Spaces:
Runtime error
Runtime error
File size: 4,848 Bytes
8434bec bd5a570 8434bec bd5a570 8434bec bd5a570 8434bec bd5a570 8434bec bd5a570 8434bec bd5a570 8434bec bd5a570 8434bec ace09f3 8434bec ace09f3 8434bec ace09f3 8434bec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
import json
import zipfile
import numpy as np
import cv2
import os
import gradio as gr
from deepface import DeepFace
from ultralytics import YOLO
import urllib.request
with open("config.json", "r") as f:
config = json.load(f)
FACE_DIST_TRESH = config['FACE_DIST_TRESH']
FACE_DET_TRESH = config['FACE_DET_TRESH']
YOLO_WEIGHTS_URL = config['YOLO_WEIGHTS_URL']
yolo_weights_filename = os.path.basename(YOLO_WEIGHTS_URL)
if not os.path.exists(yolo_weights_filename):
urllib.request.urlretrieve(YOLO_WEIGHTS_URL, yolo_weights_filename)
model = YOLO(yolo_weights_filename)
def find_distance(base_face, check_face):
result = DeepFace.verify(base_face, check_face, enforce_detection=False)
return result['distance']
def find_faces(image):
outputs = model(image)
faces = []
for box in outputs[0].boxes:
if float(box.conf) >= FACE_DET_TRESH:
x, y, w, h = [int(coord) for coord in box.xywh[0]]
x_center, y_center = x + w / 2, y + h / 2
x1 = int(x_center - w)
y1 = int(y_center - h)
crop_img = image[y1:y1+h, x1:x1+w]
faces.append(crop_img)
return faces
def load_images_from_zip(zip_path):
images = []
with zipfile.ZipFile(zip_path, 'r') as zip_file:
for file_name in zip_file.namelist():
with zip_file.open(file_name) as file:
img_bytes = file.read()
img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), cv2.IMREAD_COLOR)
if img is not None:
images.append(img)
return images
def create_image(images):
table_width = 800
row_height = 100
margin = 10
text_margin = 20
table_height = text_margin + margin + (row_height + margin) * len(images)
font = cv2.FONT_HERSHEY_SIMPLEX
font_scale = 0.5
color = (255, 255, 255)
thickness = 2
table = np.zeros((table_height, table_width, 3), np.uint8)
id_col_width = 100
id_x = 10
img_x = id_col_width + 10
y = text_margin
cv2.putText(table, "Image ID", (id_x, y), font, font_scale, color, thickness)
cv2.putText(table, "Face", (img_x, y), font, font_scale, color, thickness)
y += margin
for i, img in enumerate(images):
height, width = img.shape[:2]
new_width = int(width * row_height / height)
if img_x + new_width > table_width:
new_width = table_width - img_x
img_resized = cv2.resize(img, (new_width, row_height))
cv2.putText(table, str(i), (id_x, y + margin), font, font_scale, color, thickness)
table[y:y+row_height, img_x:img_x+new_width] = img_resized
y += row_height + margin
for col in range(table.shape[1]-1, -1, -1):
if not np.any(table[:, col]):
continue
else:
break
table_cropped = table[:, :col+1+id_x]
return table_cropped
def check(avatars_zip, photos_zip):
avatars = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in load_images_from_zip(avatars_zip.name)]
photos = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in load_images_from_zip(photos_zip.name)]
input_avatars_faces = [find_faces(avatar) for avatar in avatars]
input_avatars_faces = [face for faces in input_avatars_faces for face in faces]
avatars_faces_count = len(input_avatars_faces)
not_found_faces = []
for photo in photos:
input_faces = find_faces(photo)
for input_face in input_faces:
for i in range(avatars_faces_count):
distance = find_distance(input_avatars_faces[i], input_face)
if distance <= FACE_DIST_TRESH:
break
elif i + 1 == avatars_faces_count:
not_found_faces.append(input_face)
return create_image(not_found_faces)
title = '<h1 style="text-align:center">SquadDetective</h1>'
with gr.Blocks(theme='soft', title='SquadDetective') as blocks:
gr.HTML(title)
gr.Markdown('SquadDetective is a service that helps sports teams to identify unclaimed players by comparing their faces to photos taken during matches. By using state-of-the-art facial recognition technology, SquadDetective can quickly and accurately match the faces of players in photos to a database of registered players, allowing teams to quickly identify any unclaimed players and take appropriate action. With SquadDetective, sports teams can ensure that all players are properly registered and eligible to play, helping to avoid potential penalties and other issues')
with gr.Row():
avatars = gr.inputs.File(label="Avatar Images (zip)")
photos = gr.inputs.File(label="Check Photos (zip)")
inputs = [avatars, photos]
process_button = gr.Button('Check images')
outputs=gr.outputs.Image(type="numpy", label="Results")
process_button.click(fn=check, inputs=inputs, outputs=outputs)
blocks.launch() |