semi final

.gitignore

@@ -0,0 +1,5 @@
+.env
+temp/ 
+tmp/
+*.npz 
+*.ann

app.py

@@ -1,7 +1,40 @@
 import gradio as gr
+from util import download
+import os 
+from dotenv import load_dotenv
+from photo_search import PhotoSearch
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+load_dotenv()
+# tree_path = download.gdrive(os.getenv('TREE_GDRIVE_ID'), dir='temp')
+# data_path = download.gdrive(os.getenv('DATA_GDRIVE_ID'), dir='temp')
+tree_path= 'temp/trees.ann'
+data_path = 'temp/result_torch.npz'
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+ai = PhotoSearch(tree_path, data_path)
+
+def search_photo(image):    
+    try:
+        found_ids = ai.search(image)
+        if len(found_ids) == 0:
+            return '''
+                Foto Serupa Tidak Ditemukan, Tips: 
+                - Gunakan foto terupdate,
+                - Jika kamu punya foto saat lari, lebih baik gunakan foto itu,
+                - Jangan menggunakan foto blur,
+                - Jangan upload foto yang menggunakan aksesoris yang tidak kamu gunakan saat lari (semisal kacamata)
+                '''
+                
+        result = f"{len(found_ids)} foto kamu berhasil ditemukan!"
+    except Exception as e: 
+        result = f"Gagal: {e}"
+    return result 
+
+
+gr_inputs = [
+  gr.Image(type='pil')
+]
+
+gr.Markdown('Upload Foto Wajah Kamu Ya (Pastikan hanya terdapat SATU wajah pada foto yang kamu upload)')
+iface = gr.Interface(fn=search_photo, inputs=gr_inputs, outputs="markdown")
 iface.launch()

face_recognition.py

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+from facenet_pytorch import InceptionResnetV1, MTCNN as FACE_DETECTOR
+import torch 
+import numpy as np 
+
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+print('running device: ', device)
+
+class FacenetPytorch:
+
+    def __init__(self):
+        self.face_detector = FACE_DETECTOR(keep_all=True, device=device, margin=20, thresholds=[0.6, 0.9, 0.9])
+        self.resnet = InceptionResnetV1(pretrained='vggface2').eval()
+    
+    def get_embedding(self, image):
+        faces = self.extract_faces(image)
+        assert len(faces) == 1, f"total face should be 1, instead of {len(faces)}"
+        return np.squeeze(self.resnet(faces[0]).detach().numpy())
+
+    def extract_faces(self, image):
+        result = []
+        faces = self.face_detector(image)
+        if faces is not None:
+            print('total face: ', len(faces))
+            for face in faces:
+                result.append(face.unsqueeze(0))
+        return result

photo_search.py

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+from annoy import AnnoyIndex
+from face_recognition import FacenetPytorch
+import numpy as np 
+from numpy.linalg import norm
+
+class PhotoSearch:
+    def __init__ (self, tree_path, data_path, index_size = 512):
+        self.tree_path = tree_path
+        self.data_path = data_path
+
+        self.t = AnnoyIndex(index_size, 'angular')
+        self.t.load(tree_path)
+
+        data = np.load(data_path, allow_pickle=True)
+        self.face_grive_id = data.f.id
+        self.extractor = FacenetPytorch()
+
+    def search(self, image):
+        face_emb = self.extractor.get_embedding(image)
+        nns = self.t.get_nns_by_vector(face_emb, 20)
+        result = []
+        for n in nns:
+            face_origin = self.t.get_item_vector(n)
+            print(self.face_grive_id[n])
+            if self.cosine(face_emb, face_origin) < 0.7: continue
+
+            result.append('get..')
+
+        return result
+    
+    def cosine(self, x, y):
+        return np.dot(x,y)/(norm(x)*norm(y))

requirements.txt

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+aiofiles==23.1.0
+aiohttp==3.8.5
+aiosignal==1.3.1
+altair==5.0.1
+annotated-types==0.5.0
+annoy==1.17.3
+async-timeout==4.0.2
+click==8.1.6
+cmake==3.27.0
+contourpy==1.1.0
+cycler==0.11.0
+facenet-pytorch==2.5.3
+fastapi==0.100.1
+ffmpy==0.3.1
+fonttools==4.41.1
+frozenlist==1.4.0
+fsspec==2023.6.0
+gdown==4.7.1
+gradio==3.39.0
+gradio_client==0.3.0
+h11==0.14.0
+httpcore==0.17.3
+httpx==0.24.1
+huggingface-hub==0.16.4
+keras==2.13.1
+kiwisolver==1.4.4
+linkify-it-py==2.0.2
+lit==16.0.6
+matplotlib==3.7.2
+mdit-py-plugins==0.3.3
+mpmath==1.3.0
+mtcnn==0.1.1
+multidict==6.0.4
+networkx==3.1
+numpy==1.25.1
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+opencv-python==4.8.0.74
+orjson==3.9.2
+pandas==2.0.3
+Pillow==10.0.0
+pydantic==2.1.1
+pydantic_core==2.4.0
+pydub==0.25.1
+pyparsing==3.0.9
+PySocks==1.7.1
+python-dotenv==1.0.0
+python-multipart==0.0.6
+pytz==2023.3
+semantic-version==2.10.0
+starlette==0.27.0
+sympy==1.12
+toolz==0.12.0
+torch==2.0.1
+torchvision==0.15.2
+tqdm==4.65.0
+triton==2.0.0
+typing_extensions==4.7.1
+tzdata==2023.3
+uc-micro-py==1.0.2
+uvicorn==0.23.1
+websockets==11.0.3
+yarl==1.9.2

util/download.py

@@ -0,0 +1,14 @@
+import gdown
+import os
+
+
+def gdrive(id, dir = "."):
+    filename = gdown.download(id=id, quiet=True)
+    file_path = f'{dir}/{filename}'
+
+    if os.path.isdir(dir) == False: os.mkdir(dir)        
+    try:
+        os.replace(filename, file_path)
+    except:
+        print('replace err...')
+    return file_path