remove prints & tests from "guess block size" related scripts.

guess_block_size.py

@@ -112,8 +112,6 @@ def guess_nice_block_size(src: np.ndarray, freq_analysis_only: bool = False,
     desc_analysis_pairs = [] if freq_analysis_only else analyze_keypoint_scales(src)
 
     # all pairs should come already sorted in descending order w/ respect to weight
-    print(freq_analysis_pairs)
-    print(desc_analysis_pairs)
 
     # filter very small distances, with respect to the src size
     min_dim = min(src.shape[:2])
@@ -133,23 +131,4 @@ def guess_nice_block_size(src: np.ndarray, freq_analysis_only: bool = False,
         *normalize_weights(desc_analysis_pairs)
     ]  # may contain duplicates or multiples, that is expected
 
-    print(f"final pairs: {final_pairs}")
     return make_guess(final_pairs, min_dim, max_block_size)
-
-
-if __name__ == "__main__":
-    from cv2 import imread, IMREAD_GRAYSCALE
-
-    image_path = "t9.png"
-    image = imread(image_path, IMREAD_GRAYSCALE)
-    block_size = guess_nice_block_size(image, freq_analysis_only=False)
-    print(f"guessed block_size = {block_size}")
-    # prev values
-    # t9  -> 64
-    # t16 -> 55
-    # t18 -> 82
-    # new values  (freq_only=false, true)
-    # t9  -> 64, 64
-    # t16 -> 44, 44
-    # t18 -> 48, 42
-    # t166 -> 99, 88  (fixed!)

misc/bse_desc_util.py

@@ -105,12 +105,4 @@ def analyze_keypoint_scales(image: np.ndarray) -> size_weight_pairs:
     dist_weight_pairs.extend([(round(distance_pairs[i]), pairs_areas[i] * label_counts[i])
                               for i, _ in enumerate(labels_coverage)])
     dist_weight_pairs.sort(key=lambda i: i[1], reverse=True)
-    print(f"sorted descs = {dist_weight_pairs}")
     return dist_weight_pairs
-
-
-if __name__ == "__main__":
-    image_path = '../t9.png'
-    image = cv2.imread(image_path)
-    data = analyze_keypoint_scales(image)
-    print(data)

misc/bse_ft_util.py

@@ -53,12 +53,4 @@ def compute_wavelens_of_interest(spectrum: np.ndarray, max_to_fetch: int = 16) -
 def analyze_freq_spectrum(image: np.ndarray, max_items: int = 16) -> size_weight_pairs:
     magnitude_spectrum = compute_fft(image)
     wlen_mag_pairs = compute_wavelens_of_interest(magnitude_spectrum, max_items)
-    print(f"sorted wavelens = {wlen_mag_pairs}")
     return wlen_mag_pairs
-
-
-if __name__ == "__main__":
-    image_path = "../t16.png"
-    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
-    data = analyze_freq_spectrum(image)
-    print(data)