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Facial-feature-detector

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paresh95 commited on Oct 2, 2023

Commit

0347340

1 Parent(s): c3143ea

PS | Improve UI

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Files changed (6) hide show

app.py +58 -15
parameters.yml +26 -0
src/cv_utils.py +8 -0
src/face_proportions.py +3 -2
src/face_symmetry.py +4 -3
src/face_texture.py +5 -2

app.py CHANGED Viewed

@@ -1,36 +1,79 @@
 import gradio as gr
 import os
 from src.face_texture import GetFaceTexture
 from src.face_symmetry import GetFaceSymmetry
 from src.face_demographics import GetFaceDemographics
 from src.face_proportions import GetFaceProportions
-def combined_fn(input_image):
-    texture_results = GetFaceTexture().main(input_image)
-    symmetry_results = GetFaceSymmetry().main(input_image)
-    demographics_results = GetFaceDemographics().main(input_image)
-    proportion_results = GetFaceProportions().main(input_image)
-    return (*texture_results, *symmetry_results, demographics_results, *proportion_results)
 gigi_hadid = os.path.join(os.path.dirname(__file__), "data/gigi_hadid.webp")
 iface = gr.Interface(
     fn=combined_fn,
-    inputs=gr.Image(type="pil", label="Upload Face Image", value=gigi_hadid),
     outputs=[
         gr.Image(type="pil", label="Extracted face"),
-        gr.Image(type="pil", label="Extracted face texture"),
-        "json",
-        gr.Image(type="pil", label="Face symmetry"),
-        "json",
-        "json",
-        "json",
-        "json",
         gr.Image(type="pil", label="Face landmarks"),
     ],
     title="Advanced Facial Feature Detector",
-    description="A comprehensive tool for detailed face analysis. Please upload a clear face image for best results.",
     theme=gr.themes.Soft(),
     live=False,
 )

 import gradio as gr
 import os
+import yaml
 from src.face_texture import GetFaceTexture
 from src.face_symmetry import GetFaceSymmetry
 from src.face_demographics import GetFaceDemographics
 from src.face_proportions import GetFaceProportions
+def combined_fn(input_image, input_image_2):
+    demographics_dict = GetFaceDemographics().main(input_image)
+    golden_ratios_dict, equal_ratios_dict, face_landmarks_image = GetFaceProportions().main(input_image)
+    face_symmetry_image, symmetry_dict = GetFaceSymmetry().main(input_image)
+    face_image, face_texture_image, texture_dict = GetFaceTexture().main(input_image)
+    results = {
+        "Demographic predictions": demographics_dict,
+        "Face proportions (golden ratio)": golden_ratios_dict,
+        "Face proportions (equal ratio)": equal_ratios_dict,
+        "Face symmetry metrics": symmetry_dict,
+        "Face texture metrics": texture_dict
+    }
+    with open("parameters.yml", 'r') as file:
+        data = yaml.safe_load(file)
+        results_interpretation = data["results_interpretation"]
+    return (results, results_interpretation, face_image, face_landmarks_image, face_symmetry_image, face_texture_image)
 gigi_hadid = os.path.join(os.path.dirname(__file__), "data/gigi_hadid.webp")
+jay_z = os.path.join(os.path.dirname(__file__), "data/jay_z.jpg")
 iface = gr.Interface(
     fn=combined_fn,
+    inputs=[
+        gr.Image(type="pil", label="Upload Face 1", value=jay_z),
+        gr.Image(type="pil", label="Upload Face 2", value=gigi_hadid)
+            ],
     outputs=[
+        gr.JSON(label="Results"),
+        gr.JSON(label="Results explainer"),
         gr.Image(type="pil", label="Extracted face"),
         gr.Image(type="pil", label="Face landmarks"),
+        gr.Image(type="pil", label="Face symmetry"),
+        gr.Image(type="pil", label="Extracted face texture"),
     ],
     title="Advanced Facial Feature Detector",
+    description=
+    """
+    <!DOCTYPE html>
+    <html lang="en">
+    <head>
+        <meta charset="UTF-8">
+        <meta name="viewport" content="width=device-width, initial-scale=1.0">
+        <title>JSON Output in HTML</title>
+        <style>
+            .section {
+                margin-bottom: 20px;
+            }
+        </style>
+    </head>
+    <body>
+    <div class="section">
+        <p><strong>Description:</strong> This tool analyses a facial image to predict age and gender, assess symmetry, evaluate proportions, and examine texture.</p>
+        <p><strong>Instructions:</strong> For optimal results, upload a clear front-facing image (see example image). To do so, either drag and drop your photo or click on "Upload Face Image", then press 'Submit'.</p>
+        <p><strong>Interpreting the results:</strong></p>
+        <p><strong>Other information:</strong></p>
+        <ul>
+            <li>No uploaded photo is stored.</li>
+            <li>The output will take several seconds to compute.</li>
+            <li>If an error occurs try again or try a different photo or angle.</li>
+        </ul>
+    </div>
+    </body>
+    </html>
+    """,
     theme=gr.themes.Soft(),
     live=False,
 )

parameters.yml CHANGED Viewed

@@ -9,3 +9,29 @@ face_gender:
   model: "models/face_gender/gender_net.caffemodel"
 face_landmarks:
   model: 'models/face_alignment/shape_predictor_68_face_landmarks.dat'

   model: "models/face_gender/gender_net.caffemodel"
 face_landmarks:
   model: 'models/face_alignment/shape_predictor_68_face_landmarks.dat'
+results_interpretation:
+  Demographic predictions:
+    age_range: "Predicted age"
+    age_confidence: "Confidence of age prediction (0-1)"
+    gender: "Predicted gender"
+    gender_confidence: "Confidence of gender prediction (0-1)"
+  Face proportions (golden ratio):
+    Ideal ratio (golden ratio): "The ideal facial proportion ratio according to the classical Greek work on maths and geometry. Ideal facial features ratios in this section should be 1:1.62 (the golden ratio)"
+    Top of nose to middle of mouth vs middle mouth to bottom of chin: "See description"
+    Middle of mouth to bottom of mouth vs top of mouth to middle of mouth: "See description"
+  Face proportions (equal ratio):
+    Ideal ratio: "Typical facial features ratios in this section should be 1:1 (equal ratio)"
+    Eye width vs distance between eyes: "See description"
+    Eye to eyebrows vs eye height: "See description"
+    Center of left to right eye vs mouth width: "See description"
+  Face symmetry metrics:
+    structural_similarity: "Range: -1 (opposite) to 1 (similar). Considers differences in structural information, luminance, and texture."
+    cosine_distance: "Ranges: -1 to 1. 0 =similar, -1 or 1 = not similar. Considers differences in pixels."
+    mse: Lower means more similar. Minimum value is 0, maximum is unbounded. Measures pixel differences."
+    mae:  Lower means more similar. Minimum value is 0, maximum is unbounded. Measures pixel differences."
+    histogram_correlation: "Range: -1 (opposite) to 1 (similar). Considers similarity based on color or intensity distributions."
+    histogram_intersection: "Range: 0 (similar) to total pixels in image (not similar). Consider similarity based on color or intensity distributions."
+    orb_detector_matches: "Higher is better. Counts the number of matches between keypoints in images."
+    pixel_difference: "Lower is better.  Minimum value is 0, maximum is unbounded."
+  Face texture metrics:
+    Texture std: "Lower means less varied facial texture. Minimum is 0, maximum is unbounded."

src/cv_utils.py CHANGED Viewed

@@ -1,5 +1,6 @@
 import cv2
 import numpy as np
 def get_image(image_input) -> np.array:
@@ -9,3 +10,10 @@ def get_image(image_input) -> np.array:
     else:
         image = cv2.cvtColor(np.array(image_input), cv2.COLOR_RGB2BGR)  # PIL uses RGB
     return image

 import cv2
 import numpy as np
+from PIL import Image as PILImage
 def get_image(image_input) -> np.array:
     else:
         image = cv2.cvtColor(np.array(image_input), cv2.COLOR_RGB2BGR)  # PIL uses RGB
     return image
+def resize_image_height(image: PILImage.Image, new_height=300) -> PILImage.Image:
+    aspect_ratio = image.width / image.height
+    new_width = int(aspect_ratio * new_height)
+    image = image.resize((new_width, new_height))
+    return image

src/face_proportions.py CHANGED Viewed

@@ -4,7 +4,7 @@ import cv2
 import os
 import numpy as np
 import imutils
-from src.cv_utils import get_image
 from typing import List, Union
 from PIL import Image as PILImage
@@ -110,7 +110,8 @@ class GetFaceProportions:
         golden_ratios = {k: round(v, 2) for k, v in golden_ratios.items()}
         equal_ratios = self.compute_equal_ratios(shape)
         equal_ratios = {k: round(v, 2) for k, v in equal_ratios.items()}
-        image = PILImage.fromarray(image)
         return golden_ratios, equal_ratios, image

 import os
 import numpy as np
 import imutils
+from src.cv_utils import get_image, resize_image_height
 from typing import List, Union
 from PIL import Image as PILImage
         golden_ratios = {k: round(v, 2) for k, v in golden_ratios.items()}
         equal_ratios = self.compute_equal_ratios(shape)
         equal_ratios = {k: round(v, 2) for k, v in equal_ratios.items()}
+        image = PILImage.fromarray(image)
+        image = resize_image_height(image, new_height=300)
         return golden_ratios, equal_ratios, image

src/face_symmetry.py CHANGED Viewed

@@ -1,6 +1,6 @@
 import cv2
 import numpy as np
-from src.cv_utils import get_image
 from typing import Tuple, List, Union
 from skimage.metrics import structural_similarity as ssim
 from scipy.spatial import distance
@@ -132,9 +132,10 @@ class GetFaceSymmetry:
                 lowest_mse = d["mse"]
         full_face = np.hstack((best_left_half, best_right_half))
-        full_face = PILImage.fromarray(full_face)
         best_face_data = {k: float(round(v, 2)) for k, v in best_face_data.items()}
-        return full_face, best_face_data
 if __name__ == "__main__":

 import cv2
 import numpy as np
+from src.cv_utils import get_image, resize_image_height
 from typing import Tuple, List, Union
 from skimage.metrics import structural_similarity as ssim
 from scipy.spatial import distance
                 lowest_mse = d["mse"]
         full_face = np.hstack((best_left_half, best_right_half))
+        full_face_image = PILImage.fromarray(full_face)
+        full_face_image = resize_image_height(full_face_image, new_height=300)
         best_face_data = {k: float(round(v, 2)) for k, v in best_face_data.items()}
+        return full_face_image, best_face_data
 if __name__ == "__main__":

src/face_texture.py CHANGED Viewed

@@ -4,7 +4,7 @@ from skimage.feature import local_binary_pattern
 import dlib
 import imutils
 from PIL import Image as PILImage
-from src.cv_utils import get_image
 from typing import Tuple, List, Union
@@ -51,12 +51,15 @@ class GetFaceTexture:
         lbp = (lbp * 255).astype(np.uint8)
         return PILImage.fromarray(lbp)
-    def main(self, image_input) -> List[Union[PILImage.Image, np.array, float]]:
         image = get_image(image_input)
         gray_image = self.preprocess_image(image)
         face_image = self.get_face(gray_image)
         lbp, std = self.get_face_texture(face_image)
         face_texture_image = self.postprocess_image(lbp)
         return face_image, face_texture_image, {"Texture std": round(std, 2)}

 import dlib
 import imutils
 from PIL import Image as PILImage
+from src.cv_utils import get_image, resize_image_height
 from typing import Tuple, List, Union
         lbp = (lbp * 255).astype(np.uint8)
         return PILImage.fromarray(lbp)
+    def main(self, image_input) -> List[Union[PILImage.Image, PILImage.Image, dict]]:
         image = get_image(image_input)
         gray_image = self.preprocess_image(image)
         face_image = self.get_face(gray_image)
         lbp, std = self.get_face_texture(face_image)
         face_texture_image = self.postprocess_image(lbp)
+        face_image = PILImage.fromarray(face_image)
+        face_image = resize_image_height(face_image, new_height=300)
+        face_texture_image = resize_image_height(face_texture_image, new_height=300)
         return face_image, face_texture_image, {"Texture std": round(std, 2)}