修改检测输出返回值

Dockerfile

@@ -10,9 +10,9 @@ RUN mkdir -p /app
 WORKDIR /app
 COPY requirements.txt /app
 RUN python -m venv .
-RUN pip install pip==20.1.1
+RUN pip install pip==23.0.1
 RUN pip install setuptools==46.1.3
-RUN pip install --no-cache-dir -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+RUN pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
 COPY ./app /app
 EXPOSE 5000
 CMD ["gunicorn", "--bind", ":5000", "server:app"]

README.md

@@ -44,3 +44,14 @@
 | 原始图片 | img_src | string | 图像的base64编码字符串   |
 | 是否有裂缝 | crack | bool | 是否有裂缝 |
 | 是否有坑洼 | pothole | bool | 是否有坑洼 |
+
+
+## 编译说明
+
+### x86编译docker 
+
+```docker build -t hpds-road-detection:v1.0  .```
+
+### arm64编译docker
+
+```docker buildx build -t hpds-road-detection-edge:v1.0 . --platform=linux/arm64```

app/serve.py

@@ -3,7 +3,7 @@ import numpy as np
 
 
 def serve_unet_model():
-    TFLITE_MODEL = "/app/UNet_25_Crack.tflite"
+    TFLITE_MODEL = "../app/UNet_25_Crack.tflite"
 
     tflite_interpreter = tf.lite.Interpreter(model_path=TFLITE_MODEL)
 
@@ -20,7 +20,7 @@ def serve_rcnn_model():
     detection_graph = tf.Graph()
     with detection_graph.as_default():
         od_graph_def = tf.compat.v1.GraphDef()
-        with tf.compat.v1.gfile.GFile("/app/frozen_inference_graph.pb", 'rb') as fid:
+        with tf.compat.v1.gfile.GFile("../app/frozen_inference_graph.pb", 'rb') as fid:
             serialized_graph = fid.read()
             od_graph_def.ParseFromString(serialized_graph)
             tf.import_graph_def(od_graph_def, name='')

app/server.py

@@ -191,13 +191,17 @@ def index():
             result = result > 0.5
             result = result * 255
             mask = np.squeeze(result)
+            bg = np.asarray(img_segment).copy()
             is_crack = False
             for i in range(len(mask)):
                 for j in range(len(mask[i])):
                     if mask[i][j] > 0:
+                        bg[i][j][0] = 0
+                        bg[i][j][1] = 0
+                        bg[i][j][2] = 255
                         is_crack = True
-                        break
 
+            img = Image.fromarray(bg.astype("uint8"))
 
             # start pothole detection
             image_np = load_image_into_numpy_array(img_src)
@@ -216,15 +220,22 @@ def index():
                 skip_scores=True,
                 skip_labels=True)
             raw_bytes = io.BytesIO()
-            img_src.save(raw_bytes, "JPEG")
+            raw_src = io.BytesIO()
+            img.save(raw_bytes, "JPEG")
+            img_src.save(raw_src,"JPEG")
             raw_bytes.seek(0)
+            raw_src.seek(0)
             img_byte = raw_bytes.getvalue()
-            img_str = base64.b64encode(img_byte)
+            img_src_byte = raw_src.getvalue()
+            img_str = base64.b64encode(img_src_byte)
+            img_discern = base64.b64encode(img_byte)
+
             data = {
                 "code": 0,
                 "crack": is_crack,
                 "pothole": is_pothole,
-                "img_src": img_str.decode('utf-8')
+                "img_src": img_str.decode('utf-8'),
+                "img_discern": img_discern.decode('utf-8')
             }
             return jsonify(data)
         else:

requirements.txt

@@ -1,48 +1,6 @@
-absl-py==0.9.0
-astunparse==1.6.3
-cachetools==4.1.0
-certifi==2020.4.5.1
-chardet==3.0.4
-click==7.1.2
-cycler==0.10.0
 Flask==1.1.2
-gast==0.3.3
-gevent==20.5.0
-google-auth==1.15.0
-google-auth-oauthlib==0.4.1
-google-pasta==0.2.0
-greenlet==0.4.15
-grpcio==1.29.0
-gunicorn==20.0.4
-h5py==2.10.0
-idna==2.9
-importlib-metadata==1.6.0
-itsdangerous==1.1.0
-Jinja2==2.11.2
-Keras-Preprocessing==1.1.2
-Markdown==3.2.2
-MarkupSafe==1.1.1
 matplotlib==3.2.1
 numpy==1.18.4
-oauthlib==3.1.0
-opt-einsum==3.2.1
 Pillow==7.1.2
-protobuf==3.11.3
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
-pyparsing==2.4.7
-python-dateutil==2.8.1
-requests==2.23.0
-requests-oauthlib==1.3.0
-rsa==4.0
-scipy==1.4.1
 six==1.15.0
-tensorboard==2.2.1
-tensorboard-plugin-wit==1.6.0.post3
 tensorflow==2.2.0
-tensorflow-estimator==2.2.0
-termcolor==1.1.0
-urllib3==1.25.9
-Werkzeug==1.0.1
-wrapt==1.12.1
-zipp==3.1.0