CLI working, opencv now >3

MANIFEST.in

@@ -1 +1,2 @@
 include README.md LICENSE
+include autocrop/*

autocrop.egg-info/PKG-INFO

@@ -0,0 +1,79 @@
+Metadata-Version: 1.1
+Name: autocrop
+Version: 0.1
+Summary: Automatically crops faces from batches of pictures
+Home-page: https://github.com/leblancfg/autocrop
+Author: Francois Leblanc
+Author-email: [email protected]
+License: MIT
+Description: 
+        # autocrop
+
+        ![obama_crop](https://cloud.githubusercontent.com/assets/15659410/10975709/3e38de48-83b6-11e5-8885-d95da758ca17.png)
+
+        Basic script using openCV, that automatically detects and crops faces from batches of photos.
+
+        Perfect for batch work for ID cards or profile pictures, will output 500px wide square images, centered around the biggest face detected. It can also add a touch of auto gamma correction.
+
+        ## Installation
+        Simple!
+
+        The script will process all `.jpg` files in the `/photos` directory. The cropped files are placed in `photos/crop`, and originals are moved to `photos/bkp`.
+
+        If it can't find a face in the picture, it'll simply leave it in `/photos`.
+
+        ### conda
+        The easiest way to run *autocrop* is to use the [Anaconda Python distribution](https://www.anaconda.com/download/) and run the following:
+
+            git clone https://github.com/leblancfg/autocrop
+            conda install --channel conda-forge --file requirements.txt
+
+        Move your pictures to be cropped in the *photos* directory, then run the script with:
+
+            cd autocrop
+            python autocrop.py
+
+        If running on Windows, this is by far the sanest way to approach this problem. Also, installing Anaconda doesn't require admin privileges on Windows.
+
+        ### pip
+        Otherwise, binaries for the Python-only bindings for OpenCV have recently been made available through pip, which makes installation a breeze.
+
+            git clone https://github.com/leblancfg/autocrop
+            pip install numpy opencv-python
+
+        Move your pictures to be cropped in the *photos* directory, then run the script with:
+
+            cd autocrop
+            python autocrop.py
+
+        ## Versions
+        The script works on openCV 2.4.9 and python 2.7+ and 3+. It has not been tested otherwise. For now, it also artificially restricts filetype as jpg and output size as 500px. These values can easily be tweaked in the header in `autocrop.py`.
+
+        ## More Info
+        Check out:
+        * http://docs.opencv.org/master/d7/d8b/tutorial_py_face_detection.html#gsc.tab=0
+        * http://docs.opencv.org/master/d5/daf/tutorial_py_histogram_equalization.html#gsc.tab=0
+
+        Adapted from:
+        * http://photo.stackexchange.com/questions/60411/how-can-i-batch-crop-based-on-face-location
+
+        ## TODO
+        Pull requests welcome! I don't see major feature additions in the future, but proper 
+        * [ ] Create PyPI and conda-forge packages so that it can be directly pip- or conda-installable.
+        * [ ] Split off into smaller functions, and write unit tests.
+        * [ ] Handle input filetypes for `*.bmp`, `*.dib`, `*.jp2`, `*.png`, `*.webp`, `*.pbm`, `*.pgm`, `*.ppm`, `*.sr`, `*.ras`, `*.tiff`, `*.tif`.
+        * [ ] Handle output image size.
+        * [ ] Handle CLI input: `$ autocrop [-w width] [-h height] [-i input-folder] [-o output-folder] [--passport=<country>]`
+
+Platform: UNKNOWN
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2.6
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.3
+Classifier: Programming Language :: Python :: 3.4
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy

autocrop.egg-info/SOURCES.txt

@@ -0,0 +1,17 @@
+LICENSE
+MANIFEST.in
+README.md
+setup.cfg
+setup.py
+autocrop/.__init__.py.swp
+autocrop/.autocrop.py.swp
+autocrop/__init__.py
+autocrop/__version__.py
+autocrop/autocrop.py
+autocrop/haarcascade_frontalface_default.xml
+autocrop.egg-info/PKG-INFO
+autocrop.egg-info/SOURCES.txt
+autocrop.egg-info/dependency_links.txt
+autocrop.egg-info/entry_points.txt
+autocrop.egg-info/requires.txt
+autocrop.egg-info/top_level.txt

autocrop.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

autocrop.egg-info/entry_points.txt

@@ -0,0 +1,3 @@
+[console_scripts]
+autocrop = autocrop:cli
+

autocrop.egg-info/requires.txt

@@ -0,0 +1,2 @@
+numpy
+opencv-python

autocrop.egg-info/top_level.txt

@@ -0,0 +1 @@
+autocrop

autocrop/__init__.py

@@ -1,3 +1,4 @@
+import argparse
 import os
 import sys
 
@@ -13,3 +14,4 @@
 
 if __name__ == '__main__':
     cli()
+

autocrop/__version__.py

@@ -1 +1,4 @@
+__title__ = 'autocrop'
+__description__ = 'Automatically crops faces from batches of pictures'
+__author__ = 'François Leblanc'
 __version__ = '0.1'

autocrop/autocrop.py

@@ -1,23 +1,30 @@
 from __future__ import print_function
 
+import argparse
 from contextlib import contextmanager
 import cv2
 import glob
 import numpy as np
-import argparse
 import os
 import shutil
+import sys
+
+from .__version__ import __title__, __description__, __author__, __version__
+
 
 # Internal variables
-errors = 0
 fixexp = True                 # Flag to fix underexposition
 marker = False                # Flag for gamma correct
 INPUT_FILETYPES = ['*.jpg', '*.jpeg']
 INCREMENT = 0.06
 GAMMA_THRES = 0.001 
 GAMMA = 0.90
 FACE_RATIO = 6
-cascPath = 'haarcascade_frontalface_default.xml'
+
+# Load XML Resource
+cascFile= 'haarcascade_frontalface_default.xml'
+d = os.path.dirname(sys.modules['autocrop'].__file__)
+cascPath = os.path.join(d, cascFile)
 
 # Define directory change within context
 @contextmanager
@@ -34,11 +41,16 @@ def gamma(img, correction):
     img = cv2.pow(img/255.0, correction)
     return np.uint8(img*255)
 
-def main():
+def main(path, fheight, fwidth):
+    """Given path containing image files to process, will
+    1) copy them to `path/bkp`, and 
+    2) create face-cropped versions and place them in `path/crop`
+    """
+    errors = 0
     # Create the haar cascade
     faceCascade = cv2.CascadeClassifier(cascPath)
 
-    with cd('../photos/'):
+    with cd(path):
         files_grabbed = []
         for files in INPUT_FILETYPES:
             files_grabbed.extend(glob.glob(files))
@@ -52,13 +64,12 @@ def main():
             minface = int(np.sqrt(height*height + width*width) / 8)
 
             # ====== Detect faces in the image ======
-            faces = [[]]
             faces = faceCascade.detectMultiScale(
                 gray,
                 scaleFactor=1.1,
                 minNeighbors=5,
                 minSize=(minface, minface),
-                flags = cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT | cv2.cv.CV_HAAR_DO_ROUGH_SEARCH
+                flags = cv2.CASCADE_FIND_BIGGEST_OBJECT | cv2.CASCADE_DO_ROUGH_SEARCH
             )
 
             # Handle no faces
@@ -82,7 +93,11 @@ def main():
                     break
 
             # Crop the image from the original
-            image = image[y-2*pad:y+h+pad, x-1.5*pad:x+w+1.5*pad]
+            h1 = int(x-1.5*pad) 
+            h2 = int(x+w+1.5*pad)
+            v1 = int(y-2*pad)
+            v2 = int(y+h+pad)
+            image = image[v1:v2, h1:h2]
 
             # Resize the damn thing
             image = cv2.resize(image, (fheight, fwidth), interpolation = cv2.INTER_AREA)
@@ -108,14 +123,12 @@ def main():
 
 def cli():
     parser = argparse.ArgumentParser(description='Automatically crops faces from batches of pictures')
-    parser.add_argument('-w', '--width', default=500, help='Width of the cropped files in pixels')
-    parser.add_argument('-h', '--height', default=500, help='Height of the cropped files in pixels')
-    args = parser.parse_args()
-    fwidth = args.width
-    fheight = args.height
+    parser.add_argument('-p', '--path', default='photos', help='Path where images to crop are located')
+    parser.add_argument('-w', '--width', type=int, default=500, help='Width of the cropped files in pixels')
+    parser.add_argument('-H', '--height', type=int, default=500, help='Height of the cropped files in pixels')
 
-    # if len(args) != 1:
-    #     parser.error('wrong number of arguments')
+    args = parser.parse_args()
+    print('Processing images in folder:', path)
 
-    main()
+    main(args.path, args.height, args.width)
 

build/lib/autocrop/__init__.py

@@ -0,0 +1,17 @@
+import argparse
+import os
+import sys
+
+# Inject vendored directory into system path.
+v_path = os.path.abspath(os.path.sep.join([os.path.dirname(os.path.realpath(__file__)), 'vendor']))
+sys.path.insert(0, v_path)
+
+# Inject patched directory into system path.
+v_path = os.path.abspath(os.path.sep.join([os.path.dirname(os.path.realpath(__file__)), 'patched']))
+sys.path.insert(0, v_path)
+
+from .autocrop import cli
+
+if __name__ == '__main__':
+    cli()
+

build/lib/autocrop/__version__.py

@@ -0,0 +1,4 @@
+__title__ = 'autocrop'
+__description__ = 'Automatically crops faces from batches of pictures'
+__author__ = 'François Leblanc'
+__version__ = '0.1'

build/lib/autocrop/autocrop.py

@@ -0,0 +1,134 @@
+from __future__ import print_function
+
+import argparse
+from contextlib import contextmanager
+import cv2
+import glob
+import numpy as np
+import os
+import shutil
+import sys
+
+from .__version__ import __title__, __description__, __author__, __version__
+
+
+# Internal variables
+fixexp = True                 # Flag to fix underexposition
+marker = False                # Flag for gamma correct
+INPUT_FILETYPES = ['*.jpg', '*.jpeg']
+INCREMENT = 0.06
+GAMMA_THRES = 0.001 
+GAMMA = 0.90
+FACE_RATIO = 6
+
+# Load XML Resource
+cascFile= 'haarcascade_frontalface_default.xml'
+d = os.path.dirname(sys.modules['autocrop'].__file__)
+cascPath = os.path.join(d, cascFile)
+
+# Define directory change within context
+@contextmanager
+def cd(newdir):
+    prevdir = os.getcwd()
+    os.chdir(os.path.expanduser(newdir))
+    try:
+        yield
+    finally:
+        os.chdir(prevdir)
+
+# Define simple gamma correction fn
+def gamma(img, correction):
+    img = cv2.pow(img/255.0, correction)
+    return np.uint8(img*255)
+
+def main(path, fheight, fwidth):
+    """Given path containing image files to process, will
+    1) copy them to `path/bkp`, and 
+    2) create face-cropped versions and place them in `path/crop`
+    """
+    errors = 0
+    # Create the haar cascade
+    faceCascade = cv2.CascadeClassifier(cascPath)
+
+    with cd(path):
+        files_grabbed = []
+        for files in INPUT_FILETYPES:
+            files_grabbed.extend(glob.glob(files))
+
+        for file in files_grabbed:
+            image = cv2.imread(file)
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+            # Scale the image
+            height, width = (image.shape[:2])
+            minface = int(np.sqrt(height*height + width*width) / 8)
+
+            # ====== Detect faces in the image ======
+            faces = faceCascade.detectMultiScale(
+                gray,
+                scaleFactor=1.1,
+                minNeighbors=5,
+                minSize=(minface, minface),
+                flags = cv2.CASCADE_FIND_BIGGEST_OBJECT | cv2.CASCADE_DO_ROUGH_SEARCH
+            )
+
+            # Handle no faces
+            if len(faces) == 0: 
+                print(' No faces can be detected in file {0}.'.format(str(file)))
+                errors += 1
+                break
+
+            # Copy to /bkp
+            shutil.copy(file, 'bkp')
+
+            # Make padding from probable biggest face
+            x, y, w, h = faces[-1]
+            pad = h / FACE_RATIO
+
+            # Make sure padding is contained within picture
+            while True:  # decreases pad by 6% increments to fit crop into image. Can lead to very small faces.
+                if y-2*pad < 0 or y+h+pad > height or int(x-1.5*pad) < 0 or x+w+int(1.5*pad) > width:
+                    pad = (1 - INCREMENT) * pad
+                else:
+                    break
+
+            # Crop the image from the original
+            h1 = int(x-1.5*pad) 
+            h2 = int(x+w+1.5*pad)
+            v1 = int(y-2*pad)
+            v2 = int(y+h+pad)
+            image = image[v1:v2, h1:h2]
+
+            # Resize the damn thing
+            image = cv2.resize(image, (fheight, fwidth), interpolation = cv2.INTER_AREA)
+
+            # ====== Dealing with underexposition ======
+            if fixexp == True:
+                # Check if under-exposed
+                uexp = cv2.calcHist([gray], [0], None, [256], [0,256])
+
+                if sum(uexp[-26:]) < GAMMA_THRES * sum(uexp) :
+                    marker = True
+                    image = gamma(image, GAMMA)
+
+            # Write cropfile
+            cropfilename = '{0}'.format(str(file))
+            cv2.imwrite(cropfilename, image)
+
+            # Move files to /crop
+            shutil.move(cropfilename, 'crop')
+
+    # Stop and print timer
+    print(' {0} files have been cropped'.format(len(files_grabbed) - errors))
+
+def cli():
+    parser = argparse.ArgumentParser(description='Automatically crops faces from batches of pictures')
+    parser.add_argument('-p', '--path', default='photos', help='Path where images to crop are located')
+    parser.add_argument('-w', '--width', type=int, default=500, help='Width of the cropped files in pixels')
+    parser.add_argument('-H', '--height', type=int, default=500, help='Height of the cropped files in pixels')
+
+    args = parser.parse_args()
+    print('Processing images in folder:', path)
+
+    main(args.path, args.height, args.width)
+