Initial commit

.gitignore

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+yolov8n-face.pt

README.md

@@ -1,8 +1,8 @@
 ---
 title: FootballChecker
-emoji: 🐨
-colorFrom: blue
-colorTo: indigo
+emoji: 👁️
+colorFrom: purple
+colorTo: pink
 sdk: gradio
 sdk_version: 3.35.2
 app_file: app.py

app.py

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+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 = np.asarray(bytearray(file.read()), dtype=np.uint8)
+                img = cv2.imdecode(img_bytes, cv2.IMREAD_COLOR)
+                images.append(img)
+    return [img for img in images if img is not None]
+
+def create_image(images):
+    if not images:
+        return np.zeros((1, 1, 3), dtype=np.uint8)
+    
+    max_width = max([img.shape[1] for img in images])
+    total_height = sum([img.shape[0] for img in images])
+
+    new_image = np.zeros((total_height, max_width, 3), dtype=np.uint8)
+
+    y_offset = 0
+    for img in images:
+        new_image[y_offset:y_offset+img.shape[0], :img.shape[1], :] = img
+        y_offset += img.shape[0]
+
+    new_image = cv2.resize(new_image, (0, 0), fx=0.5, fy=0.5)
+
+    return new_image
+
+def check(avatars_zip, photos_zip):
+  avatars = load_images_from_zip(avatars_zip.name)
+  photos = load_images_from_zip(photos_zip.name)
+  not_found_faces = []
+  for photo in photos:
+    photo = cv2.cvtColor(photo, cv2.COLOR_RGB2BGR)
+    input_faces = find_faces(photo)
+    for input_face in input_faces:
+        avatars_checked = 0
+        for avatar in avatars:
+            avatar = cv2.cvtColor(avatar, cv2.COLOR_RGB2BGR)
+            distance = find_distance(avatar, input_face)
+            if distance > FACE_DIST_TRESH:
+                break
+            else:
+                avatars_checked+=1
+            if avatars_checked == len(avatars):
+                not_found_faces.append(input_face)
+
+  return create_image(not_found_faces)
+
+title = '<h1 style="text-align:center">FootballChecker</h1>'
+
+with gr.Blocks(theme='soft', title='FootballChecker') as blocks:
+    gr.HTML(title)
+    gr.Markdown('Face checking toolkit')
+    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()

config.json

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+{
+    "FACE_DIST_TRESH": 0.15,
+    "FACE_DET_TRESH": 0.3,
+    "YOLO_WEIGHTS_URL": "https://github.com/akanametov/yolov8-face/releases/download/v0.0.0/yolov8n-face.pt"
+}

requirements.txt

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+deepface
+ultralytics
+zipfile