Update extract_text.py

extract_text.py

@@ -1,29 +1,33 @@
-import cv2
 import numpy as np
+import cv2
 import easyocr
 import torch
 
-# Inicializar EasyOCR
 device = "cuda" if torch.cuda.is_available() else "cpu"
 reader = easyocr.Reader(["en"], gpu=(device == "cuda"), verbose=False)
 
-def extract_text_from_image(img, gpu_available):
-    reader = easyocr.Reader(['en'], gpu=gpu_available, verbose=False)
+def extract_text_from_image(upload_file, gpu_available):
+    upload_file.file.seek(0)
+    file_bytes = np.frombuffer(upload_file.file.read(), np.uint8)
+    
+    img = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+
+    if img is None:
+        raise ValueError("Não foi possível decodificar a imagem.")
 
-    img = np.array(img) 
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
-    # Resizing and blurring
     scale_factor = 2
     upscaled = cv2.resize(img, None, fx=scale_factor, fy=scale_factor, interpolation=cv2.INTER_LINEAR)
     blur_img = cv2.blur(upscaled, (5, 5))
     
+    reader = easyocr.Reader(['en'], gpu=gpu_available, verbose=False)
     all_text_found = []
     text_ = reader.readtext(blur_img, detail=1, paragraph=False, text_threshold=0.3)
 
     for t in text_:
         bbox, text, score = t
-        if score > 0.1:  # Filter weak detections
+        if score > 0.1:
             all_text_found.append(text)
 
-    return all_text_found
+    return all_text_found