import base64 import cv2 import numpy as np import requests import logging import tempfile import subprocess import os from typing import List 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 from skimage.metrics import structural_similarity as ssim from models import RequestModel, ResponseModel from PIL import Image from io import BytesIO logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') mobilenet = MobileNetV2(weights="imagenet", include_top=False, pooling='avg') def preprocess_image_for_mobilenet(image): 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) 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_sim(img1, img2, img1AssetCode, img2AssetCode): 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] sim_score = (sim + 1) * 50 print(f"MobileNet similarity score from {img1AssetCode} and {img2AssetCode} is {sim_score}") return float(sim_score) except Exception as e: logging.error("Erro ao calcular similaridade com MobileNet", exc_info=True) return 0 def orb_sim(img1, img2, img1AssetCode, img2AssetCode): score = 0 try: orb = cv2.ORB_create() kp_a, desc_a = orb.detectAndCompute(img1, None) kp_b, desc_b = orb.detectAndCompute(img2, None) 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: score = (len(similar_regions) / len(matches)) * 100 if (score > 0): logging.info(f"Orb score from {img1AssetCode} and {img2AssetCode} is {score}") except Exception as e: logging.error("Erro ao verificar similaridade ORB", exc_info=True) return 1 if 0 < score < 1 else score def ssim_sim(img1, img2): s, _ = ssim(img1, img2, full=True) return (s + 1) * 50 def load_image_url(source, assetCode, contentType=None, ffmpeg_path='ffmpeg', frame_time=1): Image.MAX_IMAGE_PIXELS = None def extract_frame_from_video(video_path_or_url, time_sec): print(f"[INFO] A extrair frame do vídeo: {video_path_or_url} no segundo {time_sec}") with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as temp_frame: frame_path = temp_frame.name command = [ ffmpeg_path, "-ss", str(time_sec), "-i", video_path_or_url, "-frames:v", "1", "-q:v", "2", "-y", frame_path ] print(f"[DEBUG] Comando ffmpeg: {' '.join(command)}") result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) if result.returncode != 0: print(f"[ERRO] ffmpeg falhou com código {result.returncode}") print(f"[ERRO] stderr: {result.stderr.decode('utf-8')}") raise RuntimeError("Erro ao extrair frame com ffmpeg.") if not os.path.exists(frame_path): print("[ERRO] Frame não criado. Verifica se o caminho do vídeo está correto e acessível.") raise ValueError("Frame não encontrado após execução do ffmpeg.") frame = cv2.imread(frame_path, cv2.IMREAD_GRAYSCALE) os.remove(frame_path) if frame is None: print("[ERRO] Falha ao ler frame extraído com OpenCV.") raise ValueError("Erro ao carregar frame extraído.") print(f"[SUCESSO] Frame extraído com sucesso de {video_path_or_url}") return frame try: if source.startswith('http'): print(f"[INFO] Content-Type de {assetCode} é {contentType}") if contentType and contentType.startswith('video'): return extract_frame_from_video(source, frame_time) print(f"[INFO] A carregar imagem {assetCode} a partir de URL") response = requests.get(source) img = np.asarray(bytearray(response.content), dtype=np.uint8) img = cv2.imdecode(img, cv2.IMREAD_GRAYSCALE) return img else: print(f"[INFO] A tentar carregar base64 de {assetCode} como imagem ou vídeo.") try: img_bytes = base64.b64decode(source) if contentType and contentType.startswith('image'): print(f"[INFO] Base64 de {assetCode} identificado como imagem") img = Image.open(BytesIO(img_bytes)) img = np.array(img) img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) return img else: print(f"[INFO] Base64 de {assetCode} identificado como vídeo") with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video: temp_video.write(img_bytes) temp_video_path = temp_video.name frame = extract_frame_from_video(temp_video_path, frame_time) os.remove(temp_video_path) return frame except Exception as e: print(f"[ERRO] Falha ao processar base64 de {assetCode}: {e}") raise except Exception as e: print(f"[ERRO] Falha ao carregar imagem para {assetCode}: {e}") return None def check_similarity(images: List[RequestModel]): logging.info(f"Checking similarity for main source with resource id {images[0].originId}") original_image = load_image_url(images[0].source, images[0].assetCode) original_image_shape = original_image.shape results = [] for i in range(1, len(images)): try: image = load_image_url(images[i].source, images[i].source) image = cv2.resize(image, original_image_shape[::-1]) similarity_score = ssim_sim(original_image, image) similarity_orb_score = orb_sim(original_image, image, images[0].assetCode, images[i].assetCode) similarity_mobilenet_score = mobilenet_sim(original_image, image, images[0].assetCode, images[i].assetCode) except Exception as e: logging.error(f"Error loading image for resource id {images[i].originId} : {e}") similarity_score = 0 similarity_orb_score = 0 similarity_mobilenet_score = 0 response = ResponseModel(originId=images[i].originId, source=images[i].source, sequence=images[i].sequence, assetCode=images[i].assetCode, similarity=similarity_score, similarityOrb=similarity_orb_score, similarityMobileNet=similarity_mobilenet_score) results.append(response) return results