Minor typo and import fixes

README.md

@@ -40,7 +40,7 @@ It's the recommended way to explore this tool. It provides notebooks for playing
 
 ## Unit Tests
 
-All the codes in `src` are covered.
+All the code in `src` is covered.
 
 ```
 cd tests
@@ -59,7 +59,7 @@ The loader reads the file and creates a mask.
 
 The mask is a numpy array. The bright parts are set to 255, the rest is set to 0. It contains ONLY these 2 numbers.
 
-#### Atrributes
+#### Attributes
 
 - low_threshold = (0, 0, 250)
 
@@ -70,7 +70,7 @@ They control the creation of the mask, used in the function `cv.inRange`.
 
 #### Result
 
-Here, yellow is `255`, purple is `0`.
+Here, yellow is `255` and purple is `0`.
 
 ![mask](./data/output/mask.jpeg)
 
@@ -80,19 +80,19 @@ The extractor, first, generates the regions from the mask.
 
 Then, it removes the small and the big regions because the signature is neither too big nor too small.
 
-The process is as followed.
+The process is as follows:
 
 1. label the image
 
    `skimage.measure.label` labels the connected regions of an integer array. It returns a labeled array, where all connected regions are assigned the same integer value.
 
-2. calculate the average size of regions
+2. calculate the average size of the regions
 
-   Here, the size means **the number of the pixels in a region**. 
+   Here, the size means **the number of pixels in a region**. 
 
-   We accumulate the number of the pixels in all the regions, `total_pixels`. The average size is `total_pixels / nb_regions`.
+   We accumulate the number of pixels in all the regions, `total_pixels`. The average size is `total_pixels / nb_regions`.
 
-   If the size of a region is smaller `min_area_size`, this region is ignored. `min_area_size` is given by the user.
+   If the size of a region is smaller than `min_area_size`, this region is ignored. `min_area_size` is given by the user.
 
 3. calculate the size of the small outlier
 
@@ -105,10 +105,10 @@ The process is as followed.
 4. calculate the size of the big outlier
 
     ```
-    big_size_outlier = small_size_outlier * amplfier
+    big_size_outlier = small_size_outlier * amplifier
     ```
 
-    `amplfier` is given by the user.
+    `amplifier` is given by the user.
 
 5. remove the small and big outliers
 
@@ -118,7 +118,7 @@ The process is as followed.
 
 - outlier_bias = 100
 
-- amplfier = 10 
+- amplifier = 10 
 
   > `15` is used in the demo.
 
@@ -131,7 +131,7 @@ The process is as followed.
 
 ### Cropper
 
-The cropper finds the **contours** of regions in the **labeled masks** and crop them.
+The cropper finds the **contours** of regions in the **labeled masks** and crops them.
 
 #### Attributes
 
@@ -166,9 +166,9 @@ Suppose `(h, w) = cropped_mask.shape`.
 
 - max_pixel_ratio: [low, high]
 
-    low < the number of 0 / the number of 255 < high.
+    low < the number of 0s / the number of 255s < high.
 
-    The mask should only have 2 value, 0 and 255.
+    The mask should only have 2 values, 0 and 255.
 
 By default: 
 
@@ -180,6 +180,6 @@ By default:
 
 - `max(h, w) / min(h, w)` = 3.48
 
-- number of `0` / number of `255` = 0.44
+- number of `0s` / number of `255s` = 0.44
 
 So, this image is signed.

demo.py

@@ -1,5 +1,4 @@
 import sys
-
 from signature_detect.cropper import Cropper
 from signature_detect.extractor import Extractor
 from signature_detect.loader import Loader
@@ -8,7 +7,7 @@
 
 def main(file_path: str) -> None:
     loader = Loader()
-    extractor = Extractor(amplfier=15)
+    extractor = Extractor(amplifier=15)
     cropper = Cropper()
     judger = Judger()
 

setup.py

@@ -24,5 +24,5 @@
         "opencv-python",
     ],
     extras_require={"dev": ["coverage>=5.5"]},
-    license = "MIT",
-)
+    license="MIT",
+)

src/signature_detect/cropper.py

@@ -1,8 +1,8 @@
-import math
-from typing import Any
 import cv2
+import math
 import numpy as np
 from PIL import Image
+from typing import Any
 
 
 class Cropper:
@@ -71,7 +71,7 @@ def find_contours(self, img):
           img: numpy array
         Return:
           boxes: A numpy array of contours.
-          each items in the array is a contour (x, y, w, h)
+          each item in the array is a contour (x, y, w, h)
         """
         cnts = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
         cnt = cnts[0] if len(cnts) == 2 else cnts[1]
@@ -86,7 +86,6 @@ def find_contours(self, img):
                 and h < copy_img.shape[0]
                 and w < copy_img.shape[1]
             ):
-
                 # cv2.rectangle(copy_img, (x, y), (x + w, y + h), (155, 155, 0), 1)
                 boxes.append([x, y, w, h])
 
@@ -99,9 +98,9 @@ def find_contours(self, img):
 
         return sorted_boxes
 
-    def is_intersected(self, new_box, orignal_box) -> bool:
+    def is_intersected(self, new_box, original_box) -> bool:
         [x_a, y_a, w_a, h_a] = new_box
-        [x_b, y_b, w_b, h_b] = orignal_box
+        [x_b, y_b, w_b, h_b] = original_box
 
         if y_a > y_b + h_b:
             return False
@@ -188,7 +187,7 @@ def merge_regions_and_masks(self, mask, regions) -> dict:
 
     def run(self, np_image):
         """
-        read the signature extracted by Extractor, and crop it.
+        read the signature extracted by Extractor and crop it.
         """
 
         # find contours

src/signature_detect/extractor.py

@@ -1,16 +1,16 @@
+import numpy as np
 from typing import Any
 from skimage import measure, morphology
 from skimage.measure import regionprops
-import numpy as np
 
 
 class Extractor:
     """
-    Extract the signature from a mask. The process is as followed.
+    Extract the signature from a mask. The process is as follows.
 
     1. It finds the regions in an image mask. Each region has a label (unique number).
     2. It removes the small regions. The small region is defined by attributes.
-    3. It remove the big regions. The big region is defined by attributes.
+    3. It removes the big regions. The big region is defined by attributes.
     4. It returns a labeled image. The numbers in the image are the region labels, NOT pixels.
 
     Attributes
@@ -19,8 +19,8 @@ class Extractor:
         The weight of small outlier size
     outlier_bias: int
         The bias of small outlier size
-    amplfier: int
-        The amplfier calculates the big outlier size from the small one
+    amplifier: int
+        The amplifier calculates the big outlier size from the small one
     min_area_size: int
         The min region area size in the labeled image.
 
@@ -31,22 +31,22 @@ class Extractor:
     """
 
     def __init__(
-        self, outlier_weight=3, outlier_bias=100, amplfier=10, min_area_size=10
+        self, outlier_weight=3, outlier_bias=100, amplifier=10, min_area_size=10
     ):
         # the parameters are used to remove small size connected pixels outlier
         self.outlier_weight = outlier_weight
         self.outlier_bias = outlier_bias
         # the parameter is used to remove big size connected pixels outlier
-        self.amplfier = amplfier
+        self.amplifier = amplifier
         self.min_area_size = min_area_size
 
     def __str__(self) -> str:
         s = "\nExtractor\n==========\n"
         s += "outlier_weight = {}\n".format(self.outlier_weight)
         s += "outlier_bias = {}\n".format(self.outlier_bias)
         s += "> small_outlier_size = outlier_weight * average_region_size + outlier_bias\n"
-        s += "amplfier = {}\n".format(self.amplfier)
-        s += "> large_outlier_size = amplfier * small_outlier_size\n"
+        s += "amplifier = {}\n".format(self.amplifier)
+        s += "> large_outlier_size = amplifier * small_outlier_size\n"
         s += "min_area_size = {} (pixels)\n".format(self.min_area_size)
         s += "> min_area_size is used to calculate average_region_size.\n"
         return s
@@ -69,7 +69,6 @@ def extract(self, mask) -> Any:
 
         total_pixels = 0
         nb_region = 0
-        average = 0.0
         for region in regionprops(labels):
             if region.area > self.min_area_size:
                 total_pixels += region.area
@@ -83,7 +82,7 @@ def extract(self, mask) -> Any:
 
             # big_size_outlier is used as a threshold value to remove pixels
             # are bigger than big_size_outlier
-            big_size_outlier = small_size_outlier * self.amplfier
+            big_size_outlier = small_size_outlier * self.amplifier
 
             # remove small pixels
             labeled_image = morphology.remove_small_objects(labels, small_size_outlier)

src/signature_detect/judger.py

@@ -1,5 +1,5 @@
-from typing import Any
 import numpy as np
+from typing import Any
 
 
 class Judger:
@@ -12,7 +12,7 @@ class Judger:
 
         low < max(h, w) / min(h, w) < high.
 
-        h, w are the heigth and width of the input mask.
+        h, w are the height and width of the input mask.
 
     - max_pixel_ratio: [low, high]
 

src/signature_detect/loader.py

@@ -1,8 +1,7 @@
-from typing import Any
-
 import cv2
 import numpy as np
 import os
+from typing import Any
 from wand.image import Image
 
 

tests/test_cropper.py

@@ -1,16 +1,13 @@
+import numpy as np
 import sys
 import unittest
-
-import numpy as np
-
-sys.path.append("..")
-
 from signature_detect.cropper import Cropper
 from signature_detect.extractor import Extractor
 from signature_detect.loader import Loader
-
 from tests.data.dummy import TEST_IMAGE_PATH
 
+sys.path.append("..")
+
 
 class TestCropper(unittest.TestCase):
     def test_init(self):

tests/test_extractor.py

@@ -1,22 +1,19 @@
+import numpy as np
 import sys
 import unittest
-
-import numpy as np
-
-sys.path.append("..")
-
 from signature_detect.extractor import Extractor
 from signature_detect.loader import Loader
-
 from tests.data.dummy import TEST_IMAGE_PATH
 
+sys.path.append("..")
+
 
 class TestExtractor(unittest.TestCase):
     def test_init(self):
         extractor = Extractor()
         self.assertEqual(extractor.outlier_weight, 3)
         self.assertEqual(extractor.outlier_bias, 100)
-        self.assertEqual(extractor.amplfier, 10)
+        self.assertEqual(extractor.amplifier, 10)
         self.assertEqual(extractor.min_area_size, 10)
 
     def test_str(self):
@@ -25,8 +22,8 @@ def test_str(self):
         s += "outlier_weight = 3\n"
         s += "outlier_bias = 100\n"
         s += "> small_outlier_size = outlier_weight * average_region_size + outlier_bias\n"
-        s += "amplfier = 10\n"
-        s += "> large_outlier_size = amplfier * small_outlier_size\n"
+        s += "amplifier = 10\n"
+        s += "> large_outlier_size = amplifier * small_outlier_size\n"
         s += "min_area_size = 10 (pixels)\n"
         s += "> min_area_size is used to calculate average_region_size.\n"
         self.assertEqual(str(extractor), s)

tests/test_judger.py

@@ -1,17 +1,15 @@
+import numpy as np
 import sys
 import unittest
-
-import numpy as np
-
-sys.path.append("..")
-
 from signature_detect.cropper import Cropper
 from signature_detect.extractor import Extractor
 from signature_detect.loader import Loader
 from signature_detect.judger import Judger
-
 from tests.data.dummy import TEST_IMAGE_PATH
 
+sys.path.append("..")
+
+
 class TestJudger(unittest.TestCase):
     def test_init(self):
         judger = Judger()
@@ -30,7 +28,7 @@ def test_str(self):
     def test_is_valid_mask(self):
         judger = Judger()
 
-        mask = np.array([[0,0,0,0]])
+        mask = np.array([[0, 0, 0, 0]])
         res = judger.judge(mask)
         self.assertFalse(res)
 
@@ -47,7 +45,7 @@ def test_is_valid_mask(self):
     def test_judge(self):
         judger = Judger()
 
-        mask = np.array([[255,0,0,0,0]])
+        mask = np.array([[255, 0, 0, 0, 0]])
         res = judger.judge(mask)
         self.assertFalse(res)
 

tests/test_loader.py

@@ -1,14 +1,11 @@
+import numpy as np
 import sys
 import unittest
-
-import numpy as np
-
-sys.path.append("..")
-
 from signature_detect.loader import Loader
-
 from tests.data.dummy import TEST_IMAGE_PATH, TEST_PDF_PATH, TEST_TIF_PATH
 
+sys.path.append("..")
+
 
 class TestLoader(unittest.TestCase):
     def test_loader_init(self):