feat 新增 生成sketch时对图片清理背景，剔除带有污点的结果图

app/core/config.py

@@ -19,7 +19,7 @@ class Settings(BaseSettings):
     LOGGING_CONFIG_FILE = os.path.join(BASE_DIR, 'logging_env.py')
 
 
-DEBUG = False
+DEBUG = True
 if DEBUG:
     LOGS_PATH = "logs/"
     CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv"
@@ -66,6 +66,7 @@ GI_MODEL_NAME = 'stable_diffusion_xl_lcm'
 GI_MODEL_URL = '10.1.1.150:8001'
 GI_MINIO_BUCKET = "aida-users"
 GI_RABBITMQ_QUEUES = os.getenv("GI_RABBITMQ_QUEUES", f"GenerateImage{RABBITMQ_ENV}")
+GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg"
 
 # SEG service config
 SEG_MODEL_URL = '10.1.1.240:10000'

app/service/generate_image/service.py

@@ -22,7 +22,7 @@ from tritonclient.utils import np_to_triton_dtype
 
 from app.core.config import *
 from app.schemas.generate_image import GenerateImageModel
-from app.service.generate_image.utils.remove_background import remove_background
+from app.service.generate_image.utils.image_processing import remove_background, stain_detection
 from app.service.generate_image.utils.upload_sd_image import upload_png_sd
 
 logger = logging.getLogger()
@@ -30,11 +30,14 @@ logger = logging.getLogger()
 
 class GenerateImage:
     def __init__(self, request_data):
+        if DEBUG is False:
+            self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+            self.channel = self.connection.channel()
+        self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+        self.channel = self.connection.channel()
         self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
         self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
-        self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
-        self.channel = self.connection.channel()
         if request_data.mode == "img2img":
             self.image = self.get_image(request_data.image_url)
             self.prompt = request_data.prompt
@@ -60,9 +63,10 @@ class GenerateImage:
             image_file = BytesIO(response.data)
             image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8)
             image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+            image = cv2.resize(image_cv2, (1024, 1024))
         except minio.error.S3Error:
-            image_cv2 = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
+            image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
-        return image_cv2
+        return image
 
     def callback(self, result, error):
         if error:
@@ -73,12 +77,22 @@ class GenerateImage:
         else:
             image_result = result.as_numpy("generated_image")[0]
             if self.category == "sketch":
-                image_result = remove_background(np.asarray(image_result))
+                # 去背景
-            image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
+                remove_bg_image = remove_background(np.asarray(image_result))
-            self.generate_data['status'] = "SUCCESS"
+                # 污点检测
-            self.generate_data['message'] = "success"
+                is_smudge, not_smudge_image = stain_detection(remove_bg_image)
-            self.generate_data['data'] = str(image_url)
+                if is_smudge is False:
-            self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+                    self.generate_data['status'] = "SUCCESS"
+                    self.generate_data['message'] = "success"
+                    self.generate_data['data'] = GI_SYS_IMAGE_URL
+                    self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
+                else:
+                    image_result = not_smudge_image
+                    image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
+                    self.generate_data['status'] = "SUCCESS"
+                    self.generate_data['message'] = "success"
+                    self.generate_data['data'] = str(image_url)
+                    self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
 
     def read_tasks_status(self):
         status_data = self.redis_client.get(self.tasks_id)
@@ -131,7 +145,8 @@ class GenerateImage:
             raise Exception(str(e))
         finally:
             dict_generate_data, str_generate_data = self.read_tasks_status()
-            self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
+            if DEBUG is False:
+                self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
             logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}")
 
 

app/service/generate_image/test.py

@@ -22,7 +22,7 @@ from PIL import Image
 import time
 from app.core.config import *
 from app.schemas.generate_image import GenerateImageModel
-from app.service.generate_image.utils.remove_background import remove_background
+from app.service.generate_image.utils.image_processing import remove_background
 from app.service.generate_image.utils.upload_sd_image import upload_png_sd
 from app.service.utils.decorator import RunTime
 from app.service.utils.generate_uuid import generate_uuid

app/service/generate_image/utils/image_processing.py

@@ -1,11 +1,15 @@
+import logging
+
 import cv2
 import mmcv
 import numpy as np
 import torch
-from PIL import Image
 import tritonclient.http as httpclient
 import torch.nn.functional as F
 from app.core.config import *
+import cv2
+
+logger = logging.getLogger()
 
 
 def seg_preprocess(img_path):
@@ -107,11 +111,15 @@ def remove_background(image):
 
     result_mask = front_mask + back_mask
     white_background = np.ones_like(image_obj) * 255
-    result_image = np.where(result_mask[:, :, None].astype(bool), image_obj, white_background)
+    remove_bg_image = np.where(result_mask[:, :, None].astype(bool), image_obj, white_background)
+    # cv2.imwrite("source_image", image)
+    # cv2.imwrite("remove_bg_image", remove_bg_image)
 
-    import cv2
+    return remove_bg_image
 
-    edges = cv2.Canny(result_image, 50, 150)
+
+def bounding_box(image):
+    edges = cv2.Canny(image, 50, 150)
     # 查找轮廓
     contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     # 初始化包围所有外接矩形的大矩形的坐标
@@ -126,7 +134,29 @@ def remove_background(image):
 
     # 根据大矩形的坐标来裁剪原始图像
     result_image = image[y_min:y_max, x_min:x_max]
-    # cv2.imshow("", cropped_image)
+    # cv2.imshow("result_image", result_image)
     # cv2.waitKey(0)
-
     return result_image
+
+
+def stain_detection(image, spot_size=200):
+    height, width, _ = image.shape
+
+    corners = [
+        image[0:spot_size, 0:spot_size],  # top left
+        image[0:spot_size, width - spot_size:width],  # top right
+        image[height - spot_size:height, 0:spot_size],  # bottom left
+        image[height - spot_size:height, width - spot_size:width]  # bottom right
+    ]
+
+    for index, corner in enumerate(corners):
+        num_white_pixels = (corner == [255, 255, 255]).all(axis=2).sum()
+        if num_white_pixels != spot_size * spot_size:
+            logger.info(f"第{index + 1}发现了污点")
+            return False, None
+
+    if DEBUG:
+        for corner_coords in [(0, 0), (0, width - spot_size), (height - spot_size, 0), (height - spot_size, width - spot_size)]:
+            cv2.rectangle(image, corner_coords, (corner_coords[0] + spot_size, corner_coords[1] + spot_size), (0, 0, 255), 2)
+
+    return True, image