Fixed line drawing in detection // working state

config.ini

 [general]
-multiprocessing = 0
+multiprocessing = 1
 limit = 0
 
 [product]

detection/hough.py

@@ -65,7 +65,7 @@ def detect_lines_blockwise(granule: {}, params: {}):
     if granule.get(col.flight_count) <= 0:
         return granule
 
-    img = granule.get(col.cda_i)
+    img = granule.get(col.cda)
     split_by = 0
 
     # Split blocks

detection/label.py

@@ -77,7 +77,7 @@ def _correct_value(val, shape):
     return int(val)
 
 
-# TODO: refactor
+# DO NOT TOUCH THIS CODE, IT DOES NOT WANT TO BE REFACTORED SO PLEASE LEAVE IT LIKE THIS
 def get_line_label_intersections(labeled: np.array, line_peaks, shape: (int, int)) -> [int]:
     intersections = []
 
@@ -86,20 +86,21 @@ def get_line_label_intersections(labeled: np.array, line_peaks, shape: (int, int
         if angle == 0.0:
             continue
 
-        points = _peaks_to_points(angle, dist, shape)
+        y0 = _correct_value((dist - 0 * np.cos(angle) / np.sin(angle)), shape[0])
+        y1 = _correct_value((dist - shape[1] * np.cos(angle) / np.sin(angle)), shape[1])
+        x0 = 0
+        x1 = _correct_value(shape[1], shape[1])
 
-        if points is None:
+        if y0 is None or y1 is None or x0 is None or x1 is None:
             continue
 
         line_arr = np.zeros(labeled.shape, dtype=int)
-        rr, cc = sk_line(*points)
+        rr, cc = sk_line(x1, x0, y1, y0)
         line_arr[rr, cc] = 1
 
         for label_num in range(1, labeled.max()):
-
             if label_num == 0:
                 continue
-
             mask = labeled == label_num
             masked = line_arr * mask
 
@@ -109,6 +110,38 @@ def get_line_label_intersections(labeled: np.array, line_peaks, shape: (int, int
     return list(set(intersections))
 
 
+# def _line_segment_intersect(labeled, h, theta, d, threshold, shape):
+#     intersections = []
+#
+#     for _, angle, dist in zip(*hough_line_peaks(h, theta, d, threshold=threshold * h.max())):
+#
+#         if angle == 0.0:
+#             continue
+#
+#         y0 = _correct_value((dist - 0 * np.cos(angle) / np.sin(angle)), shape[0])
+#         y1 = _correct_value((dist - shape[1] * np.cos(angle) / np.sin(angle)), shape[1])
+#         x0 = 0
+#         x1 = _correct_value(shape[1], shape[1])
+#
+#         if y0 is None or y1 is None or x0 is None or x1 is None:
+#             continue
+#
+#         line_arr = np.zeros(labeled.shape, dtype=int)
+#         rr, cc = line(x1, x0, y1, y0)
+#         line_arr[rr, cc] = 1
+#
+#         for label_num in range(1, labeled.max()):
+#             if label_num == 0:
+#                 continue
+#             mask = labeled == label_num
+#             masked = line_arr * mask
+#
+#             if masked.any():
+#                 intersections.append(label_num)
+#
+#     return list(set(intersections))
+
+
 # TODO: combine to one method (or several)
 def line_segment_intersect_prob(labeled: np.array, lines: []) -> [int]:
     intersections = []

main.py

@@ -6,14 +6,13 @@ from common.config import init_params
 from common.constants import columns_to_save
 from common.fileio import write_results, write_config
 from common.path import create_path
-import os
 
 params = init_params()
 
 results_file = create_path([params.get('output_dir'), 'results.csv'])
 config_file = create_path([params.get('output_dir'), 'config.json'])
-# input_files = glob(params.get('hdf_dir') + '/*.hdf')
-input_files = [os.path.join(params.get('hdf_dir'), 'MODTBGA.A2012094.h15v03.006.2015240000438.hdf')]
+input_files = glob(params.get('hdf_dir') + '/*.hdf')
+# input_files = [os.path.join(params.get('hdf_dir'), 'MODTBGA.A2012094.h15v03.006.2015240000438.hdf')]
 
 limit = params.get('limit')
 

pipeline.py

 from extraction import hdf, flights
-from preprocessing import cda, misc
+from preprocessing import cda
 from detection import hough
 from plot import plots
 
@@ -21,7 +21,6 @@ def run(filepath: str, params: {}):
     PREPROCESSING
     """
     granule = cda.preprocess_image(granule, params)
-    granule = misc.preprocess_image(granule, params)
 
     """
     DETECTION