Merge pull request #1 from UPstartDeveloper/dev

Rm extraneous packages from requirements.txt, edit .gitignore and add…

Author: UPstartDeveloper

.gitignore

@@ -6,6 +6,10 @@ __pycache__/
 # C extensions
 *.so
 
+# OS and Editor stuff
+.DS_Store
+.vscode
+
 # Distribution / packaging
 .Python
 build/

Dockerfile

@@ -5,7 +5,7 @@ WORKDIR /deploy/
 RUN apt update
 RUN apt install -y git
 RUN apt-get install -y libglib2.0-0
-RUN pip install git+https://github.com/CVxTz/FastImageClassification
+RUN pip install git+https://github.com/UPstartDeveloper/Fire-Detection-API
 EXPOSE 8080
 
 ENTRYPOINT uvicorn main:app --host 0.0.0.0 --port 8080 --workers 1

app/config.yaml

@@ -1,5 +1,5 @@
-resize_shape: [224, 224]
-targets: ['beach', 'city', 'sunset', 'trees']
-model_path: "image_classification.h5"
-model_url: "https://github.com/CVxTz/FastImageClassification/releases/download/v0.1/image_classification.h5"
-model_sha256: "d5cd9082651faa826cab4562f60e3095502286b5ea64d5b25ba3682b66fbc305"
+resize_shape: [256, 256]
+targets: ['Fire_Images']
+model_path: "fire_classifier.h5"
+model_url: "https://github.com/UPstartDeveloper/Fire-Detection-API/releases/download/v0.0.1/fire_classifier.h5"
+model_sha256: "b527ecea0f4786e45eb0242f263752c45fb55754"

app/main.py

@@ -1,10 +1,10 @@
 from fastapi import FastAPI, File, UploadFile
 
-from fast_image_classification.predictor import ImagePredictor
+from fire_classifier.predictor import ImagePredictor
 
 app = FastAPI()
 
-predictor_config_path = "config.yaml"
+predictor_config_path = "./app/config.yaml"
 
 predictor = ImagePredictor.init_from_config_url(predictor_config_path)
 

fire_classifier/__init__.py

@@ -0,0 +1,73 @@
+import argparse
+
+import numpy as np
+import yaml
+from tensorflow.keras.models import load_model
+
+from fire_classifier.preprocessing_utilities import (
+    read_img_from_path,
+    read_from_file,
+)
+from fire_classifier.utils import download_model
+
+
+class ImagePredictor:
+    def __init__(self, model_path, resize_size, targets):
+        self.model_path = model_path
+        self.model = load_model(self.model_path)
+        self.resize_size = resize_size
+        self.targets = targets
+
+    @classmethod
+    def init_from_config_path(cls, config_path):
+        with open(config_path, "r") as f:
+            config = yaml.load(f, yaml.SafeLoader)
+        predictor = cls(
+            model_path=config["model_path"],
+            resize_size=config["resize_shape"],
+            targets=config["targets"],
+        )
+        return predictor
+
+    @classmethod
+    def init_from_config_url(cls, config_path):
+        with open(config_path, "r") as f:
+            config = yaml.load(f, yaml.SafeLoader)
+
+        download_model(
+            config["model_url"], config["model_path"], config["model_sha256"]
+        )
+
+        return cls.init_from_config_path(config_path)
+
+    def predict_from_array(self, arr):
+        pred = self.model.predict(arr[np.newaxis, ...]).ravel().tolist()
+        pred = [round(x, 3) for x in pred]
+        return {k: v for k, v in zip(self.targets, pred)}
+
+    def predict_from_path(self, path):
+        arr = read_img_from_path(path)
+        return self.predict_from_array(arr)
+
+    def predict_from_file(self, file_object):
+        arr = read_from_file(file_object)
+        return self.predict_from_array(arr)
+
+
+if __name__ == "__main__":
+    """
+    python predictor.py --predictor_config "../example/predictor_config.yaml"
+
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--predictor_config_path",
+        help="predictor_config_path",
+        default="../example/predictor_config.yaml",
+    )
+
+    args = parser.parse_args()
+
+    predictor_config_path = args.predictor_config_path
+
+    predictor = ImagePredictor.init_from_config_path(predictor_config_path)

fire_classifier/predictor.py

@@ -0,0 +1,14 @@
+import cv2
+import numpy as np
+
+
+def read_img_from_path(path):
+    img = cv2.imread(path, cv2.IMREAD_COLOR)
+    return img
+
+
+def read_from_file(file_object):
+    arr = np.fromstring(file_object.read(), np.uint8)
+    img_np = cv2.imdecode(arr, cv2.IMREAD_COLOR)
+
+    return img_np

fire_classifier/preprocessing_utilities.py

@@ -0,0 +1,19 @@
+import hashlib
+import os
+from tensorflow.keras.utils import get_file
+
+
+def get_hash(filename):
+    sha256_hash = hashlib.sha256()
+    with open(filename, "rb") as f:
+        for byte_block in iter(lambda: f.read(4096), b""):
+            sha256_hash.update(byte_block)
+
+    return sha256_hash.hexdigest()
+
+
+def download_model(url, file_path, file_sha256):
+    if os.path.exists(file_path) and get_hash(file_path) == file_sha256:
+        print("File already exists")
+    else:
+        get_file(origin=url, fname=file_path, cache_dir=".", cache_subdir="")

fire_classifier/utils.py

@@ -1,10 +1,7 @@
-opencv_python==4.1.1.26
-numpy==1.17.3
-tensorflow==2.0.1
-imgaug==0.3.0
-pandas==0.25.2
+numpy
+tensorflow
 fastapi==0.49.0
+opencv-python==4.5.3.56
 PyYAML==5.3
-scikit_learn==0.22.2
 uvicorn
 python-multipart