Add basic image io and remove python C-API refs from numpy_returns.cpp

python_examples/cnn_face_detector.py

@@ -33,14 +33,12 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html.
+#       pip install numpy
 
 import sys
 import dlib
-from skimage import io
 
 if len(sys.argv) < 3:
     print(
@@ -55,7 +53,7 @@
 
 for f in sys.argv[2:]:
     print("Processing file: {}".format(f))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
     # The 1 in the second argument indicates that we should upsample the image
     # 1 time.  This will make everything bigger and allow us to detect more
     # faces.

python_examples/correlation_tracker.py

@@ -32,16 +32,14 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
+#       pip install numpy
 
 import os
 import glob
 
 import dlib
-from skimage import io
 
 # Path to the video frames
 video_folder = os.path.join("..", "examples", "video_frames")
@@ -54,7 +52,7 @@
 # We will track the frames as we load them off of disk
 for k, f in enumerate(sorted(glob.glob(os.path.join(video_folder, "*.jpg")))):
     print("Processing Frame {}".format(k))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
 
     # We need to initialize the tracker on the first frame
     if k == 0:

python_examples/face_alignment.py

@@ -21,16 +21,13 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires OpenCV and Numpy which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install opencv-python numpy
-#   Or downloaded from http://opencv.org/releases.html
+#       pip install numpy
 
 import sys
 
 import dlib
-import cv2
-import numpy as np
 
 if len(sys.argv) != 3:
     print(
@@ -48,14 +45,8 @@
 detector = dlib.get_frontal_face_detector()
 sp = dlib.shape_predictor(predictor_path)
 
-# Load the image using OpenCV
-bgr_img = cv2.imread(face_file_path)
-if bgr_img is None:
-    print("Sorry, we could not load '{}' as an image".format(face_file_path))
-    exit()
-
-# Convert to RGB since dlib uses RGB images
-img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
+# Load the image using Dlib
+img = dlib.load_rgb_image(face_file_path)
 
 # Ask the detector to find the bounding boxes of each face. The 1 in the
 # second argument indicates that we should upsample the image 1 time. This
@@ -72,20 +63,17 @@
 for detection in dets:
     faces.append(sp(img, detection))
 
+window = dlib.image_window()
+
 # Get the aligned face images
 # Optionally: 
 # images = dlib.get_face_chips(img, faces, size=160, padding=0.25)
 images = dlib.get_face_chips(img, faces, size=320)
 for image in images:
-    cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    cv2.imshow('image',cv_bgr_img)
-    cv2.waitKey(0)
+    window.set_image(image)
+    dlib.hit_enter_to_continue()
 
 # It is also possible to get a single chip
 image = dlib.get_face_chip(img, faces[0])
-cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-cv2.imshow('image',cv_bgr_img)
-cv2.waitKey(0)
-
-cv2.destroyAllWindows()
-
+window.set_image(image)
+dlib.hit_enter_to_continue()

python_examples/face_clustering.py

@@ -28,16 +28,14 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
+#       pip install numpy
 
 import sys
 import os
 import dlib
 import glob
-from skimage import io
 
 if len(sys.argv) != 5:
     print(
@@ -66,7 +64,7 @@
 # Now find all the faces and compute 128D face descriptors for each face.
 for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
     print("Processing file: {}".format(f))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
 
     # Ask the detector to find the bounding boxes of each face. The 1 in the
     # second argument indicates that we should upsample the image 1 time. This

python_examples/face_detector.py

@@ -37,23 +37,20 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
+#       pip install numpy
 
 import sys
 
 import dlib
-from skimage import io
-
 
 detector = dlib.get_frontal_face_detector()
 win = dlib.image_window()
 
 for f in sys.argv[1:]:
     print("Processing file: {}".format(f))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
     # The 1 in the second argument indicates that we should upsample the image
     # 1 time.  This will make everything bigger and allow us to detect more
     # faces.
@@ -76,7 +73,7 @@
 # Also, the idx tells you which of the face sub-detectors matched.  This can be
 # used to broadly identify faces in different orientations.
 if (len(sys.argv[1:]) > 0):
-    img = io.imread(sys.argv[1])
+    img = dlib.load_rgb_image(sys.argv[1])
     dets, scores, idx = detector.run(img, 1, -1)
     for i, d in enumerate(dets):
         print("Detection {}, score: {}, face_type:{}".format(

python_examples/face_jitter.py

@@ -25,26 +25,23 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires OpenCV and Numpy which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install opencv-python numpy
+#       pip install numpy
 #
 #   The image file used in this example is in the public domain:
 #   https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg
 import sys
 
 import dlib
-import cv2
-import numpy as np
 
-def show_jittered_images(jittered_images):
+def show_jittered_images(window, jittered_images):
     '''
         Shows the specified jittered images one by one
     '''
     for img in jittered_images:
-        cv_bgr_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-        cv2.imshow('image',cv_bgr_img)
-        cv2.waitKey(0)
+        window.set_image(img)
+        dlib.hit_enter_to_continue()
 
 if len(sys.argv) != 2:
     print(
@@ -62,14 +59,8 @@ def show_jittered_images(jittered_images):
 detector = dlib.get_frontal_face_detector()
 sp = dlib.shape_predictor(predictor_path)
 
-# Load the image using OpenCV
-bgr_img = cv2.imread(face_file_path)
-if bgr_img is None:
-    print("Sorry, we could not load '{}' as an image".format(face_file_path))
-    exit()
-
-# Convert to RGB since dlib uses RGB images
-img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
+# Load the image using dlib
+img = dlib.load_rgb_image(face_file_path)
 
 # Ask the detector to find the bounding boxes of each face.
 dets = detector(img)
@@ -83,15 +74,14 @@ def show_jittered_images(jittered_images):
 
 # Get the aligned face image and show it
 image = dlib.get_face_chip(img, faces[0], size=320)
-cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-cv2.imshow('image',cv_bgr_img)
-cv2.waitKey(0)
+window = dlib.image_window()
+window.set_image(image)
+dlib.hit_enter_to_continue()
 
 # Show 5 jittered images without data augmentation
 jittered_images = dlib.jitter_image(image, num_jitters=5)
-show_jittered_images(jittered_images)
+show_jittered_images(window, jittered_images)
 
 # Show 5 jittered images with data augmentation
 jittered_images = dlib.jitter_image(image, num_jitters=5, disturb_colors=True)
-show_jittered_images(jittered_images)
-cv2.destroyAllWindows()
+show_jittered_images(window, jittered_images)

python_examples/face_landmark_detection.py

@@ -45,16 +45,14 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
+#       pip install numpy
 
 import sys
 import os
 import dlib
 import glob
-from skimage import io
 
 if len(sys.argv) != 3:
     print(
@@ -76,7 +74,7 @@
 
 for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
     print("Processing file: {}".format(f))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
 
     win.clear_overlay()
     win.set_image(img)

python_examples/face_recognition.py

@@ -40,16 +40,14 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
+#       pip install numpy
 
 import sys
 import os
 import dlib
 import glob
-from skimage import io
 
 if len(sys.argv) != 4:
     print(
@@ -76,7 +74,7 @@
 # Now process all the images
 for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
     print("Processing file: {}".format(f))
-    img = io.imread(f)
+    img = dlib.load_rgb_image(f)
 
     win.clear_overlay()
     win.set_image(img)

python_examples/find_candidate_object_locations.py

@@ -31,18 +31,14 @@
 #   command:
 #       sudo apt-get install cmake
 #
-#   Also note that this example requires scikit-image which can be installed
+#   Also note that this example requires Numpy which can be installed
 #   via the command:
-#       pip install scikit-image
-#   Or downloaded from http://scikit-image.org/download.html. 
-
-
+#       pip install numpy 
 
 import dlib
-from skimage import io
 
 image_file = '../examples/faces/2009_004587.jpg'
-img = io.imread(image_file)
+img = dlib.load_rgb_image(image_file)
 
 # Locations of candidate objects will be saved into rects
 rects = []

python_examples/opencv_webcam_face_detection.py

@@ -0,0 +1,59 @@
+#!/usr/bin/python
+# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+#
+#   This example program shows how to find frontal human faces in a webcam stream using OpenCV.
+#   It is also meant to demonstrate that rgb images from Dlib can be used with opencv by just
+#   swapping the Red and Blue channels.
+#
+#   You can run this program and see the detections from your webcam by executing the
+#   following command:
+#       ./opencv_face_detection.py
+#
+#   This face detector is made using the now classic Histogram of Oriented
+#   Gradients (HOG) feature combined with a linear classifier, an image
+#   pyramid, and sliding window detection scheme.  This type of object detector
+#   is fairly general and capable of detecting many types of semi-rigid objects
+#   in addition to human faces.  Therefore, if you are interested in making
+#   your own object detectors then read the train_object_detector.py example
+#   program.  
+#
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
+#
+#   Also note that this example requires Numpy which can be installed
+#   via the command:
+#       pip install numpy
+
+import sys
+import dlib
+import cv2
+
+detector = dlib.get_frontal_face_detector()
+cam = cv2.VideoCapture(0)
+color_green = (0,255,0)
+line_width = 3
+while True:
+    ret_val, img = cam.read()
+    rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    dets = detector(rgb_image)
+    for det in dets:
+        cv2.rectangle(img,(det.left(), det.top()), (det.right(), det.bottom()), color_green, line_width)
+    cv2.imshow('my webcam', img)
+    if cv2.waitKey(1) == 27:
+        break  # esc to quit
+cv2.destroyAllWindows()

python_examples/requirements.txt

@@ -1,3 +1,2 @@
-scikit-image>=0.9.3
 opencv-python 
 numpy