修改 dockerfile

Dockerfile

@@ -1,8 +1,8 @@
 FROM nvcr.io/nvidia/l4t-tensorflow:r32.6.1-tf2.5-py3
+
 RUN apt-get install make g++ gcc
 RUN pip3 install gunicorn 
 RUN pip3 install setuptools==46.1.3
-#RUN pip3 install flask numpy Pillow six matplotlib
 RUN mkdir -p /app
 WORKDIR /app
 
@@ -12,5 +12,3 @@ RUN pip3 install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
 COPY ./app /app
 EXPOSE 5000
 CMD ["gunicorn", "--bind", ":5000", "server:app"]
-
-

Dockerfile_bak

@@ -1,18 +0,0 @@
-FROM python:3.7.16
-ENV PYTHONUNBUFFERED 1
-# RUN sed -i s@/deb.debian.org/@/mirrors.aliyun.com/@g /etc/apt/sources.list
-# RUN cat /etc/apt/sources.list
-# RUN apt-get update \
-#    && apt-get install -y make \
-#    && apt-get clean \
-#    && rm -rf /var/lib/apt/lists/*
-RUN mkdir -p /app
-WORKDIR /app
-COPY requirements.txt /app
-RUN python -m venv .
-# RUN pip install pip==23.0.1
-RUN pip install setuptools==46.1.3
-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

@@ -1,14 +1,14 @@
-# 道路病害检测
+# 病害检测
 
-## 利用了cnn网络和unet网络进行道路裂缝和坑洼图片的检测. 
+## 利用unet网络进行图片的裂缝检测. 
 
 ## API 接口
 
-### 道路裂缝检测接口(U-Net CNN)
+### 裂缝检测接口(U-Net CNN)
 
 - 请求
 
-```curl -k -X POST -F 'image=@image_path/ -v http://0.0.0.0:5000/segment ```
+```curl -k -X POST -F 'image=@image_path/ -v http://0.0.0.0:5000/bridge/crack ```
 
 - 返回接口
 
@@ -17,41 +17,3 @@
 | 返回结果 | result | bool | 是否有裂缝 |
 | 返回图片 | img | string | 图像的base64编码字符串 |
 
-
-### 道路坑洼检测接口(R-CNN)
-
-```curl -k -X POST -F 'image=@image_path/ -v http://0.0.0.0:5000/detect/rcnn ```
-
-
-- 返回接口
-
-| 名称   | 参数 | 类型 | 说明 |
-|------|------|-------|-------|
-| 返回结果 | result | bool | 是否有坑洼 |
-| 返回图片 | img | string | 图像的base64编码字符串 |
-
-
-### 裂缝和坑洼检测接口
-
-```curl -k -X POST -F 'image=@image_path/ -v http://0.0.0.0:5000/ ```
-
-
-- 返回接口
-
-| 名称   | 参数 | 类型     | 说明               |
-|------|------|--------|------------------|
-| 接口编码 | code | int    | 0：正常 ； 10001： 异常 |
-| 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/server.py

@@ -42,45 +42,7 @@ def load_image_into_numpy_array(image):
         (im_height, im_width, 3)).astype(np.uint8)
 
 
-@app.route("/detect/rcnn", methods=["POST"])
+@app.route("/bridge/crack", methods=["POST"])
-def detect_rcnn():
-    if flask.request.method == "POST":
-        if flask.request.files.get("image"):
-            image = Image.open(flask.request.files["image"])
-            image_np = load_image_into_numpy_array(image)
-            # image_np_expanded = np.expand_dims(image_np, axis=0)
-            output_dict = run_inference_for_single_image(image_np, detection_graph)
-            category_index = {0: {"name": "pothole"}, 1: {"name": "pothole"}}
-            print(output_dict.get('detection_masks'))
-            i, is_crack = vis_util.visualize_boxes_and_labels_on_image_array(
-                image_np,
-                output_dict['detection_boxes'],
-                output_dict['detection_classes'],
-                output_dict['detection_scores'],
-                category_index,
-                instance_masks=output_dict.get('detection_masks'),
-                use_normalized_coordinates=True,
-                line_thickness=8,
-                skip_scores=True,
-                skip_labels=True)
-            img = Image.fromarray(image_np.astype("uint8"))
-            img = img.resize((128, 128))
-            raw_bytes = io.BytesIO()
-            img.save(raw_bytes, "JPEG")
-            raw_bytes.seek(0)
-            img_byte = raw_bytes.getvalue()
-            img_str = base64.b64encode(img_byte)
-            data = {
-                "result": is_crack,
-                "img": img_str.decode('utf-8')
-            }
-            return jsonify(data)
-        else:
-            return "Could not find image"
-    return "Please use POST method"
-
-
-@app.route("/segment", methods=["POST"])
 def segment():
     if flask.request.method == "POST":
         if flask.request.files.get("image"):
@@ -176,75 +138,5 @@ def run_inference_for_single_image(image, graph):
     return output_dict
 
 
-@app.route('/', methods=["POST"])
-def index():
-    if flask.request.method == "POST":
-        if flask.request.files.get("image"):
-            img_src = Image.open(flask.request.files["image"])
-            # start crack detection
-            img_segment = prepare_img(img_src, "segment")
-            input_data = np.expand_dims(img_segment, axis=0)
-            input_data = np.float32(input_data) / 255.0
-            tflite_interpreter_c.set_tensor(input_details_c[0]['index'], input_data)
-            tflite_interpreter_c.invoke()
-            result = tflite_interpreter_c.get_tensor(output_details_c[0]['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
-
-            img = Image.fromarray(bg.astype("uint8"))
-
-            # start pothole detection
-            image_np = load_image_into_numpy_array(img_src)
-            # image_np_expanded = np.expand_dims(image_np, axis=0)
-            output_dict = run_inference_for_single_image(image_np, detection_graph)
-            category_index = {0: {"name": "pothole"}, 1: {"name": "pothole"}}
-            _, is_pothole = vis_util.visualize_boxes_and_labels_on_image_array(
-                image_np,
-                output_dict['detection_boxes'],
-                output_dict['detection_classes'],
-                output_dict['detection_scores'],
-                category_index,
-                instance_masks=output_dict.get('detection_masks'),
-                use_normalized_coordinates=True,
-                line_thickness=8,
-                skip_scores=True,
-                skip_labels=True)
-            raw_bytes = io.BytesIO()
-            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_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_discern": img_discern.decode('utf-8')
-            }
-            return jsonify(data)
-        else:
-            data = {
-                "code": 10001,
-                "msg": "Could not find image"
-            }
-            return jsonify(data)
-    return "Road Damage Detection"
-
 if __name__ == "__main__":
     app.run()

build.sh

@@ -1,3 +1,3 @@
 # /usr/bash
 
-docker build --tag hpds-road-detection:1.0.0 .
+docker build --tag hpds-bridge-detection:1.0.0 .

docker-compose.yml

@@ -1,13 +1,13 @@
 version: "3.6"
 services:
-  hpds-python-model:
+  hpds-bridge-detection-model:
-    container_name: hpds-road-detection-model
+    container_name: hpds-bridge-detection-model
-    image: hpds-road-detection:1.0.0 
+    image: hpds-bridge-detection:1.0.0
     networks:
       - hpds-network
     restart: always
     ports:
-      - 8000:5000
+      - "8002:5000"
     volumes:
       - /usr/local/cuda/lib64:/usr/local/cuda/lib64