Revert "replace usages of `imutils` with opencv equivalents"

src/eynollah/utils/__init__.py

@@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from shapely import geometry
 import cv2
+import imutils
 from scipy.signal import find_peaks
 from scipy.ndimage import gaussian_filter1d
 import time

src/eynollah/utils/rotate.py

@@ -1,5 +1,6 @@
 import math
-import numpy as np
+
+import imutils
 import cv2
 
 def rotatedRectWithMaxArea(w, h, angle):
@@ -10,11 +11,11 @@ def rotatedRectWithMaxArea(w, h, angle):
     side_long, side_short = (w, h) if width_is_longer else (h, w)
 
     # since the solutions for angle, -angle and 180-angle are all the same,
-    # it suffices to look at the first quadrant and the absolute values of sin,cos:
+    # if suffices to look at the first quadrant and the absolute values of sin,cos:
     sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
     if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
-        # half constrained case: two crop corners touch the longer side, 
-        # the other two corners are on the mid-line parallel to the longer line
+        # half constrained case: two crop corners touch the longer side,
+        #   the other two corners are on the mid-line parallel to the longer line
         x = 0.5 * side_short
         wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
     else:
@@ -24,45 +25,6 @@ def rotatedRectWithMaxArea(w, h, angle):
 
     return wr, hr
 
-
-def rotate_image_opencv(image, angle):
-    # Calculate the original image dimensions (h, w) and the center point (cx, cy)
-    h, w = image.shape[:2]
-    cx, cy = (w // 2, h // 2)
-
-    # Compute the rotation matrix
-    M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0)
-
-    # Calculate the new bounding box
-    corners = np.array([
-        [0, 0],
-        [w, 0],
-        [w, h],
-        [0, h]
-    ])
-
-    # Apply rotation matrix to the corner points
-    ones = np.ones(shape=(len(corners), 1))
-    corners_ones = np.hstack([corners, ones])
-    transformed_corners = M @ corners_ones.T
-    transformed_corners = transformed_corners.T
-
-    # Calculate the new bounding box dimensions
-    min_x, min_y = np.min(transformed_corners, axis=0)
-    max_x, max_y = np.max(transformed_corners, axis=0)
-
-    newW = int(np.ceil(max_x - min_x))
-    newH = int(np.ceil(max_y - min_y))
-
-    # Adjust the rotation matrix to account for translation
-    M[0, 2] += (newW / 2) - cx
-    M[1, 2] += (newH / 2) - cy
-
-    # Perform the affine transformation (rotation)
-    rotated_image = cv2.warpAffine(image, M, (newW, newH))
-
-    return rotated_image
-
 def rotate_max_area_new(image, rotated, angle):
     wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
     h, w, _ = rotated.shape
@@ -73,7 +35,7 @@ def rotate_max_area_new(image, rotated, angle):
     return rotated[y1:y2, x1:x2]
 
 def rotation_image_new(img, thetha):
-    rotated = rotate_image_opencv(img, thetha)
+    rotated = imutils.rotate(img, thetha)
     return rotate_max_area_new(img, rotated, thetha)
 
 def rotate_image(img_patch, slope):
@@ -82,10 +44,13 @@ def rotate_image(img_patch, slope):
     M = cv2.getRotationMatrix2D(center, slope, 1.0)
     return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
 
-def rotate_image_different(img, slope):
+def rotate_image_different( img, slope):
+    # img = cv2.imread('images/input.jpg')
     num_rows, num_cols = img.shape[:2]
+
     rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
-    return cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
+    img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
+    return img_rotation
 
 def rotate_max_area(image, rotated, rotated_textline, rotated_layout, rotated_table_prediction, angle):
     wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
@@ -97,17 +62,17 @@ def rotate_max_area(image, rotated, rotated_textline, rotated_layout, rotated_ta
     return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_table_prediction[y1:y2, x1:x2]
 
 def rotation_not_90_func(img, textline, text_regions_p_1, table_prediction, thetha):
-    rotated = rotate_image_opencv(img, thetha)
-    rotated_textline = rotate_image_opencv(textline, thetha)
-    rotated_layout = rotate_image_opencv(text_regions_p_1, thetha)
-    rotated_table_prediction = rotate_image_opencv(table_prediction, thetha)
+    rotated = imutils.rotate(img, thetha)
+    rotated_textline = imutils.rotate(textline, thetha)
+    rotated_layout = imutils.rotate(text_regions_p_1, thetha)
+    rotated_table_prediction = imutils.rotate(table_prediction, thetha)
     return rotate_max_area(img, rotated, rotated_textline, rotated_layout, rotated_table_prediction, thetha)
 
 def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha):
-    rotated = rotate_image_opencv(img, thetha)
-    rotated_textline = rotate_image_opencv(textline, thetha)
-    rotated_layout = rotate_image_opencv(text_regions_p_1, thetha)
-    rotated_layout_full = rotate_image_opencv(text_regions_p_fully, thetha)
+    rotated = imutils.rotate(img, thetha)
+    rotated_textline = imutils.rotate(textline, thetha)
+    rotated_layout = imutils.rotate(text_regions_p_1, thetha)
+    rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha)
     return rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
 
 def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
@@ -118,3 +83,4 @@ def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout
     x1 = w // 2 - int(wr / 2)
     x2 = x1 + int(wr)
     return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2]
+