# See the License for the specific language governing permissions and
# limitations under the License.
#
-
+import os
+import cv2
import config
from flask_sslify import SSLify
-from flask import Flask, Response
+from flask import Flask, request, jsonify, Response, send_file
from flask_cors import CORS
+from werkzeug import secure_filename
+
+app = Flask(__name__)
+CORS(app)
+sslify = SSLify(app)
+app.config['JSON_AS_ASCII'] = False
+app.config['UPLOAD_PATH'] = '/usr/app/images/input/'
+app.config['supports_credentials'] = True
+app.config['CORS_SUPPORTS_CREDENTIALS'] = True
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
+ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
+MODEL_PATH = '/usr/app/model/'
+IMAGE_PATH = '/usr/app/images/result/'
+count = 0
+listOfMsgs = []
class model_info():
def __init__(self, model_name):
- self.model = 'model_info/MobileNetSSD_deploy.caffemodel'
- self.model_name = model_name
- self.prototxt = 'model_info/MobileNetSSD_deploy.prototxt'
- self.confidenceLevel = 80
-
- def get_model(self):
- return self.model
+ self.model_name = 'MobileNetSSD_deploy.caffemodel'
+ self.prototxt = 'MobileNetSSD_deploy.prototxt'
+ self.confidenceLevel = 0.2
def get_prototxt(self):
return self.prototxt
def get_confidence_level(self):
return self.confidenceLevel
- def update_model(self, model, prototxt, model_name):
+ def update_model(self, model_loc, prototxt, model_name):
self.prototxt = prototxt
- self.model = model
self.model_name = model_name
14: 'motorbike', 15: 'person', 16: 'pottedplant',
17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor'}
-app = Flask(__name__)
-CORS(app)
-sslify = SSLify(app)
-app.config['JSON_AS_ASCII'] = False
-app.config['UPLOAD_PATH'] = '/usr/app/images/'
-app.config['supports_credentials'] = True
-app.config['CORS_SUPPORTS_CREDENTIALS'] = True
-app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
-ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
-count = 0
-listOfMsgs = []
-
def allowed_file(filename):
- return '.' in filename and filename.rsplit('.', 1)[1].lower()\
- in ALLOWED_EXTENSIONS
+ return '.' in filename and filename.rsplit('.', 1)[1].lower() \
+ in ALLOWED_EXTENSIONS
+
+
+# Obj-detection from input frame
+def Detection(img):
+ print ('inside detection func')
+ modelInfo = model_info("caffe")
+ ConfPercent = modelInfo.get_confidence_level()
+ model_name = modelInfo.get_model_name()
+ prototxt_name = modelInfo.get_prototxt()
+
+ model = MODEL_PATH + model_name
+ prototxt = MODEL_PATH + prototxt_name
+ image = app.config['UPLOAD_PATH'] + img
+ label = 'bottels'
+ print(ConfPercent)
+ print(model)
+ print(prototxt)
+ print("image path is" + image)
+
+ # Load the Caffe model
+ net = cv2.dnn.readNetFromCaffe(prototxt, model)
+ # Load image fro
+ frame = cv2.imread(image)
+
+ frame_resized = cv2.resize(frame, (300, 300)) # resize frame for
+ # prediction
+ heightFactor = frame.shape[0]/300.0
+ widthFactor = frame.shape[1]/300.0
+
+ # MobileNet requires fixed dimensions for input image(s)
+ # so we have to ensure that it is resized to 300x300 pixels.
+ # set a scale factor to image because network the objects has
+ # differents size.
+ # We perform a mean subtraction (127.5, 127.5, 127.5)
+ # to normalize the input;
+ # after executing this command our "blob" now has the shape:
+ # (1, 3, 300, 300)
+ blob = cv2.dnn.blobFromImage(frame_resized, 0.007843, (300, 300),
+ (127.5, 127.5, 127.5), False)
+ # Set to network the input blob
+ net.setInput(blob)
+ # Prediction of network
+ detections = net.forward()
+
+ frame_copy = frame.copy()
+ # Size of frame resize (300x300)
+ cols = frame_resized.shape[1]
+ rows = frame_resized.shape[0]
+
+ # For get the class and location of object detected,
+ # There is a fix index for class, location and confidence
+ # value in @detections array .
+ for i in range(detections.shape[2]):
+ confidence = detections[0, 0, i, 2] # Confidence of prediction
+ if confidence > ConfPercent: # Filter prediction
+ class_id = int(detections[0, 0, i, 1]) # Class label
+
+ # Object location
+ xLeftBottom = int(detections[0, 0, i, 3] * cols)
+ yLeftBottom = int(detections[0, 0, i, 4] * rows)
+ xRightTop = int(detections[0, 0, i, 5] * cols)
+ yRightTop = int(detections[0, 0, i, 6] * rows)
+
+ xLeftBottom_ = int(widthFactor * xLeftBottom)
+ yLeftBottom_ = int(heightFactor * yLeftBottom)
+ xRightTop_ = int(widthFactor * xRightTop)
+ yRightTop_ = int(heightFactor * yRightTop)
+ # Draw location of object
+ cv2.rectangle(frame_resized, (xLeftBottom, yLeftBottom),
+ (xRightTop, yRightTop),
+ (0, 255, 0))
+
+ cv2.rectangle(frame_copy, (xLeftBottom_, yLeftBottom_),
+ (xRightTop_, yRightTop_),
+ (0, 255, 0), -1)
+ opacity = 0.3
+ cv2.addWeighted(frame_copy, opacity, frame, 1 - opacity, 0, frame)
+
+ count = 0
+ for i in range(detections.shape[2]):
+ confidence = detections[0, 0, i, 2] # Confidence of prediction
+ if confidence > ConfPercent: # Filter prediction
+ class_id = int(detections[0, 0, i, 1]) # Class label
+
+ # Object location
+ xLeftBottom = int(detections[0, 0, i, 3] * cols)
+ yLeftBottom = int(detections[0, 0, i, 4] * rows)
+ xRightTop = int(detections[0, 0, i, 5] * cols)
+ yRightTop = int(detections[0, 0, i, 6] * rows)
+
+ xLeftBottom_ = int(widthFactor * xLeftBottom)
+ yLeftBottom_ = int(heightFactor * yLeftBottom)
+ xRightTop_ = int(widthFactor * xRightTop)
+ yRightTop_ = int(heightFactor * yRightTop)
+ cv2.rectangle(frame, (xLeftBottom_, yLeftBottom_),
+ (xRightTop_, yRightTop_),
+ (0, 0, 0), 2)
+
+ # Draw label and confidence of prediction in frame resized
+ if class_id in classNames:
+ label = classNames[class_id] + ": " + str(confidence)
+ labelSize, baseLine = cv2.getTextSize(label,
+ cv2.FONT_HERSHEY_TRIPLEX,
+ 0.8, 1)
+
+ yLeftBottom_ = max(yLeftBottom_, labelSize[1])
+ cv2.rectangle(
+ frame,
+ (xLeftBottom_, yLeftBottom_ - labelSize[1]),
+ (xLeftBottom_ + labelSize[0], yLeftBottom_ + baseLine),
+ (255, 255, 255), cv2.FILLED)
+ cv2.putText(frame, label, (xLeftBottom_, yLeftBottom_),
+ cv2.FONT_HERSHEY_TRIPLEX, 0.8, (0, 0, 0))
+ print(label)
+ count = count + 1
+
+ print("total item count", count)
+ # cv2.namedWindow("frame", cv2.WINDOW_NORMAL)
+ print("before im write")
+ cv2.imwrite(IMAGE_PATH + "result.jpeg", frame)
+ # cv2.imshow("frame", frame)
+ # cv2.waitKey(0)
+ print("before im before destroy window")
+ # cv2.destroyAllWindows()
+ # Detect_result = {'ImposedImage': 'frame', 'ObjCount': count,
+ # 'ObjType': type, 'Time': time}
+ Detect_result = {'ObjCount': count}
+ print(Detect_result)
+ return Detect_result
@app.route('/mep/v1/obj_detection/uploadModel', methods=['POST'])
upload model
:return: html file
"""
+ app.logger.info("Received message from ClientIP [" + request.remote_addr
+ + "] Operation [" + request.method + "]" +
+ " Resource [" + request.url + "]")
+ modelInfo = model_info("caffe")
+ modelInfo.update_model("caffe", "mobilenet_ssd", "prototext")
return Response("success")
Trigger the video_feed() function on opening "0.0.0.0:5000/video_feed" URL
:return:
"""
+ app.logger.info("Received message from ClientIP [" + request.remote_addr
+ + "] Operation [" + request.method + "]" +
+ " Resource [" + request.url + "]")
+ confidenceLevel = 0.2
+ modelInfo = model_info("caffe")
+ modelInfo.set_confidence_level(confidenceLevel)
return Response("success")
-@app.route('/mep/v1/obj_detection/detect', methods=['GET'])
+@app.route('/mep/v1/obj_detection/detect', methods=['POST'])
def Obj_Detection():
"""
Trigger the Obj detection on input frame/image
Input: frame/image
:return: imposed frame, count, Obj type, time taken by detection
"""
- return Response("success")
+ app.logger.info("Received message from ClientIP [" + request.remote_addr
+ + "] Operation [" + request.method + "]" +
+ " Resource [" + request.url + "]")
+
+ if 'file' not in request.files:
+ raise IOError('No file')
+
+ file = request.files['file']
+ if file.filename == '':
+ app.logger.info('No file selected for uploading')
+ raise IOError('No file')
+ if file and allowed_file(file.filename):
+ filename = secure_filename(file.filename)
+ print('file name', filename)
+ file.save(os.path.join(app.config['UPLOAD_PATH'], filename))
+ app.logger.info('File successfully uploaded')
+ print('file path', app.config['UPLOAD_PATH'] + filename)
+ Detect_result = Detection(filename)
+ else:
+ app.logger.info('Allowed file types are txt, pdf, png, jpg, jpeg, gif')
+ return Response("failure")
+ return jsonify(Detect_result)
+
+
+@app.route('/mep/v1/obj_detection/image', methods=['GET'])
+def image_download():
+ """
+ Trigger the Obj detection on input frame/image
+ Input: frame/image
+ :return: imposed frame, count, Obj type, time taken by detection
+ """
+ app.logger.info("Received message from ClientIP [" + request.remote_addr
+ + "] Operation [" + request.method + "]" +
+ " Resource [" + request.url + "]")
+
+ return send_file(IMAGE_PATH + "result.jpeg",
+ attachment_filename='result.jpeg')
def start_server(handler):
Code Review / ealt-edge.git / blobdiff