +120,7 @@ class Key:
     dt_linegap = 'probabilistic_line_gap'
     dt_cannysigma = 'filter_canny_sigma'
     dt_filter = 'edge_detection'
+    dt_thresh_prob = 'probabilistic_threshold'
 
     # Detection - Straight
     dt_thresh_hough = 'hough_threshold'

config.ini

@@ -3,7 +3,7 @@ hdf_input_root_dir = ../data/granules
 flight_root_dir = ../data/flights
 output_root_dir = ../data/output
 multiprocessing = 1
-limit = 5
+limit = 0
 
 [product]
 name = MODTBGA
@@ -20,18 +20,20 @@ k = 0.1
 order = 0
 
 [detection]
-method = straight
+method = probabilistic
 split_by = 6
 thresh_bin = 70
 max_px_size = 150
-connectivity = 2
 thresh_ratio = 5
 thresh_hough = 0.5
 postprocess = 0
-line_length = 150
-line_gap = 3
 filter = canny
-canny_sigma = 2
+canny_sigma = 1
+connectivity = 2
+line_length = 20
+line_gap = 5
+thresh_prob = 5
+
 
 [classification]
 thresh_low = 20

detection/hough.py

@@ -51,13 +51,13 @@ def _scharr(img: np.array) -> np.array:
 
 # TODO: add thresholding again
 def probabilistic(img, params):
-    if params[Key.dt_filter] == 'scharr':
-        edges = _scharr(img)
-    else:
-        edges = _canny(img, params[Key.dt_cannysigma])
+    # if params[Key.dt_filter] == 'scharr':
+    #     edges = _scharr(img)
+    # else:
+    #     edges = _canny(img, params[Key.dt_cannysigma])
 
     lines = probabilistic_hough_line(
-        edges, params[Key.dt_linelength], params[Key.dt_linegap])
+        img, threshold=params[Key.dt_thresh_prob], line_length=params[Key.dt_linelength], line_gap=params[Key.dt_linegap])
     labeled = label_segments(img)
     intersect_labels = line_segment_intersect_prob(labeled, lines)
     line_type_labels = get_line_type_segments(

detection/label.py

@@ -171,7 +171,7 @@ def line_segment_intersect_prob(labeled: np.array, lines: []) -> [int]:
             if masked.any():
                 intersections.append(label_num)
 
-    return intersections
+    return list(set(intersections))
 
 
 def _draw_lines(hough_lines: [], dimension):

main.py

@@ -22,7 +22,7 @@ def main():
     results = categorise(granules, params)
     write_csv_pandas(results, params[Key.out_res], result_columns)
 
-    plot_flight_pixel_ratio(results)
+    # plot_flight_pixel_ratio(results)
 
 
 if __name__ == '__main__':