removed facedetection cause errors

docs/conf.py

@@ -34,9 +34,9 @@
 author = 'Giulio Gabrieli'
 
 # The short X.Y version
-version = '0.0.8.6'
+version = '0.0.8.7'
 # The full version, including alpha/beta/rc tags
-release = '0.0.8.6'
+release = '0.0.8.7'
 
 
 # -- General configuration ---------------------------------------------------

pyaesthetics.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: pyaesthetics
-Version: 0.0.8.6
+Version: 0.0.8.7
 Summary: Images aesthetic analysis
 Home-page: https://github.com/Gabrock94/pyaesthetics
 Author: Giulio Gabrieli

pyaesthetics/__init__.py

@@ -14,7 +14,7 @@
 from . import visualcomplexity
 
 # This is used to print out the package version
-__version__ = '0.0.8.6' #Version Control
+__version__ = '0.0.8.7' #Version Control
 
 print("Thank you for using pyaesthetics. If you use it in your work, please cite:")
 print("Gabrieli, G., Bornstein, M. H., Setoh, P., & Esposito, G. (2023). Machine learning estimation of users’ implicit and explicit aesthetic judgments of web-pages. Behaviour & Information Technology, 42(4), 392-402.")

pyaesthetics/analysis.py

@@ -145,9 +145,9 @@ def analyze_image(pathToImg, method='fast', resize=True, newSize=(600, 400), min
         resultdict["Colorfulness_HSV"] = colorfulness.colorfulness_hsv(imageColor)
         resultdict["Colorfulness_RGB"] = colorfulness.colorfulness_rgb(imageColor)
         resultdict["FacesCv2"] = facedetection.detect_faces_cv2(imageColor)
-        resultdict["FacesFd"] = facedetection.detect_faces(pathToImg)
+        # resultdict["FacesFd"] = facedetection.detect_faces(pathToImg)
         resultdict["Number_of_Faces_Cv2"] = len(resultdict["FacesCv2"])
-        resultdict["Number_of_Faces_fd"] = len(resultdict["FacesFd"])
+        # resultdict["Number_of_Faces_fd"] = len(resultdict["FacesFd"])
         resultdict["Colors"] = colordetection.get_colors_w3c(imageColor, ncolors=140)
         A = spacebaseddecomposition.get_areas(imageColor_O)
         Adict = spacebaseddecomposition.text2image_ratio(A)
@@ -180,7 +180,7 @@ def analyze_image(pathToImg, method='fast', resize=True, newSize=(600, 400), min
     sample_img = data_folder + "face1.png"
 
     # Analyze the sample image using the 'complete' method
-    results = analyze_image(sample_img, method='fast')
+    results = analyze_image(sample_img, method='complete')
     # Print the analysis results
     print(results)
 

pyaesthetics/facedetection.py

@@ -54,38 +54,39 @@ def detect_faces_cv2(img, plot=False):
     # Return the list of detected faces
     return faces
 
-def detect_faces(imgpath, plot=False, model='hog'):
-    """ This function uses face-recognition to detect faces in a picture.
-        By default it uses the hog method. cnn method can be passed as model parameter.
+# TODO: Wait for patch
+# def detect_faces(imgpath, plot=False, model='hog'):
+#     """ This function uses face-recognition to detect faces in a picture.
+#         By default it uses the hog method. cnn method can be passed as model parameter.
 
-        :param img: path to the image to analyze
-        :type img: string
-        :param plot: whether to plot or not the results
-        :type plot: bool
-        :param model: which model to use for the detection of faces (hog or cnn). Default is 'hog'.
-        :type model: string
-        :return: list of detected faces as rectangles
-        :rtype: list
-    """
+#         :param img: path to the image to analyze
+#         :type img: string
+#         :param plot: whether to plot or not the results
+#         :type plot: bool
+#         :param model: which model to use for the detection of faces (hog or cnn). Default is 'hog'.
+#         :type model: string
+#         :return: list of detected faces as rectangles
+#         :rtype: list
+#     """
 
-    # Convert the image to grayscale as Haar cascades work better on grayscale images
-    image = face_recognition.load_image_file(imgpath)
-    faces = face_recognition.face_locations(image, model=model)
-    # If plot is True, draw rectangles around detected faces and display the image
-    if plot:
-        for top, right, bottom, left in faces:
-            cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 2)
-            plt.imshow(image)
+#     # Convert the image to grayscale as Haar cascades work better on grayscale images
+#     image = face_recognition.load_image_file(imgpath)
+#     faces = face_recognition.face_locations(image, model=model)
+#     # If plot is True, draw rectangles around detected faces and display the image
+#     if plot:
+#         for top, right, bottom, left in faces:
+#             cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 2)
+#             plt.imshow(image)
 
-    # if plot:
-    #     for top, right, bottom, left in faces:
-    #     # You can access the actual face itself like this:
-    #         face_image = image[top:bottom, left:right]
-    #         pil_image = Image.fromarray(face_image)
-    #         pil_image.show()
+#     # if plot:
+#     #     for top, right, bottom, left in faces:
+#     #     # You can access the actual face itself like this:
+#     #         face_image = image[top:bottom, left:right]
+#     #         pil_image = Image.fromarray(face_image)
+#     #         pil_image.show()
 
-    # Return the list of detected faces
-    return faces
+#     # Return the list of detected faces
+#     return faces
 
 ###############################################################################
 #                                                                             #
@@ -116,7 +117,7 @@ def detect_faces(imgpath, plot=False, model='hog'):
     print("Number of faces in the picture is:", len(faces))
 
     # Detect faces in the image and plot the results
-    faces = detect_faces(sample_img, plot=True)
+    # faces = detect_faces(sample_img, plot=True)
 
     # Print the number of faces detected
     print("Number of faces in the picture is:", len(faces))

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pyaesthetics"
-version = "0.0.8.6"
+version = "0.0.8.7"
 description = "A python package to estimate visual features about the aesthetic appearance of still images."
 authors = ["Giulio Gabrieli <[email protected]>"]
 license = "GPL-3.0"