Merge pull request #5 from BMW-InnovationLab/dev

Yolov4-v3 OPENCV inference

Author: hadikoub

README.md

@@ -1,10 +1,10 @@
-# YOLO v3 CPU Inference API for Windows and Linux
+# YOLO v4-v3 CPU Inference API for Windows and Linux
 
-This is a repository for an object detection inference API using the Yolov3 Opencv.
+This is a repository for an object detection inference API using the Yolov4 and Yolo v3  Opencv.
 
 The inference REST API works on CPU and doesn't require any GPU usage. It's supported on both Windows and Linux Operating systems.
 
-Models trained using our training Yolov3 repository can be deployed in this API. Several object detection models can be loaded and used at the same time.
+Models trained using our training Yolov4 or Yolov3  repository can be deployed in this API. Several object detection models can be loaded and used at the same time.
 
 ![predict image](./docs/4.gif)
 
@@ -44,13 +44,12 @@ To [install Docker on Windows](https://docs.docker.com/docker-for-windows/instal
 In order to build the project run the following command from the project's root directory:
 
 ```sh
-sudo docker build -t yolov3_inference_api_cpu -f ./docker/dockerfile .
+sudo docker build -t yolov4_inference_api_cpu -f ./docker/dockerfile .
 ```
 ### Behind a proxy
 
 ```sh
-sudo docker build --build-arg http_proxy='' --build-arg https_proxy='' -t yolov3_inference_api_cpu -f ./docker/dockerfile .
-
+sudo docker build --build-arg http_proxy='' --build-arg https_proxy='' -t yolov4_inference_api_cpu -f ./docker/dockerfile .
 ```
 
 ## Run The Docker Container
@@ -60,12 +59,12 @@ To run the API, go the to the API's directory and run the following:
 #### Using Linux based docker:
 
 ```sh
-sudo docker run -itv $(pwd)/models:/models -p <docker_host_port>:7770 yolov3_inference_api_cpu
+sudo docker run -itv $(pwd)/models:/models -p <docker_host_port>:7770 yolov4_inference_api_cpu
 ```
 #### Using Windows based docker:
 
 ```sh
-docker run -itv ${PWD}/models:/models -p <docker_host_port>:7770 yolov3_inference_api_cpu
+docker run -itv ${PWD}/models:/models -p <docker_host_port>:7770 yolov4_inference_api_cpu
 ```
 
 The <docker_host_port> can be any unique port of your choice.
@@ -150,7 +149,7 @@ Inside each subfolder there should be a:
 
   ```json
     {
-      "inference_engine_name": "yolov3_opencv_cpu_detection",
+      "inference_engine_name": "yolov4_opencv_cpu_detection",
       "confidence": 60,
       "nms_threshold": 0.6,
       "image": {
@@ -171,6 +170,9 @@ Inside each subfolder there should be a:
     }
   ```
   P.S
+  
+  - You can choose **"inference_engine_name"**: between **yolov4_opencv_cpu_detection** and **yolov3_opencv_cpu_detection** depending on the model you have.
+  
   - You can change confidence and nms_threshold values while running the API
   - The API will return bounding boxes with a confidence higher than the "confidence" value. A high "confidence" can show you only accurate predictions
 
@@ -180,26 +182,23 @@ Inside each subfolder there should be a:
     <thead align="center">
         <tr>
             <th></th>
-            <th>Windows</th>
             <th colspan=3>Ubuntu</th>
         </tr>
     </thead>
     <thead align="center">
         <tr>
             <th>Network\Hardware</th>
             <th>Intel Xeon CPU 2.3 GHz</th>
-            <th>Intel Xeon CPU 2.3 GHz</th>
             <th>Intel Core i9-7900 3.3 GHZ</th>
-            <th>GeForce GTX 1080</th>
+            <th>Tesla V100</th>
         </tr>
     </thead>
     <tbody align="center">
         <tr>
-            <td>pascalvoc_dataset</td>
-            <td>0.885 seconds/image</td>
-            <td>0.793 seconds/image</td>
-            <td>0.295 seconds/image</td>
-            <td>0.0592 seconds/image</td>
+            <td>COCO Dataset</td>
+            <td>0.259 seconds/image</td>
+            <td>0.281 seconds/image</td>
+            <td>0.0691 seconds/image</td>
         </tr>
     </tbody>
 </table>
@@ -213,3 +212,5 @@ Inside each subfolder there should be a:
 Antoine Charbel, inmind.ai , Beirut, Lebanon
 
 Daniel Anani, inmind.ai, Beirut, Lebanon
+
+Hadi Koubeissy, Beirut, Lebanon

docker/dockerfile

@@ -3,11 +3,51 @@ FROM python:3.6
 LABEL maintainer="[email protected]"
 
 COPY docker/requirements.txt .
+ENV HOME /root
 
 COPY src/main /main
 
+RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get -y --no-install-recommends install \
+	locales \
+        wget \
+        software-properties-common \
+        ca-certificates \
+        build-essential \
+        cmake \
+        git \
+        libopencv-dev \
+        python3-dev \
+        python3-pip \
+        libgtk2.0-dev\
+        pkg-config\
+    && apt-get -y autoremove \
+    && apt-get clean \
+    && pip3 install setuptools wheel \
+    && rm -rf /var/lib/apt/lists/{apt,dpkg,cache,log} /tmp/* /var/tmp/*
+
 RUN pip install -r requirements.txt
 
-WORKDIR /main
 
+WORKDIR ${HOME}
+#Checkout version should be 4.4.0 when the new version is released 
+RUN git clone http://github.com/opencv/opencv.git && cd opencv \
+    && git checkout 4.4.0    \
+    && mkdir build && cd build              \
+    && cmake -D CMAKE_BUILD_TYPE=RELEASE    \
+        -D CMAKE_INSTALL_PREFIX=/usr/local  \
+        -D WITH_CUDA=OFF                     \
+        -D WITH_OPENCL=OFF                  \
+        -D ENABLE_FAST_MATH=1               \
+        -D CUDA_FAST_MATH=1                 \
+        -D WITH_CUBLAS=1                    \
+        -D BUILD_DOCS=OFF                   \
+        -D BUILD_PERF_TESTS=OFF             \
+        -D BUILD_TESTS=OFF                  \
+        ..                                  \
+    && make -j `nproc`                      \
+    && make install                         \
+    && cd ${HOME} && rm -rf ./opencv/
+
+
+WORKDIR /main
 CMD ["uvicorn", "start:app", "--host", "0.0.0.0", "--port", "7770"]

docs/1.gif

@@ -130,6 +130,8 @@ def get_labels(self, model_name):
         :param model_name: Model name
         :return: List of model labels
         """
+        if model_name not in self.models_hash_dict and model_name not in self.models_hash_dict.values():
+            raise ModelNotFound
         if not self.model_loaded(model_name):
             self.load_model(model_name)
         return self.models_dict[model_name].labels
@@ -140,6 +142,8 @@ def get_labels_custom(self, model_name):
         :param model_name: Model name
         :return: A list of mode's labels with their hashed values
         """
+        if model_name not in self.models_hash_dict and model_name not in self.models_hash_dict.values():
+            raise ModelNotFound 
         if re.match(r'[0-9a-fA-F]{8}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{4}\-[0-9a-fA-F]{12}', model_name,
                     flags=0):
             for key, value in self.models_hash_dict.items():
@@ -162,6 +166,8 @@ def get_config(self, model_name):
         :param model_name: Model name
         :return: List of model's configuration
         """
+        if model_name not in self.models_hash_dict and model_name not in self.models_hash_dict.values():
+            raise ModelNotFound 
         if not self.model_loaded(model_name):
             self.load_model(model_name)
         return self.models_dict[model_name].configuration

docs/2.gif

@@ -26,7 +26,8 @@ def get_engine(path_to_model):
 		try:
 			# import one of the available inference engine class (in this project there's only one), and return a
 			# model instance
-			return getattr(__import__(inference_engine_name), 'InferenceEngine')(path_to_model)
+			if inference_engine_name=='yolov3_opencv_cpu_detection' or inference_engine_name=='yolov4_opencv_cpu_detection':
+				return getattr(__import__("yolo_opencv_cpu_detection"), 'InferenceEngine')(path_to_model)
 		except ApplicationError as e:
 			raise e
 		except Exception as e:

docs/3.gif

@@ -36,7 +36,7 @@ def load(self):
 		with open(os.path.join(self.model_path, 'obj.names'), 'r') as f:
 			self.labels = [line.strip() for line in f.readlines()]
 		self.net = cv2.dnn.readNet(os.path.join(self.model_path, 'yolo-obj.cfg'),
-								   os.path.join(self.model_path, 'yolo-obj.weights'))
+								   os.path.join(self.model_path, 'yolo-obj.weights'),'darknet')
 
 	async def infer(self, input_data, draw, predict_batch):
 		await asyncio.sleep(0.00001)
@@ -55,10 +55,10 @@ async def infer(self, input_data, draw, predict_batch):
 		height, width, depth = np_image.shape
 		# create input blob
 		blob = cv2.dnn.blobFromImage(
-			np_image, self.scale, (self.image_width, self.image_height), (self.R_mean, self.G_mean, self.B_mean),
-			self.swapRB, self.crop)
+			np_image, size=(self.image_width, self.image_height), swapRB=self.swapRB, ddepth=cv2.CV_8U)
+
 		# feed the blob to the network
-		self.net.setInput(blob)
+		self.net.setInput(blob, scalefactor=self.scale, mean=[self.R_mean,self.G_mean,self.B_mean])
 		# get the output layers
 		output_layers = self.net.forward(self.__get_output_layers__())
 		# for each detection from each output layer
@@ -87,13 +87,7 @@ async def infer(self, input_data, draw, predict_batch):
 		remaining_indices = cv2.dnn.NMSBoxes(
 			boxes, confidences, conf_threshold, nms_threshold)
 
-		for i in range(len(boxes)):
-			# i = i[0]
-			box = boxes[i]
-			x = box[0]
-			y = box[1]
-			w = box[2]
-			h = box[3]
+		
 
 		# release resources
 		cv2.destroyAllWindows()
@@ -115,7 +109,7 @@ async def infer(self, input_data, draw, predict_batch):
 
 			if (left < 0):
 				left = 0
-			if (right > height- 1):
+			if (right > width- 1):
 				right = width - 1
 			if (top < 0):
 				top = 0