盒子ocr检测

test_ocr.py

@@ -0,0 +1,45 @@
+import cv2
+import time
+from pp_onnx.onnx_paddleocr import ONNXPaddleOcr, draw_ocr
+
+# 优化参数配置，提高速度
+model = ONNXPaddleOcr(
+    use_angle_cls=True,
+    use_gpu=False,
+    providers=['AzureExecutionProvider'],
+    provider_options=[{'device_id': 0}],
+    det_limit_type='max',
+)
+
+def sav2Img(org_img, result, name="./result_img/3.jpg"):
+    from PIL import Image
+    result = result[0]
+    image = org_img[:, :, ::-1]
+    boxes = [line[0] for line in result]
+    txts = [line[1][0] for line in result]
+    scores = [line[1][1] for line in result]
+    im_show = draw_ocr(image, boxes, txts, scores)
+    im_show = Image.fromarray(im_show)
+    im_show.save(name)
+
+
+# 执行OCR推理
+img = cv2.imread('./test_img/3.jpg')
+if img is None:
+    print(f"❌ 未找到图像文件")
+else:
+    # 图像预处理 - 缩小图像尺寸加速处理
+    h, w = img.shape[:2]
+    max_size = 1080
+    if max(h, w) > max_size:
+        scale = max_size / max(h, w)
+        img = cv2.resize(img, (int(w * scale), int(h * scale)))
+
+    s = time.time()
+    result = model.ocr(img)
+    e = time.time()
+    print(f"total time: {e - s:.3f} 秒")
+    print("result:", result)
+    for box in result[0]:
+        print(box)
+    sav2Img(img, result)