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app/service/super_resolution/service.py

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+import io
+import logging
+from io import BytesIO
+
+import cv2
+import numpy as np
+import torch
+import tritonclient.http as httpclient
+from minio import Minio
+
+from app.core.config import MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, MINIO_PORT
+
+from app.service.utils.decorator import RunTime
+from app.service.utils.generate_uuid import generate_uuid
+
+logger = logging.getLogger()
+
+
+class SuperResolution:
+    def __init__(self):
+        self.triton_client = httpclient.InferenceServerClient(url=f"10.1.1.150:7000")
+        self.minio_client = Minio(
+            f"{MINIO_IP}:{MINIO_PORT}",
+            access_key=MINIO_ACCESS,
+            secret_key=MINIO_SECRET,
+            secure=MINIO_SECURE)
+
+    @RunTime
+    def read_image(self, image_url):
+        image_data = self.minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
+        img = np.frombuffer(image_data.data, np.uint8)  # 转成8位无符号整型
+        img = cv2.imdecode(img, cv2.IMREAD_COLOR).astype(np.float32) / 255.  # 解码
+        return img
+
+    @RunTime
+    def sr_result(self, image_url, sr_xn):
+        sample = self.read_image(image_url)
+        sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
+        sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
+        inputs = [
+            httpclient.InferInput("input", sample.shape, datatype="FP32")
+        ]
+        inputs[0].set_data_from_numpy(sample, binary_data=True)
+        results = self.triton_client.infer(model_name="super_resolution", inputs=inputs)
+
+        sr_output = torch.from_numpy(results.as_numpy(f"output"))
+        output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+        if output.ndim == 3:
+            output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))  # CHW-RGB to HCW-BGR
+        output = (output * 255.0).round().astype(np.uint8)
+        output_url = self.upload_img_sr(output, generate_uuid())
+        return output_url
+
+    def upload_img_sr(self, image, object_name):
+        try:
+            image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
+            image_url = f"test/{self.minio_client.put_object(f'test', f'{object_name}.jpg', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
+
+            return image_url
+        except Exception as e:
+            logger.warning(f"upload_png_mask runtime exception : {e}")
+
+
+if __name__ == '__main__':
+    service = SuperResolution()
+    result_url = service.sr_result("test/128_image/11.png", 4)
+    print(result_url)

app/service/super_resolution/test.py

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+import time
+
+import cv2
+import numpy as np
+import torch
+import tritonclient.http as httpclient
+from PIL import Image
+
+triton_client = httpclient.InferenceServerClient(url=f"10.1.1.150:7000")
+
+sample = cv2.imread("comic2.png", cv2.IMREAD_COLOR).astype(np.float32) / 255.
+sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
+sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
+inputs = [
+    httpclient.InferInput("input", sample.shape, datatype="FP32")
+]
+inputs[0].set_data_from_numpy(sample, binary_data=True)
+start_time = time.time()
+results = triton_client.infer(model_name="super_resolution", inputs=inputs)
+print(time.time() - start_time)
+sr_output = torch.from_numpy(results.as_numpy(f"output"))
+output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
+if output.ndim == 3:
+    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))  # CHW-RGB to HCW-BGR
+output = (output * 255.0).round().astype(np.uint8)
+# cv2.imshow("", output)
+# cv2.waitKey(0)
+cv2.imwrite("comic3.png", output)

app/service/utils/decorator.py

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+import time
+import logging
+
+
+def RunTime(func):
+    def wrapper(*args, **kwargs):
+        t1 = time.time()
+        res = func(*args, **kwargs)
+        t2 = time.time()
+        if t2 - t1 > 0.05:
+            logging.info(f"function：【{func.__name__}】,runtime：【{str(t2 - t1)}】s")
+        return res
+
+    return wrapper

app/service/utils/generate_uuid.py

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+import threading
+import uuid
+
+id_lock = threading.Lock()
+
+
+def generate_uuid():
+    with id_lock:
+        unique_id = str(uuid.uuid1())
+    return unique_id