add celery

demo/predictor.py

 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
 import cv2
 import torch
-import skin_detector
+from skin_detector import skin_to_white
 
 from torchvision import transforms as T
 
@@ -292,6 +292,7 @@ class COCODemo(object):
 
         colors = self.compute_colors_for_labels(labels).tolist()
 
+        img = None
         for mask, color in zip(masks, colors):
             thresh = mask[0, :, :, None]
             contours, hierarchy = cv2_util.findContours(
@@ -300,6 +301,9 @@ class COCODemo(object):
             color = [0, 0, 0]
             img = cv2.drawContours(image, contours, -1, color, 3)
 
+        if img is None:
+            return
+
         # 把轮廓之外的颜色变为黑色
         a, b, c = img.shape
         for x in range(a):
@@ -314,7 +318,7 @@ class COCODemo(object):
                 img[x, y] = [0, 0, 0]
 
         # 把图片皮肤颜色变为白色
-        img = skin_detector(img)
+        img = skin_to_white(img)
 
         composite = img
         return composite

demo/test.py

 import os
+from multiprocessing import Process
+from threading import Thread
 from webcam import img_to_three_color_map
 
 
 def main():
-
-    os.walk('../../fashionbase/images/dataset_0520/weiyi')
-    img_to_three_color_map(source_image, target_image)
-
+    rs = os.walk('../../fashionbase/images/dataset_0520/weiyi')
+    for a, b, c in rs:
+         for i in c[:100]:
+             source_path = '../../fashionbase/images/dataset_0520/weiyi/' + i
+             target_path = 'output/' + i
+             print(source_path)
+             print(target_path)
+             img_to_three_color_map(source_path, target_path)
+             #p = Process(target=img_to_three_color_map, args=[source_path, target_path])
+             #p.start()
 
 
 if __name__ == '__main__':

demo/webcam.py

@@ -64,6 +64,7 @@ def img_to_three_color_map(source_path, target_path):
 
     img = cv2.imread(source_path)
     composite = coco_demo.run_on_opencv_image(img)
+    if composite is not None:
         cv2.imwrite(target_path, composite)
     # cam = cv2.VideoCapture(0)
     # while True:

demo/work.py

+import os
+from proj.tasks import img_reproce
+
+
+def write_redis(source_path, target_path):
+    i = 0
+    while i < 3:
+        try:
+            r = img_reproce.apply_async(args=[source_path, target_path])
+            return r
+        except:
+            i += 1
+
+
+def main():
+    rs = os.walk('../../fashionbase/images/dataset_0520/weiyi')
+    for a, b, files in rs:
+        for file in files:
+            source_path = '../../fashionbase/images/dataset_0520/weiyi/' + file
+            target_path = source_path.replace('dataset_0520', 'dataset_0520_10000')
+            print(source_path)
+            print(target_path)
+            # write_redis(source_path, target_path)
+
+
+if __name__ == '__main__':
+    main()

proj/celery.py

+from __future__ import absolute_import
+
+from celery import Celery
+
+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+app = Celery('proj', include=['proj.tasks'])
+app.config_from_object('proj.config')
+
+
+if __name__ == '__main__':
+    app.start()

proj/config.py

+BROKER_URL = 'redis://:zi1jlhVHtH8MGNqB@172.17.40.166:6379/3'
+
+BROKER_TRANSPORT_OPTIONS = {'visibility_timeout': 86400}  # 24 hour.
+
+CELERY_RESULT_BACKEND = 'redis://:zi1jlhVHtH8MGNqB@172.17.40.166:6379/3' # 把任务结果存在了Redis
+
+CELERY_TASK_SERIALIZER = 'msgpack' # 任务序列化和反序列化使用msgpack方案
+
+CELERY_RESULT_SERIALIZER = 'json' # 读取任务结果一般性能要求不高，所以使用了可读性更好的JSON
+
+CELERY_TASK_RESULT_EXPIRES = 60 * 60 * 24 # 任务过期时间，不建议直接写86400，应该让这样的magic数字表述更明显
+
+CELERY_ACCEPT_CONTENT = ['json', 'msgpack'] # 指定接受的内容类型
\ No newline at end of file

proj/tasks.py

+from __future__ import absolute_import
+
+from proj.celery import app
+from demo.webcam import img_to_three_color_map
+
+
+@app.task
+def img_reproce(source_path, target_path):
+    img_to_three_color_map(source_path, target_path)
\ No newline at end of file