Subindo arquivos7

app.py

@@ -4,17 +4,30 @@ from PIL import Image
 from gtts import gTTS
 import numpy as np
 import cv2
-from sklearn.feature_extraction.image import greycomatrix, greycoprops
 from transformers import BlipProcessor, BlipForConditionalGeneration, MarianMTModel, MarianTokenizer
 
 # Carregar o modelo YOLOv5
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
 
-# Função para análise de textura usando GLCM
-def analyze_texture(image):
+# Função para calcular a GLCM e o contraste manualmente
+def calculate_glcm_contrast(image):
     gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
-    glcm = greycomatrix(gray_image, distances=[5], angles=[0], levels=256, symmetric=True, normed=True)
-    contrast = greycoprops(glcm, 'contrast')[0, 0]
+    max_value = gray_image.max() + 1
+    glcm = np.zeros((max_value, max_value), dtype=np.float64)
+
+    for i in range(gray_image.shape[0] - 1):
+        for j in range(gray_image.shape[1] - 1):
+            x = gray_image[i, j]
+            y = gray_image[i + 1, j + 1]
+            glcm[x, y] += 1
+
+    glcm = glcm / glcm.sum()
+
+    contrast = 0.0
+    for i in range(max_value):
+        for j in range(max_value):
+            contrast += (i - j) ** 2 * glcm[i, j]
+    
     return contrast
 
 # Função para descrever imagem usando BLIP
@@ -45,7 +58,7 @@ def process_image(image):
     mean_rgb = np.mean(np.array(image), axis=(0, 1))
 
     # Análise de textura
-    texture_contrast = analyze_texture(image)
+    texture_contrast = calculate_glcm_contrast(image)
 
     # Descrição da imagem
     description = describe_image(image)

requirements.txt

@@ -3,6 +3,5 @@ torch
 Pillow
 numpy
 opencv-python
-scikit-learn
 transformers
 gtts