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import os
import json
import math
import base64
os.environ["DEEPFACE_HOME"] = "."
import pyzipper
import numpy as np
import gradio as gr
from annoy import AnnoyIndex
from deepface import DeepFace
index = AnnoyIndex(512, "euclidean")
index.load(f"face.db")
ANNOY_INDEX = json.load(open(f"face.json"))
with pyzipper.AESZipFile('persons.zip') as zf:
password = os.getenv("VISAGE_KEY","").encode('ascii')
zf.setpassword(password)
PERFORMER_DB = json.loads(zf.read('performers.json'))
## Prediction functions
def image_search_performer(image, threshold=20.0, results=3):
"""Search for a performer in an image
Returns a list of performers with at least following keys:
- id: the performer's id
- distance: the distance between the face in the image and the performer's face
- confidence: a confidence score between 0 and 100
- hits: the number of times the performer was found in our database
"""
image_array = np.array(image)
face = DeepFace.represent(img_path = image_array, detector_backend='retinaface', model_name='Facenet512', normalization="Facenet2018")[0]['embedding']
return search_performer(face, threshold, results)
def image_search_performers(image, threshold=20.0, results=3):
image_array = np.array(image)
response = []
faces = DeepFace.represent(img_path = image_array, detector_backend='retinaface', model_name='Facenet512', normalization="Facenet2018")
# faces = DeepFace.represent(img_path = image_array, detector_backend='yolov8', model_name='SFace')
for face in faces:
embedding = face['embedding']
area = face['facial_area']
confidence = face['face_confidence']
cimage = image.crop((area['x'], area['y'], area['x'] + area['w'], area['y'] + area['h']))
# return image base64 encoded for display
with open('temp.jpg', 'wb') as f:
cimage.save(f, format='JPEG')
im_b64 = base64.b64encode(open('temp.jpg', 'rb').read())
response.append({
'image': im_b64.decode('ascii'),
'confidence': confidence,
'performers': search_performer(embedding, threshold, results)
})
return response
def vector_search_performer(vector_json, threshold=20.0, results=3):
"""Search for a performer from a vector
The vector should be created with Deepface and should be a 512 vector.
For best results use the following settings:
- detector_backend: retinaface
- model: Facenet512
- normalization: Facenet2018
Returns a list of performers with at least following keys:
- id: the performer's id
- distance: the distance between the face in the image and the performer's face
- confidence: a confidence score between 0 and 100
- hits: the number of times the performer was found in our database
"""
vector = np.array(json.loads(vector_json))
return search_performer(vector, threshold, results)
def search_performer(vector, threshold=20.0, results=3):
threshold = threshold or 20.0
results = results or 3
ids, distances = index.get_nns_by_vector(
vector, 50, search_k=10000, include_distances=True
)
persons = {}
for p, distance in zip(ids, distances):
id = ANNOY_INDEX[p]
if id in persons:
persons[id]["hits"] += 1
persons[id]["distance"] -= 0.5
persons[id]["confidence"] = normalize_confidence_from_distance(persons[id]["distance"], threshold)
continue
persons[id] = {
"id": id,
"distance": round(distance, 2),
"confidence": normalize_confidence_from_distance(distance, threshold),
"hits": 1,
}
if id in PERFORMER_DB:
persons[id].update(PERFORMER_DB.get(id))
persons = sorted(persons.values(), key=lambda x: x["distance"])
# persons = [p for p in persons if p["distance"] < threshold]
return persons[:results]
def normalize_confidence_from_distance(distance, threshold=20.0):
"""Normalize confidence to 0-100 scale"""
confidence = face_distance_to_conf(distance, threshold)
return int(((confidence - 0.0) / (1.0 - 0.0)) * (100.0 - 0.0) + 0.0)
def face_distance_to_conf(face_distance, face_match_threshold=20.0):
"""Using a face distance, calculate a similarity confidence value"""
if face_distance > face_match_threshold:
# The face is far away, so give it a low confidence
range = (1.0 - face_match_threshold)
linear_val = (1.0 - face_distance) / (range * 2.0)
return linear_val
else:
# The face is close, so give it a high confidence
range = face_match_threshold
linear_val = 1.0 - (face_distance / (range * 2.0))
# But adjust this value by a curve so that we don't get a linear
# transition from close to far. We want it to be more confident
# the closer it is.
return linear_val + ((1.0 - linear_val) * math.pow((linear_val - 0.5) * 2, 0.2))
image_search = gr.Interface(
fn=image_search_performer,
inputs=[
gr.components.Image(),
gr.components.Slider(label="threshold",minimum=0.0, maximum=30.0, value=20.0),
gr.components.Slider(label="results", minimum=0, maximum=50, value=3, step=1),
],
outputs=gr.outputs.JSON(label=""),
title="Who is in the photo?",
description="Upload an image of a person and we'll tell you who it is.",
)
image_search_multiple = gr.Interface(
fn=image_search_performers,
inputs=[
gr.components.Image(type='pil'),
gr.components.Slider(label="threshold",minimum=0.0, maximum=30.0, value=20.0),
gr.components.Slider(label="results", minimum=0, maximum=50, value=3, step=1),
],
outputs=gr.outputs.JSON(label=""),
title="Who is in the photo?",
description="Upload an image of a person(s) and we'll tell you who it is.",
)
vector_search = gr.Interface(
fn=vector_search_performer,
inputs=[
gr.components.Textbox(),
gr.components.Slider(label="threshold",minimum=0.0, maximum=30.0, value=20.0),
gr.components.Slider(label="results", minimum=0, maximum=50, value=3, step=1),
],
outputs=gr.outputs.JSON(label=""),
title="Who is in the photo?",
description="512 vector created with deepface of a person and we'll tell you who it is.",
)
gr.TabbedInterface([image_search, image_search_multiple, vector_search]).launch(enable_queue=True, server_name="0.0.0.0")
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