V2.2.7 (#7)

* chore: added ImagePlot util

* chore: update version

* release: v2.2.5

* release: v2.2.7

calango/__init__.py

@@ -26,5 +26,5 @@
 from .devices import Mouse
 from .media import Image, VideoWriter, Video
 
-VERSION = "2.2.6.final.0"
+VERSION = "2.2.7.final.0"
 __version__ = get_version_pep440_compliant(VERSION)

calango/media.py

@@ -258,6 +258,16 @@ def gray_scale(self) -> Image:
             return Image(cv2.cvtColor(self, code_))
         return self
 
+    def gray_to_bgr(self) -> Image:
+        self[:, ] = cv2.cvtColor(self, cv2.COLOR_GRAY2BGR)
+        self._color_mode = "BGR"
+        return self
+
+    def gray_to_rgb(self) -> Image:
+        self[:, ] = cv2.cvtColor(self, cv2.COLOR_GRAY2RGB)
+        self._color_mode = "RGB"
+        return self
+
     def rgb_to_bgr(self) -> Image:
         if self._color_mode == 'RGB':
             self._color_mode = 'BGR'
@@ -307,6 +317,24 @@ def crop_by_center(self, size=None, keep_scale=False) -> Image:
 
         return Image(cv2.resize(im_crop[start[1]:end[1], start[0]:end[0]], (new_w, new_h)))
 
+    @staticmethod
+    def remove_shadow(image):
+        if image.shape[-1] > 1:
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # 2. Apply bilateral filter to smooth the image
+        bilateral = cv2.bilateralFilter(image, 15, 75, 75)
+
+        # 3. Compute the background using morphological closing
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 21))
+        background = cv2.morphologyEx(bilateral, cv2.MORPH_CLOSE, kernel)
+
+        # 4. Compute the ratio of the grayscale and background image to remove shadows
+        ratio = (image.astype('float') / background.astype('float')) * 255.0
+        shadow_removed = np.clip(ratio, 0, 255).astype('uint8')
+
+        return shadow_removed
+
     def prune(self) -> Image:
         min_len = self.min_len
         return Image(self.crop_by_center((min_len, min_len)))

calango/utils.py

@@ -1,6 +1,8 @@
 import base64
 import io
 from .settings import ON_COLAB_JUPYTER
+from matplotlib import pyplot as plt
+import math
 
 __all__ = ['show_local_mp4']
 
@@ -17,3 +19,30 @@ def show_local_mp4(file_name, width=640, height=480):
                     width, height, video_encoded.decode("ascii")
             )
     )
+
+
+class ImagePlot:
+    def __init__(self, fig_size=(13, 13), max_cols=4):
+        self.fig_size = fig_size
+        self.figure = plt.figure(figsize=fig_size)  # TODO: does it need to be global?
+        self._max_cols = max_cols
+        self._total_images = 0
+
+    @property
+    def current_row(self):
+        return math.ceil(self._total_images / self._max_cols)
+
+    @property
+    def current_col(self):
+        return ((self._total_images - 1) % self._max_cols) + 1
+
+    def plot(self, image, title=None, color='gray'):
+        try:
+            self._total_images += 1
+            plt.subplot(self._max_cols, self._max_cols, self._total_images)
+            plt.title(self.current_col if title is None else title)
+            plt.xticks([]), plt.yticks([])
+            plt.imshow(image.copy(), color)
+        except Exception as err:
+            self._total_images -= 1  # garante consistencia da quantidade de imagens
+            raise err

requirements.txt

@@ -1,6 +1,4 @@
 cereja
-pyautogui
 opencv-python
 matplotlib
-numpy
-mss
+numpy