feat: add mask template deployments

Author: CatchZeng

Makefile

@@ -27,6 +27,7 @@ workspace-box:
 workspace-mask:
 	python scripts/workspace/mask/workspace.py --save_dir=$(SAVE_DIR) --name=$(NAME)
 	cp -r scripts/workspace/mask/files/* $(SAVE_DIR)/$(NAME)
+	python scripts/workspace/mask/replace.py --dir=$(SAVE_DIR)/$(NAME)/deployments --pattern="{{.Name}}" --repl=$(NAME)
 	cp models/research/object_detection/model_main_tf2.py $(SAVE_DIR)/$(NAME)
 	cp models/research/object_detection/exporter_main_v2.py $(SAVE_DIR)/$(NAME)
 	touch $(SAVE_DIR)/$(NAME)/annotations/label_map.pbtxt

scripts/workspace/box/files/Makefile

@@ -1,10 +1,10 @@
 gen-tfrecord:
-	python ../../scripts/preprocessing/generate_tfrecord.py \
+	python generate_tfrecord.py \
 	-x images/train \
 	-l annotations/label_map.pbtxt \
 	-o annotations/train.record
 
-	python ../../scripts/preprocessing/generate_tfrecord.py \
+	python generate_tfrecord.py \
 	-x images/val \
 	-l annotations/label_map.pbtxt \
 	-o annotations/val.record

scripts/preprocessing/generate_tfrecord.py renamed to scripts/workspace/box/files/generate_tfrecord.py

@@ -0,0 +1,10 @@
+FROM python:3.8-slim-buster
+
+WORKDIR /app
+
+RUN pip3 install --no-cache-dir Flask pillow tensorflow==2.6.0
+
+COPY . .
+RUN rm -rf .vscode README.md Dockerfile Makefile
+
+CMD [ "python3", "-m" , "flask", "run", "--host=0.0.0.0"]

scripts/workspace/mask/files/deployments/flask/Dockerfile

@@ -0,0 +1,2 @@
+build:
+	docker build --tag {{.Name}}-flask .

scripts/workspace/mask/files/deployments/flask/Makefile

@@ -0,0 +1,29 @@
+from enum import Enum
+import json
+
+
+class Response:
+    def __init__(self, code, message, data=''):
+        self.code = code
+        self.data = data
+        self.message = message
+
+
+class Error(Enum):
+    NO_FILE_PART = Response(1000, "No image file part")
+    NO_SELECTED_FILE = Response(1001, "No selected file")
+    NO_ALLOWED_FILE = Response(1002, "No allowed file")
+
+
+def response_data(message, data=''):
+    res = Response(0, message, data)
+    return jsonify(res)
+
+
+def response_err(e):
+    res = e.value
+    return jsonify(res)
+
+
+def jsonify(r):
+    return json.dumps(r.__dict__, ensure_ascii=False)

scripts/workspace/mask/files/deployments/flask/api.py

@@ -0,0 +1,42 @@
+from flask import Flask, request, url_for
+import os
+from api import response_data, response_err, Error
+from file import allowed_file, make_upload_dir, uuid_filename
+from model import predict_image
+
+app = Flask(__name__)
+app.config['JSON_AS_ASCII'] = False
+app.config['UPLOAD_FOLDER'] = make_upload_dir()
+
+SERVER_URL = os.getenv('SERVER_URL')
+print('SERVER_URL:' + SERVER_URL)
+
+
+@app.route('/{{.Name}}/v1/predict', methods=['POST'])
+def predict():
+    if 'image' not in request.files:
+        return response_err(Error.NO_FILE_PART)
+
+    f = request.files['image']
+    if f.filename == '':
+        return response_err(Error.NO_SELECTED_FILE)
+
+    if not allowed_file(f.filename):
+        return response_err(Error.NO_ALLOWED_FILE)
+
+    filename = uuid_filename(f.filename)
+    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+    f.save(file_path)
+
+    predict_image(SERVER_URL, file_path, filename)
+
+    url = url_for('static', filename=filename)
+
+    return response_data('predict successfully', {
+        "image_url": url,
+    })
+
+
+@app.route("/")
+def hello_world():
+    return "It works!"

scripts/workspace/mask/files/deployments/flask/app.py

@@ -0,0 +1,42 @@
+import os
+import uuid
+from werkzeug.utils import secure_filename
+
+# https://developer.mozilla.org/en-US/docs/Web/Media/Formats/Image_types
+ALLOWED_EXTENSIONS = {
+    'apng', 'avif', 'jpg', 'jpeg', 'jfif', 'pjpeg', 'pjp', 'png', 'svg',
+    'webp', 'bmp', 'ico', 'cur', 'tif', 'tiff'
+}
+
+
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+def make_upload_dir(subdir='uploads'):
+    return make_dir(subdir)
+
+
+def make_save_dir(subdir='static'):
+    return make_dir(subdir)
+
+
+def make_dir(subdir):
+    basepath = os.path.dirname(__file__)
+    dir = os.path.join(basepath, subdir)
+    if not os.path.exists(dir):
+        os.makedirs(dir)
+    return dir
+
+
+def file_extension(filename):
+    _, extension = os.path.splitext(filename)
+    return extension
+
+
+def uuid_filename(filename):
+    extension = file_extension(filename)
+    uuid_name = uuid.uuid4().hex + extension
+    uuid_name = secure_filename(uuid_name)
+    return uuid_name

scripts/workspace/mask/files/deployments/flask/file.py

@@ -0,0 +1,88 @@
+import requests
+import os
+from PIL import Image
+import numpy as np
+import time
+import tensorflow as tf
+from object_detection.utils import label_map_util
+from object_detection.utils import ops as utils_ops
+from object_detection.utils import visualization_utils as viz_utils
+
+from file import make_save_dir
+
+SERVER_URL = 'http://localhost:8501/v1/models/{{.Name}}:predict'
+
+# Labels
+BASEPATH = os.path.dirname(os.path.realpath(__file__))
+PATH_TO_LABELS = os.path.join(BASEPATH, 'label_map.pbtxt')
+category_index = label_map_util.create_category_index_from_labelmap(
+    PATH_TO_LABELS, use_display_name=True)
+
+
+def predict_image(server_url, file_path, file_name):
+    image = Image.open(file_path)
+    image_np = np.array(image)
+
+    detections = request(image_np, server_url)
+
+    image_np_with_detections = visualize(image_np, detections)
+
+    save_path = os.path.join(make_save_dir(), file_name)
+    im = Image.fromarray(image_np_with_detections)
+    im.save(save_path)
+
+    return save_path
+
+
+def request(image_np, server_url=SERVER_URL):
+    payload = {"inputs": [image_np.tolist()]}
+    headers = {"content-type": "application/json"}
+    start = time.time()
+    res = requests.post(server_url, json=payload, headers=headers)
+    end = time.time()
+    print('duration: %.2fs' % (end - start))
+    json = res.json()
+    detections = json['outputs']
+    return detections
+
+
+def visualize(image_np, detections):
+    image_np_with_detections = image_np.copy()
+
+    # https://github.com/vijaydwivedi75/Custom-Mask-RCNN_TF/blob/master/mask_rcnn_eval.ipynb
+    # The following processing is only for single image
+    detection_boxes = tf.squeeze(detections['detection_boxes'], [0])
+    detection_masks = tf.squeeze(detections['detection_masks'], [0])
+    # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
+    real_num_detection = tf.cast(detections['num_detections'][0], tf.int32)
+    detection_boxes = tf.slice(detection_boxes, [0, 0],
+                               [real_num_detection, -1])
+    detection_masks = tf.slice(detection_masks, [0, 0, 0],
+                               [real_num_detection, -1, -1])
+    detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
+        detection_masks, detection_boxes, image_np.shape[0], image_np.shape[1])
+    detection_masks_reframed = tf.cast(
+        tf.greater(detection_masks_reframed, 0.5), tf.uint8)
+    # Follow the convention by adding back the batch dimension
+    detections['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)
+
+    detections['num_detections'] = int(detections['num_detections'][0])
+    detections['detection_classes'] = np.array(
+        detections['detection_classes'][0], dtype=np.uint8)
+    detections['detection_boxes'] = np.array(detections['detection_boxes'][0])
+    detections['detection_scores'] = np.array(
+        detections['detection_scores'][0])
+    detections['detection_masks'] = detections['detection_masks'][0].numpy()
+
+    viz_utils.visualize_boxes_and_labels_on_image_array(
+        image_np_with_detections,
+        detections['detection_boxes'],
+        detections['detection_classes'],
+        detections['detection_scores'],
+        category_index,
+        instance_masks=detections.get('detection_masks'),
+        use_normalized_coordinates=True,
+        max_boxes_to_draw=200,
+        min_score_thresh=.30)
+
+    return image_np_with_detections

scripts/workspace/mask/files/deployments/flask/model.py

@@ -0,0 +1,14 @@
+server {
+    listen 80;
+    listen 443 ssl;
+    server_name api.makeoptim.com;
+    ssl_certificate      /home/lindaoqiang/keys/api.makeoptim.com.pem;
+    ssl_certificate_key  /home/lindaoqiang/keys/api.makeoptim.com.key;
+
+    location / {
+            proxy_set_header Host $host;
+            proxy_set_header X-Real-IP $remote_addr;
+            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+            proxy_pass http://127.0.0.1:5000/;
+    }
+}