Merge pull request #23 from jonathanrocher/ENH/add_metadata_to_script

Enh: add metadata collection to stage 0 script

Author: jonathanrocher

README.md

@@ -20,11 +20,12 @@ scalable applications, and package them into an installer.
 ### Requirements
 
 - Python 3.6+
+- scikits.image
+- Pillow
 - Pandas
 - matplotlib
 - traits
 - traitsui
-- scikits.image
 
 ### EDM users (recommended)
 First, download and install EDM from https://www.enthought.com/edm/. Then, run 
@@ -33,7 +34,7 @@ Python environment and install all dependencies in it:
 ```commandline
 edm env create ets_tutorial
 edm shell -e ets_tutorial
-edm install pandas matplotlib traits traitsui scikits.image
+edm install pandas matplotlib traits traitsui scikits.image pillow pyqt5 ipython
 ```
 
 ### Conda users
@@ -43,14 +44,15 @@ edm install pandas matplotlib traits traitsui scikits.image
 Assuming a Python environment is created and activated on your machine, for 
 example from https://www.python.org/, 
 ```commandline
-pip install pandas matplotlib traits traitsui scikits-image
+pip install pandas matplotlib traits traitsui scikits-image pillow pyqt5 ipython
 ```
 
 ## Getting help
 ### During the tutorial
 During the tutorial, don't hesitate to ask for help: 
 - ask questions if something isn't clear,
-- there will be a number of developers in the room who can help unblock you.
+- or just call out for individual support: there will be a number of developers 
+  in the room who can help unblock you.
 
 ### After the tutorial
 - This tutorial was recorded and can be watched [here]() [TODO]

sample_images/IMG-0311_xmas_2020.JPG

@@ -1,22 +1,34 @@
+""" Script analyzing an image, detecting human faces inside it, and printing
+EXIF data about it.
+"""
+import PIL.Image
+from PIL.ExifTags import TAGS
 from skimage import data
 from skimage.feature import Cascade
-
 import matplotlib.pyplot as plt
 from matplotlib import patches
+import sys
+from os.path import join
 
 # Load the trained file from the module root.
 trained_file = data.lbp_frontal_face_cascade_filename()
 
 # Initialize the detector cascade.
 detector = Cascade(trained_file)
 
-img = data.astronaut()
+if len(sys.argv) > 1:
+    image_path = sys.argv[1]
+else:
+    image_path = join("..", "sample_images", "IMG-0311_xmas_2020.JPG")
+    # image_path = join("..", "sample_images", "owls.jpg")
+
+img = plt.imread(image_path)
 
 detected = detector.detect_multi_scale(img=img,
                                        scale_factor=1.2,
                                        step_ratio=1,
                                        min_size=(60, 60),
-                                       max_size=(123, 123))
+                                       max_size=(600, 600))
 
 plt.imshow(img)
 img_desc = plt.gca()
@@ -35,4 +47,11 @@
         )
     )
 
+img = PIL.Image.open(image_path)
+
+data = {TAGS[k]: v for k, v in img._getexif().items() if k in TAGS}
+data["Number of faces detected"] = len(detected)
+
+print(data)
+
 plt.show()