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| import os | |
| import io | |
| import time | |
| import json | |
| import math | |
| import base64 | |
| from uuid import uuid4 | |
| from PIL import Image as PILImage | |
| 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 = [] | |
| t = time.time() | |
| try: | |
| 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='Facenet512', normalization="Facenet2018") | |
| # faces = DeepFace.represent(img_path = image_array, detector_backend='mtcnn', model_name='Facenet512', normalization="Facenet2018") | |
| except ValueError as e: | |
| print(e) | |
| raise gr.Error("No faces found in the image") | |
| print(f"Time to find faces: {time.time() - t}") | |
| 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'])) | |
| buf = io.BytesIO() | |
| cimage.save(buf, format='JPEG') | |
| im_b64 = base64.b64encode(buf.getvalue()).decode('ascii') | |
| response.append({ | |
| 'image': im_b64, | |
| '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=100000, 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)) | |
| def find_faces_in_sprite(image, vtt): | |
| vtt = base64.b64decode(vtt.replace("data:text/vtt;base64,", "")) | |
| sprite = PILImage.fromarray(image) | |
| results = [] | |
| for i, (left, top, right, bottom, time_seconds) in enumerate(getVTToffsets(vtt)): | |
| cut_frame = sprite.crop((left, top, left + right, top + bottom)) | |
| faces = DeepFace.extract_faces(np.asarray(cut_frame), detector_backend="mediapipe", enforce_detection=False, align=False) | |
| faces = [face for face in faces if face['confidence'] > 0.6] | |
| if faces: | |
| size = faces[0]['facial_area']['w'] * faces[0]['facial_area']['h'] | |
| data = {'id': str(uuid4()), "offset": (left, top, right, bottom), "frame": i, "time": time_seconds, 'size': size} | |
| results.append(data) | |
| return results | |
| def getVTToffsets(vtt): | |
| time_seconds = 0 | |
| left = top = right = bottom = None | |
| for line in vtt.decode("utf-8").split("\n"): | |
| line = line.strip() | |
| if "-->" in line: | |
| # grab the start time | |
| # 00:00:00.000 --> 00:00:41.000 | |
| start = line.split("-->")[0].strip().split(":") | |
| # convert to seconds | |
| time_seconds = ( | |
| int(start[0]) * 3600 | |
| + int(start[1]) * 60 | |
| + float(start[2]) | |
| ) | |
| left = top = right = bottom = None | |
| elif "xywh=" in line: | |
| left, top, right, bottom = line.split("xywh=")[-1].split(",") | |
| left, top, right, bottom = ( | |
| int(left), | |
| int(top), | |
| int(right), | |
| int(bottom), | |
| ) | |
| else: | |
| continue | |
| if not left: | |
| continue | |
| yield left, top, right, bottom, time_seconds | |
| 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.", | |
| ) | |
| faces_in_sprite = gr.Interface( | |
| fn=find_faces_in_sprite, | |
| inputs=[ | |
| gr.Image(), | |
| gr.Textbox(label="VTT file") | |
| ], | |
| outputs=gr.JSON(label=""), | |
| ) | |
| gr.TabbedInterface([image_search, image_search_multiple, vector_search, faces_in_sprite]).queue().launch(enable_queue=True, server_name="0.0.0.0") | |