Merge pull request #40 from kriboe90/dev_kristian

adding support for reading raw sensordata

Author: O-Sven

ledsa/core/_led_helper_functions_s1.py

@@ -1,10 +1,10 @@
 import numpy as np
 
 
-def generate_mask_of_led_areas(image):
+def generate_mask_of_led_areas(image, threshold_factor):
     im_mean = np.mean(image)
     im_max = np.max(image)
-    th = 0.25 * (im_max - im_mean)
+    th = threshold_factor * (im_max - im_mean)
     print("mean pixel value:", im_mean)
     print("max pixel value:", im_max)
     im_set = np.zeros_like(image)

ledsa/core/led_helper.py

@@ -6,6 +6,7 @@
 import os
 import numpy as np
 import matplotlib.pyplot as plt
+import rawpy
 
 sep = os.path.sep
 
@@ -40,8 +41,30 @@ def load_file(filename, delim=' ', dtype='float', atleast_2d=False, silent=False
     return np.atleast_1d(data)
 
 
-def read_file(filename, channel=0):
-    data = plt.imread(filename)
+def read_file(filename, channel, colordepth=14):
+    """
+    Returns a 2D array of channel values depending on the colordepth.
+    8bit is default range for JPG. Bayer array is a 2D array where
+    all channel values except the selected channel are masked.
+    """
+    extension = os.path.splitext(filename)[-1]
+    if extension in ['.JPG','.JPEG', '.jpg', '.jpeg', '.PNG', '.png']:
+        data = plt.imread(filename)
+    elif extension in ['.CR2']:
+        with rawpy.imread(filename) as raw:
+            data = raw.raw_image_visible.copy()
+            filter_array = raw.raw_colors_visible
+            black_level = raw.black_level_per_channel[channel]
+            white_level = raw.white_level
+        channel_range = 2**colordepth - 1
+        channel_array = data.astype(np.int16) - black_level
+        channel_array *= int(channel_range / (white_level - black_level))
+        channel_array = np.clip(channel_array, 0, channel_range)
+        if channel == 0 or channel == 2:
+            channel_array = np.where(filter_array == channel, channel_array, 0)
+        elif channel == 1:
+            channel_array = np.where((filter_array == 1) | (filter_array == 3), channel_array, 0)
+        return channel_array
     return data[:, :, channel]
 
 
@@ -158,9 +181,9 @@ def generate_image_infos_csv(config, build_experiment_infos=False, build_analysi
 # ------------------------------------
 # """
 
-def find_search_areas(image, window_radius=10, skip=10):
+def find_search_areas(image, window_radius=10, skip=10, threshold_factor=0.25):
     print('finding led search areas')
-    led_mask = generate_mask_of_led_areas(image)
+    led_mask = generate_mask_of_led_areas(image, threshold_factor)
     search_areas = find_pos_of_max_col_val_per_area(image, led_mask, skip, window_radius)
     print("\nfound {} leds".format(search_areas.shape[0]))
     return search_areas

ledsa/core/ledsa_conf.py

@@ -8,10 +8,10 @@
 
 class ConfigData(cp.ConfigParser):
     # don't give img_directory a standard value
-    def __init__(self, load_config_file=True, img_directory='.', window_radius=10, num_of_arrays=None,
-                 multicore_processing=False, num_of_cores=1, reference_img=None, date=None, start_time=None,
-                 time_diff_to_image_time=None, time_img=None, img_name_string=None, first_img=None, last_img=None,
-                 first_analyse_img=None, last_analyse_img=None, skip_imgs=0, skip_leds=0, channel=0):
+    def __init__(self, load_config_file=True, img_directory='.', window_radius=10, threshold_factor=0.25,
+                 num_of_arrays=None, multicore_processing=False, num_of_cores=1, reference_img=None, date=None,
+                 start_time=None, time_diff_to_image_time=None, time_img=None, img_name_string=None, first_img=None,
+                 last_img=None, first_analyse_img=None, last_analyse_img=None, skip_imgs=0, skip_leds=0, channel=0):
         cp.ConfigParser.__init__(self, allow_no_value=True)
         if img_directory[-1] != path.sep:
             img_directory += path.sep
@@ -22,6 +22,7 @@ def __init__(self, load_config_file=True, img_directory='.', window_radius=10, n
             self.set('DEFAULT', '# Variables used in multiple parts of LEDSA')
             self['DEFAULT']['   img_directory'] = str(img_directory)
             self['DEFAULT']['   window_radius'] = str(window_radius)
+            self['DEFAULT']['   threshold_factor'] = str(threshold_factor)
             self.set('DEFAULT', '   # Number of LED lines')
             self['DEFAULT']['   num_of_arrays'] = str(num_of_arrays)
             self.set('DEFAULT', '   # Set to True if Multiprocessing should be used')

ledsa/ledsa.py

@@ -46,7 +46,8 @@ def find_search_areas(self, img_filename):
         ref_img_name = "{}{}".format(config['img_directory'], img_filename)
         data = led.read_file(ref_img_name, channel=0)
 
-        self.search_areas = led.find_search_areas(data, skip=1, window_radius=int(config['window_radius']))
+        self.search_areas = led.find_search_areas(data, skip=1, window_radius=int(config['window_radius']),
+                                                  threshold_factor=float(config['threshold_factor']))
 
         out_filename = 'analysis{}led_search_areas.csv'.format(sep)
         np.savetxt(out_filename, self.search_areas, delimiter=',',

ledsa/tests/AcceptanceTests/LedsaATestLibrary.py

@@ -27,7 +27,7 @@ def create_test_image(self, amount=1):
 
     @keyword
     def create_and_fill_config(self):
-        conf = ConfigData(False, '.', 10, 1, False, 1, 'test_img_0.png', None, None, 0, None, 'test_img_{}.png',
+        conf = ConfigData(False, '.', 10, 0.25, 1, False, 1, 'test_img_0.png', None, None, 0, None, 'test_img_{}.png',
                           0, 0, 0, 0)
         conf.set('analyse_positions', '   line_edge_indices', '0 2')
         conf.set('DEFAULT', '   date', '2018:11:27')