from typing import List from fastapi import FastAPI, HTTPException from fastapi.responses import JSONResponse from models import RequestModel import os import json import cv2 import numpy as np import base64 import requests from PIL import Image from io import BytesIO from tensorflow.keras.applications import MobileNetV2 from tensorflow.keras.applications.mobilenet_v2 import preprocess_input from tensorflow.keras.models import Model from tensorflow.keras.preprocessing.image import img_to_array from sklearn.metrics.pairwise import cosine_similarity BASE_DIR = "saved_data" app = FastAPI() mobilenet = MobileNetV2(weights="imagenet", include_top=False, pooling='avg') def orb_sim(img1, img2): # ORB orb = cv2.ORB_create() kp_a, desc_a = orb.detectAndCompute(img1, None) kp_b, desc_b = orb.detectAndCompute(img2, None) # Brute-force matcher bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(desc_a, desc_b) similar_regions = [i for i in matches if i.distance < 20] if len(matches) == 0: return 0 return len(similar_regions) / len(matches) def preprocess_image_for_mobilenet(image): # Garantir que a imagem tem 3 canais if len(image.shape) == 2: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) elif image.shape[2] == 1: image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) else: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Redimensionar e preparar imagem image = cv2.resize(image, (224, 224)) image = img_to_array(image) image = np.expand_dims(image, axis=0) image = preprocess_input(image) return image def mobilenet_similarity(img1, img2): try: img1_proc = preprocess_image_for_mobilenet(img1) img2_proc = preprocess_image_for_mobilenet(img2) feat1 = mobilenet.predict(img1_proc, verbose=0) feat2 = mobilenet.predict(img2_proc, verbose=0) sim = cosine_similarity(feat1, feat2)[0][0] # Valor entre -1 e 1 sim_score = (sim + 1) * 50 # Escalar para 0-100 print(f"MobileNet similarity score is {sim_score}") return float(sim_score) except Exception as e: print("Erro ao calcular similaridade com MobileNet") return 0 def load_image(source): Image.MAX_IMAGE_PIXELS = None if source.startswith('http'): response = requests.get(source) img = np.asarray(bytearray(response.content), dtype=np.uint8) img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE) else: img = base64.b64decode(source) img = Image.open(BytesIO(img)) img = np.array(img) img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) return img app = FastAPI() BASE_DIR = "/tmp/data" @app.post("/save") async def save(image_data: RequestModel): os.makedirs(BASE_DIR, exist_ok=True) filename = os.path.join(BASE_DIR, f"{image_data.originId}_{image_data.assetCode}.json") img1 = load_image(image_data.originSource) img2 = load_image(image_data.source) similarity_orb = None similarity_mobilenet = None if img1 is not None and img2 is not None: similarity_orb = orb_sim(img1, img2) print(f"Similaridade ORB entre {image_data.originSource} e {image_data.source}: {similarity_orb}") similarity_mobilenet = mobilenet_similarity(img1, img2) print(f"Similaridade Mobilenet entre {image_data.originSource} e {image_data.source}: {similarity_mobilenet}") data_to_save = image_data.dict() if similarity_orb is not None: data_to_save["similarityOrb"] = similarity_orb data_to_save["similarityMobilenet"] = similarity_mobilenet with open(filename, "w") as f: json.dump(data_to_save, f, indent=4) return True @app.get("/files") async def list_files(): try: files_data = [] for filename in os.listdir(BASE_DIR): filepath = os.path.join(BASE_DIR, filename) if os.path.isfile(filepath): try: with open(filepath, "r") as f: file_content = f.read() # Lê o conteúdo do ficheiro # Tenta decodificar o conteúdo como JSON, se possível try: file_content_json = json.loads(file_content) files_data.append({"filename": filename, "content": file_content_json}) except json.JSONDecodeError: files_data.append({"filename": filename, "content": file_content}) # Se não for JSON, retorna o texto except (IOError, OSError) as e: raise HTTPException(status_code=500, detail=f"Erro ao ler o ficheiro {filename}: {e}") return JSONResponse({"files_data": files_data}) except FileNotFoundError: raise HTTPException(status_code=404, detail="Diretório de dados não encontrado") @app.get("/files/similar") async def list_similar_files(): try: files_data = [] for filename in os.listdir(BASE_DIR): filepath = os.path.join(BASE_DIR, filename) if os.path.isfile(filepath): try: with open(filepath, "r") as f: file_content = f.read() try: file_content_json = json.loads(file_content) # Check for similarityOrb and filter if "similarityOrb" in file_content_json and file_content_json["similarityOrb"] > 0: files_data.append({"filename": filename, "content": file_content_json}) except json.JSONDecodeError: pass # Skip files that are not valid JSON except (IOError, OSError) as e: raise HTTPException(status_code=500, detail=f"Erro ao ler o ficheiro {filename}: {e}") return JSONResponse({"files_data": files_data}) except FileNotFoundError: raise HTTPException(status_code=404, detail="Diretório de dados não encontrado") @app.get("/files/find/{origin_id}") async def get_file_by_origin_id(origin_id: int): try: for filename in os.listdir(BASE_DIR): if filename.startswith(f"{origin_id}_") and filename.endswith(".json"): filepath = os.path.join(BASE_DIR, filename) if os.path.isfile(filepath): try: with open(filepath, "r") as f: file_content = f.read() try: file_content_json = json.loads(file_content) return JSONResponse({"filename": filename, "content": file_content_json}) except json.JSONDecodeError: return JSONResponse({"filename": filename, "content": file_content}) except (IOError, OSError) as e: raise HTTPException(status_code=500, detail=f"Erro ao ler o ficheiro {filename}: {e}") raise HTTPException(status_code=404, detail=f"Ficheiro com originId '{origin_id}' não encontrado") except FileNotFoundError: raise HTTPException(status_code=404, detail="Diretório de dados não encontrado")