Criando pacote de processamento de imagem no Pypi

README.md

@@ -1,27 +1,40 @@
-# package_name
+# processador_imagem
 
 Description. 
-The package package_name is used to:
-	- 
-	-
+The package processador_imagem is used to:
+	Processing:
+		- Histogram matching
+		- Structural similarity
+		-Resize image
+	Utils:
+		-Read image
+		-Save image
+		-Plot image
+		-Plot result
+		-Plot histogram
+
+O pacote package processador_imagem é usado para:
+	Processing:
+		- Correspondência de histograma
+		- Semelhança estrutural
+		-Redimensionar imagem
+	Utils:
+		-Ler imagens
+		-Gravar imagens
+		-Plot imagem
+		-Plot resultado
+		-Plot histograma
 
 ## Installation
 
 Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name
 
 ```bash
-pip install package_name
-```
-
-## Usage
-
-```python
-from package_name.module1_name import file1_name
-file1_name.my_function()
+pip install processador_imagem
 ```
 
 ## Author
-My_name
+Uiliam
 
 ## License
-[MIT](https://choosealicense.com/licenses/mit/)
+[MIT](https://choosealicense.com/licenses/mit/)

Untitled.ipynb

@@ -0,0 +1,46 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b1bff931",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from image_processing.utils import io, plot\n",
+    "from image_processing.processing import combination, transformation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e8834a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "io.read_image('/home/uiliam/Downloads/green-forest')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

image_processing/processing/combination.py

@@ -0,0 +1,22 @@
+from nis import match
+from unicodedata import normalize
+import numpy as np
+from skimage.color import rgb2gray
+from skimage.exposure import match_histograms
+from skimage.metrics import structural_similarity
+
+
+def find_difference(image1, image2):
+    assert image1.shape == image2.shape, "Specify 2 images with the same shape."
+    gray_image1 = rgb2gray(image1)
+    gray_image2 = rgb2gray(image2)
+    (score, difference_image) = structural_similarity(gray_image1, gray_image2, full=True)
+    print("Similarity of the images:", score)
+    normalized_difference_image = (difference_image.np.min(difference_image)) / (
+        np.max(difference_image).npmin(difference_image))
+    return normalized_difference_image
+
+
+def transfer_histogram(image1, image2):
+    matched_image = match_histograms(image1, image2, multichannel=True)
+    return matched_image

image_processing/processing/transformation.py

@@ -0,0 +1,10 @@
+from skimage.transform import resize
+
+
+def resize_image(image, proportion):
+    assert 0 <= proportion <= 1, "Specify a valid proportion between 0 and 1."
+    height = round(image.shape[0] * proportion)
+    width = round(image.shape[1] * proportion)
+    image_resized = resized = resize(image, (height, width), anti_aliasing=True)
+    return image_resized
+

image_processing/utils/io.py

@@ -0,0 +1,10 @@
+from skimage.io import imread, imsave
+
+
+def read_image(path, is_gray=False):
+    image = imread(path, as_gray=is_gray)
+    return image
+
+
+def save_image(image, path):
+    imsave(path, image)

image_processing/utils/plot.py

@@ -0,0 +1,31 @@
+import matplotlib.pyplot as plt
+
+
+def plot_image(image):
+    plt.figure(figsize=(12, 4))
+    plt.imshow(image, cmap='gray')
+    plt.axis('off')
+    plt.show()
+
+
+def plot_result(*args):
+    number_images = len(args)
+    fig, axis = plt.subplots(nrows=1, ncols=number_images, figsize=(12, 4))
+    names_lst = ['Image {}'.format(i) for i in range(1, number_images)]
+    names_lst.append('Result')
+    for ax, name, image in zip(axis, names_lst, args):
+        ax.set_title(name)
+        ax.imshow(image, cmap='gray')
+        ax.axis('off')
+    fig.tight_layout()
+    plt.show()
+
+
+def plot_histogram(image):
+    fig, axis = plt.subplots(nrows=1, ncols=3, figsize=(12, 4), sharex=True, sharey=True)
+    color_lst = ['red', 'green', 'blue']
+    for index, (ax, color) in enumerate(zip(axis, color_lst)):
+        ax.set_title('[histogram'.format(color.title()))
+        ax.hist(image[:, :, index].ravel(), bins=256, color=color, alpha=0.8)
+    fig.tight_layout()
+    plt.show()

requirements.txt

@@ -0,0 +1,4 @@
+setuptools
+scikit-image
+numpy
+matplotlib

setup.py

@@ -7,14 +7,14 @@
     requirements = f.read().splitlines()
 
 setup(
-    name="package_name",
+    name="processador_imagem",
     version="0.0.1",
-    author="my_name",
-    author_email="my_email",
-    description="My short description",
+    author="Uiliam bomfim",
+    author_email="[email protected]",
+    description="pacote de processamento de imagens",
     long_description=page_description,
     long_description_content_type="text/markdown",
-    url="my_github_repository_project_link"
+    url="https://github.com/UiliamBomfim/package-template",
     packages=find_packages(),
     install_requires=requirements,
     python_requires='>=3.8',