Merge remote-tracking branch 'origin/yigit' into efe

client_side/interface/UserData/complaints.html

@@ -206,7 +206,7 @@
             document.getElementById("trust").innerHTML = users[id]["trustability"];
             document.getElementById("phone").innerHTML = users[id]["tel"];
             document.getElementById("email").innerHTML = users[id]["email"];
-            document.getElementById("img-user").setAttribute("src","https://" + document.domain + ":5000" + users[id]["avatar"])
+            document.getElementById("img-user").setAttribute("src","data:image/png;base64," + users[id]["image"])
         }
     }
     function sendUpdate(form) {

client_side/interface/UserData/crashes.html

@@ -21,21 +21,6 @@
 
     <style>
 
-        #img:hover{
-            color: #424242;
-            -webkit-transition: all .3s ease-in;
-            -moz-transition: all .3s ease-in;
-            -ms-transition: all .3s ease-in;
-            -o-transition: all .3s ease-in;
-            transition: all .3s ease-in;
-            opacity: 1;
-            transform: scale(1.15);
-            -ms-transform: scale(1.15); /* IE 9 */
-            -webkit-transform: scale(1.15); /* Safari and Chrome */
-            max-width: 400px;
-            max-height: 400px;
-        }
-
         #img-user{
             max-width: 150px;
             max-height: 150px;
@@ -69,7 +54,6 @@
 
         #content, #user-content{
             display: inline-block;
-            margin-top: 125px;
             background: white;
         }
         #submit{
@@ -95,18 +79,16 @@
 <div id = "popup" align = "center" class = "popup">
 
     <div id = "content">
-
-        <form action="." id="submission_form">
-
-            <table border="0" cellpadding="10" cellspacing="0" id = "table" class = "modal">
+        <canvas id="canvas" width="400" height="400" style="margin-top: 30px;">
+        </canvas>
+        <table border="0" cellpadding="10" cellspacing="0" id = "table" class = "modal">
                 <tr>
-                    <td rowspan="2" colspan="7"><img src="cityscape.png" alt="img"id = "img"></td>
-                    <td colspan="2" class="title"><strong>Priority: </strong></td>
-                    <td colspan="1" id = "priority">7</td>
+                    <td colspan="4" class="title"><strong>Priority: </strong></td>
+                    <td colspan="6" id = "priority">7</td>
                 </tr>
                 <tr>
-                    <td colspan="2" class="title"><strong>Type: </strong></td>
-                    <td colspan="1" id = "type"> Ambulance</td>
+                    <td colspan="4" class="title"><strong>Type: </strong></td>
+                    <td colspan="6" id = "type"> Ambulance</td>
                 </tr>
                 <tr>
                     <td colspan="4" class="title"><strong>Submitter: </strong></td>
@@ -127,18 +109,9 @@
                     <td colspan="4" class="title"><strong>Tags: </strong></td>
                     <td colspan="6" id = "tags">Test tags</td>
                 </tr>
-                <tr>
-                    <td colspan="4" class="title"><strong>Message: </strong></td>
-                    <td colspan="6" id = "input">  <textarea name="message" rows="5" cols="30" id="response">Please enter response.</textarea>
-                    </td>
-                </tr>
-                <tr>
-                    <td colspan="10">
-                        <input type="submit" id = "submit">
-                    </td>
-                </tr>
+
             </table>
-        </form>
+
 
     </div>
 
@@ -206,7 +179,7 @@
             document.getElementById("trust").innerHTML = users[id]["trustability"];
             document.getElementById("phone").innerHTML = users[id]["tel"];
             document.getElementById("email").innerHTML = users[id]["email"];
-            document.getElementById("img-user").setAttribute("src","https://" + document.domain + ":5000" + users[id]["avatar"])
+            document.getElementById("img-user").setAttribute("src","data:image/png;base64," + users[id]["image"])
         }
     }
 </script>
@@ -232,6 +205,7 @@
 </script>
 
 <script>
+    var class_color_dict = {"person":"blue","car":"yellow"}
     var sound = document.getElementById("audio");
     var markers = [];
 
@@ -254,32 +228,22 @@
         var data = $.parseJSON(dataStr);
         console.log(data);
         for(var key in data) {
-            console.log("aaa aaa");
             data[key].forEach(function (element,index) {
-                console.log("aaa bbb");
                 if(element["location"]["longitude"] === undefined || element["location"]["latitude"] === undefined){
                     return;
                 }
-                console.log("aaa ccc");
                 LatLng = {lat: parseFloat(element["location"]["latitude"]), lng: parseFloat(element["location"]["longitude"])};
-                console.log("aaa ddd");
                 var marker = new google.maps.Marker({
                     position: {lat: parseFloat(element["location"]["latitude"]), lng: parseFloat(element["location"]["longitude"])},
                     map: map,
                     label: element["priority"].toString(),
                     title: element["message"]
                 });
-                console.log("aaa eee");
                 marker.addListener('click', function () {
                     document.getElementById('popup').style.display = 'block';
-                    document.getElementById("priority").innerHTML = element["response"]["priority"];
-                    if(element["response"]["status"]){
-                        document.getElementById("response").innerHTML = element["response"]["message"];
-                    }
-                    document.getElementById("submission_form").setAttribute("index",index);
-                    document.getElementById("submission_form").setAttribute("id",key);
-                    document.getElementById("type").innerHTML = "Road Damage";
-                    document.getElementById("message").innerHTML = element["content"];
+                    document.getElementById("priority").innerHTML = element["priority"];
+                    document.getElementById("type").innerHTML = "Car Crash";
+                    document.getElementById("message").innerHTML = element["message"];
 
                     if (!(key in users)) {
                         $.get("https://" + document.domain + ":5000/users/" + key, function (dataStr, status) {
@@ -291,7 +255,57 @@
                         document.getElementById("user").innerHTML = users[key]["realname"];
                     }
                     document.getElementById("user").setAttribute("userid", key);
-                    document.getElementById("img").setAttribute("src", "data:image/png;base64," + element["img"])
+                    var canvas = document.getElementById("canvas");
+                    var ctx = canvas.getContext("2d");
+                    var image = new Image();
+                    var rects = []
+                    image.onload = function() {
+                        if(image.height > image.width){
+                            XY_RATIO = canvas.height/image.height
+                            canvas.width = image.width * XY_RATIO
+                        }else{
+                            XY_RATIO = canvas.width/image.width
+                            canvas.height = image.height * XY_RATIO
+                        }
+                        ctx.drawImage(image, 0, 0, image.width,    image.height,     // source rectangle
+                            0, 0, canvas.width, canvas.height);
+                        for(i=0;i<element["output_dict"]["detection_classes"].length;i++){
+                            var box = element["output_dict"]["detection_boxes"][i];
+                            ctx.beginPath();
+                            ctx.lineWidth = "2";
+                            var box_class = element["output_dict"]["detection_classes"][i];
+                            ctx.strokeStyle = class_color_dict[box_class];
+                            ctx.rect(box[0]*XY_RATIO,box[2]*XY_RATIO,Math.abs(box[1]-box[0])*XY_RATIO,Math.abs(box[3]-box[2])*XY_RATIO);
+                            ctx.stroke()
+                        }
+
+                        for(i=0;i<element["people"].length;i++){
+                            ctx.beginPath();
+                            var person = element["people"][i]
+                            box = person[1]
+                            ctx.strokeStyle = "green";
+                            if(person[0]){
+                                ctx.strokeStyle = "red";
+                            }
+                            ctx.rect(box[3]*XY_RATIO,box[0]*XY_RATIO,(box[1]-box[3])*XY_RATIO,(box[0]-box[2])*XY_RATIO);
+
+                            ctx.stroke();
+                            rects.push({"x":box[3]*XY_RATIO,"y":box[0]*XY_RATIO,"w":(box[1]-box[3])*XY_RATIO,"h":(box[0]-box[2])*XY_RATIO,"id":person[2]})
+                        }
+
+                    };
+
+                    image.src = "data:image/png;base64," + element["img"]
+                    canvas.addEventListener('click', function(e) {
+                        console.log('click: ' + e.offsetX + '/' + e.offsetY);
+                        console.log(rects)
+                        var rect = collides(rects, e.offsetX, e.offsetY);
+                        if (rect) {
+                            document.getElementById('popup').style.display='none';
+                            document.getElementById('user-popup').style.display='block';
+                            setUser(rect.id);
+                        }
+                    }, false);
                 });
 
             });
@@ -303,7 +317,20 @@
         }
         denunciation_count = markers.length;
     });
-
+    function collides(rects, x, y) {
+        var isCollision = false;
+        for (var i = 0, len = rects.length; i < len; i++) {
+            var left = rects[i].x, right = rects[i].x+rects[i].w;
+            var top = rects[i].y, bottom = rects[i].y+rects[i].h;
+            if (right >= x
+                && left <= x
+                && bottom >= y
+                && top <= y) {
+                isCollision = rects[i];
+            }
+        }
+        return isCollision;
+    }
 </script>
 
 <script src="https://maps.googleapis.com/maps/api/js?key=AIzaSyBuOC03IHPA_6TPnfk18b0SAgD1uge4-dk&callback=initMap" async defer></script>

server_side/api/modules/SpotSelector.py

@@ -48,7 +48,7 @@ for i in range(len(rects)):
 	}
 
 with open("databases/locations.json","w") as f:
-	f.write(json.dumps(locs,indent=4))
+	f.write(json.dumps(locs,indent=2))
 
 
 

server_side/api/modules/car_crash.py

@@ -20,6 +20,8 @@ import ssl
 from object_detection.utils import label_map_util
 import face_recognition
 
+AI_IP = '10.10.26.161'
+
 VEHICLE_CLASSES = [3, 6, 8]
 MIN_AREA_RATIO = 0.9
 import numpy as np
@@ -27,7 +29,7 @@ import numpy as np
 MIN_SCORE_THRESH = 0.6
 
 if sys.platform == "win32":
-    sys.path.insert(0, r'C:\Users\Tednokent01\Downloads\MyCity\traffic_analyzer')
+	sys.path.insert(0, r'C:\Users\Tednokent01\Downloads\MyCity\traffic_analyzer')
 
 PATH_TO_LABELS = os.path.join('object_detection/data', 'mscoco_label_map.pbtxt')
 
@@ -39,38 +41,38 @@ api = Api(app)
 
 db_path = os.path.join(app.root_path, 'databases', 'crashes.json')
 with open(db_path, 'r') as f:
-    crashes = json.load(f)
+	crashes = json.load(f)
 
 users_path = os.path.join(app.root_path, 'databases', 'users.json')
 with open(users_path, 'r') as f:
-    users = json.load(f)
+	users = json.load(f)
 
 def load_image_into_numpy_array(image):
-    (im_width, im_height) = image.size
-    return np.array(image.getdata()).reshape(
-            (im_height, im_width, 3)).astype(np.uint8)
+	(im_width, im_height) = image.size
+	return np.array(image.getdata()).reshape(
+			(im_height, im_width, 3)).astype(np.uint8)
 
 context = ssl._create_unverified_context()
 
 def find_name(image):
-    try:
-        known_faces = []
-        known_face_names = []
-        for v in users.values():
-            known_faces.append(np.array(v['face_encoding']))
-            known_face_names.append(v['id'])
-
-        face_encoding = face_recognition.face_encodings(image)[0]
-        results = face_recognition.compare_faces(known_faces, face_encoding)
-        name = "Unknown"
-        face_distances = face_recognition.face_distance(known_faces, face_encoding)
-        best_match_index = np.argmin(face_distances)
-        if results[best_match_index]:
-            name = known_face_names[best_match_index]
-
-        return name
-    except:
-        return None
+	try:
+		known_faces = []
+		known_face_names = []
+		for v in users.values():
+			known_faces.append(np.array(v['face_encoding']))
+			known_face_names.append(v['id'])
+
+		face_encoding = face_recognition.face_encodings(image)[0]
+		results = face_recognition.compare_faces(known_faces, face_encoding)
+		name = "Unknown"
+		face_distances = face_recognition.face_distance(known_faces, face_encoding)
+		best_match_index = np.argmin(face_distances)
+		if results[best_match_index]:
+			name = known_face_names[best_match_index]
+
+		return name
+	except:
+		return None
 
 def rotate_img(img, angle):
     if angle == 90:
@@ -86,34 +88,33 @@ def process_img(img_base64):
     output_dict = json.loads(json.loads(data))
     image_np = cv2.cvtColor(load_image_into_numpy_array(Image.open(io.BytesIO(base64.b64decode(img_base64)))),cv2.COLOR_RGB2BGR)
 
-    output_dict_processed = {"detection_classes":[], "detection_scores":[], "detection_boxes":[]}
-    im_height, im_width, _ = image_np.shape
-    cars_involved = 0
-    injured_people = 0
-    prev_cars = []
-    boxes = []
-
-    spam_boxes = []
-    for index, i in enumerate(output_dict['detection_classes']):
-        score = output_dict['detection_scores'][index]
-        if score > MIN_SCORE_THRESH:
-            if i in VEHICLE_CLASSES:
-                box = output_dict['detection_boxes'][index]
-                boxes.append(Box((box[1] * im_width, box[3] * im_width,
-                                  box[0] * im_height, box[2] * im_height),
-                                  i,index))
-    box_combinations = itertools.combinations(boxes,r=2)
-    for combination in box_combinations:
-        big = combination[0].get_bigger(combination[1])
-        if big and not big in spam_boxes:
-            spam_boxes.append(big)
-    for spam in spam_boxes:
-        boxes.remove(spam)
-
-    for box in boxes:
-        output_dict_processed["detection_classes"].append(output_dict["detection_classes"][box.index])
-        output_dict_processed["detection_scores"].append(output_dict["detection_scores"][box.index])
-        output_dict_processed["detection_boxes"].append(output_dict["detection_boxes"][box.index])
+	output_dict_processed = {"detection_classes":[], "detection_scores":[], "detection_boxes":[]}
+	im_height, im_width, _ = image_np.shape
+	cars_involved = 0
+	injured_people = 0
+	prev_cars = []
+	boxes = []
+
+	spam_boxes = []
+	for index, i in enumerate(output_dict['detection_classes']):
+		score = output_dict['detection_scores'][index]
+		if score > MIN_SCORE_THRESH:
+			box = output_dict['detection_boxes'][index]
+			boxes.append(Box((box[1] * im_width, box[3] * im_width,
+							  box[0] * im_height, box[2] * im_height),
+							 i,index))
+	box_combinations = itertools.combinations(boxes,r=2)
+	for combination in box_combinations:
+		big = combination[0].get_bigger(combination[1])
+		if big and not big in spam_boxes:
+			spam_boxes.append(big)
+	for spam in spam_boxes:
+		boxes.remove(spam)
+
+	for box in boxes:
+		output_dict_processed["detection_classes"].append(output_dict["detection_classes"][box.index])
+		output_dict_processed["detection_scores"].append(output_dict["detection_scores"][box.index])
+		output_dict_processed["detection_boxes"].append(output_dict["detection_boxes"][box.index])
 
     people = {}
     for index, i in enumerate(output_dict['detection_classes']):
@@ -169,32 +170,22 @@ def process_img(img_base64):
 
 
 
-    _, buffer = cv2.imencode('.jpg', image_np)
-#    image_process = image_np[:]
-#    vis_util.visualize_boxes_and_labels_on_image_array(
-#            image_process,
-#            output_dict_processed["detection_boxes"],
-#            output_dict_processed["detection_classes"],
-#            output_dict_processed["detection_scores"],
-#            category_index,
-#            min_score_thresh=MIN_SCORE_THRESH,
-#            use_normalized_coordinates=True,
-#            line_thickness=8)
-#    cv2.imshow("a",image_process)
-#    cv2.waitKey(0)
+	_, buffer = cv2.imencode('.jpg', image_np)
+	for i in range(len(output_dict_processed["detection_classes"])):
+		box = output_dict_processed["detection_boxes"][i]
+		output_dict_processed["detection_boxes"][i] = [box[1] * im_width, box[3] * im_width, box[0] * im_height, box[2] * im_height]
 
-    for i in range(len(output_dict_processed["detection_classes"])):
-        output_dict_processed["detection_classes"][i] = category_index[output_dict_processed["detection_classes"][i]]["name"]
+		output_dict_processed["detection_classes"][i] = category_index[output_dict_processed["detection_classes"][i]]["name"]
 
-    return base64.b64encode(buffer).decode('ascii'), cars_involved, injured_people,output_dict_processed,people
+	return base64.b64encode(buffer).decode('ascii'), cars_involved, injured_people,output_dict_processed,people
 
 
 class Crash(Resource):
-    def post(self):
-        message = request.form['message']
-        base64_img = request.form['img']
-        id = request.form['id']
-        lat, long = request.form['lat'], request.form['long']
+	def post(self):
+		message = request.form['message']
+		base64_img = request.form['img']
+		id = request.form['id']
+		lat, long = request.form['lat'], request.form['long']
 
         image, car_count, injured,out,people = process_img(base64_img)
         (top, right, bottom, left) = people[0][1]     
@@ -212,78 +203,79 @@ class Crash(Resource):
         if priority > 10:
             priority = 10
 
-        crash = {
-            'img': image,
-            'message': message,
-            'priority': priority,
-            'stats': {
-                'cars': car_count,
-                'injured': injured
-            },
-            'location': {
-                'latitude': lat,
-                'longitude': long
-            },
-            "output_dict": out
-        }
-        if id in crashes:
-            crashes[id].append(crash)
-        else:
-            crashes[id] = [crash]
-
-        with open(db_path, 'w') as f:
-            json.dump(crashes, f, indent=4)
-        return crash
+		crash = {
+			'img': image,
+			'message': message,
+			'priority': priority,
+			'stats': {
+				'cars': car_count,
+				'injured': injured
+			},
+			'location': {
+				'latitude': lat,
+				'longitude': long
+			},
+			"output_dict": out,
+			"people":people
+		}
+		if id in crashes:
+			crashes[id].append(crash)
+		else:
+			crashes[id] = [crash]
+
+		with open(db_path, 'w') as f:
+			json.dump(crashes, f, indent=2)
+		return crash
 
 class Crashes(Resource):
-    def post(self):
-        process_dict = copy.deepcopy(crashes)
-        return_dict = {}
-        for id in process_dict:
-            for i in range(len(process_dict[id])):
-                del process_dict[id][i]["img"]
-
-        for id in process_dict:
-            for i in range(len(process_dict[id])):
-                location = process_dict[id][i]['location']
-                lat, lng = float(request.form['lat']), float(request.form['lng'])
-                if abs(float(location['latitude']) - lat) < 0.3 and abs(float(location['longitude']) - lng) < 0.3:
-                    if id in return_dict:
-                        return_dict[id].append(process_dict[id][i])
-                    else:
-                        return_dict[id] = [process_dict[id][i]]
-
-        return return_dict
+	def post(self):
+		process_dict = copy.deepcopy(crashes)
+		return_dict = {}
+		for id in process_dict:
+			for i in range(len(process_dict[id])):
+				del process_dict[id][i]["img"]
+
+		for id in process_dict:
+			for i in range(len(process_dict[id])):
+				location = process_dict[id][i]['location']
+				lat, lng = float(request.form['lat']), float(request.form['lng'])
+				if abs(float(location['latitude']) - lat) < 0.3 and abs(float(location['longitude']) - lng) < 0.3:
+					if id in return_dict:
+						return_dict[id].append(process_dict[id][i])
+					else:
+						return_dict[id] = [process_dict[id][i]]
+
+		return return_dict
 
 
 class Box:
-    def __init__(self,coords, type,index):
-        self.x1 = coords[0]
-        self.y1 = coords[2]
-        self.x2 = coords[1]
-        self.y2 = coords[3]
-        self.area = (self.x2-self.x1) * (self.y2-self.y1)
-        self.type = type
-        self.index = index
-
-    def get_bigger(self,box):
-        if box.type != self.type:
-            return None
-        left = max(box.x1, self.x1)
-        right = min(box.x2, self.x2)
-        bottom = max(box.y2, self.y2)
-        top = min(box.y1, self.y1)
-
-        if not left < right and bottom < top:
-            return None
-        area_temp = abs((right-left)*(top-bottom))
-        if abs((right-left)*(top-bottom))/((box.area * (box.area < self.area)) + (self.area * (box.area > self.area))) < MIN_AREA_RATIO:
-            return None
-
-        if box.area > self.area:
-            return box
-        else:
-            return self
+	def __init__(self,coords, type,index):
+		self.x1 = coords[0]
+		self.y1 = coords[2]
+		self.x2 = coords[1]
+		self.y2 = coords[3]
+		self.area = (self.x2-self.x1) * (self.y2-self.y1)
+		self.type = type
+		self.index = index
+
+	def get_bigger(self,box):
+		if box.type != self.type:
+			return None
+		left = max(box.x1, self.x1)
+		right = min(box.x2, self.x2)
+		bottom = max(box.y2, self.y2)
+		top = min(box.y1, self.y1)
+
+		if not left < right and bottom < top:
+			return None
+		area_temp = abs((right-left)*(top-bottom))
+		if abs((right-left)*(top-bottom))/((box.area * (box.area < self.area)) + (self.area * (box.area > self.area))) < MIN_AREA_RATIO:
+			return None
+
+		if box.area > self.area:
+			return box
+		else:
+			return self
 
 
 

server_side/api/modules/complaint.py

@@ -124,7 +124,7 @@ class Complaint(Resource):
 
 		del complaints[complaint["id"]][-1]["id"]
 		with open('modules/databases/complaints.json', 'w') as complaints_file:
-			json.dump(complaints, complaints_file, indent=4)
+			json.dump(complaints, complaints_file, indent=2)
 
 
 class Complaints(Resource):
@@ -139,5 +139,5 @@ class ComplaintsUpdate(Resource):
 		complaints[args.get("id")][int(args.get("index"))]["response"]["message"] = args.get("message")
 		complaints[args["id"]][int(args["index"])]["response"]["status"] = True
 		with open('modules/databases/complaints.json', 'w') as complaints_file:
-			json.dump(complaints, complaints_file, indent=4)
+			json.dump(complaints, complaints_file, indent=2)
 		return

server_side/api/modules/denunciation.py

@@ -48,7 +48,7 @@ class Alert(Resource):
                 denunciations.append(denunciation)
 
                 with open(db_path, 'w') as f:
-                    json.dump(denunciations, f, indent=4)
+                    json.dump(denunciations, f, indent=2)
 
                 return {'success': True}
             else:

server_side/api/modules/navigation.py

@@ -309,7 +309,7 @@ class Transit(Resource):
 
 		if change:
 			bus_data = open("modules/databases/bus.json", "w")
-			bus_data.write(json.dumps(bus_json, indent=4, sort_keys=True))
+			bus_data.write(json.dumps(bus_json, indent=2, sort_keys=True))
 			bus_data.close()
 
 		for route in travel["routes"]:
@@ -333,7 +333,7 @@ class Transit(Resource):
 
 		if change:
 			bus_data = open("modules/databases/bus.json", "w")
-			bus_data.write(json.dumps(bus_json, indent=4, sort_keys=True))
+			bus_data.write(json.dumps(bus_json, indent=2, sort_keys=True))
 			bus_data.close()
 
 		for i in range(len(shortest["routes"])):
@@ -361,7 +361,7 @@ class Transit(Resource):
 
 		if change:
 			bus_data = open("modules/databases/bus.json", "w")
-			bus_data.write(json.dumps(bus_json, indent=4, sort_keys=True))
+			bus_data.write(json.dumps(bus_json, indent=2, sort_keys=True))
 			bus_data.close()
 		return shortest
 

server_side/api/modules/rating_system.py

@@ -80,7 +80,7 @@ class Ratings(Resource):
     #    ratings.append(rating)
     #
     #    with open(db_path, 'w') as f:
-    #        json.dump(ratings, f, indent=4)
+    #        json.dump(ratings, f, indent=2)
     #
     #    return rating
 
@@ -119,7 +119,7 @@ class Rate(Resource):
                 'note': note
             }
             with open(db_path, 'w') as f:
-                json.dump(ratings, f, indent=4)
+                json.dump(ratings, f, indent=2)
 
             return {'message': 'Success'}
 

server_side/api/modules/smart_park.py

@@ -116,7 +116,7 @@ while 0:
 	data = generateAvg(locs,im,data)
 
 	with open("modules/databases/park_data.json","w") as f:
-		f.write(json.dumps(data,indent=4))
+		f.write(json.dumps(data,indent=2))
 	exit(0)
 
 class Empty(Resource):

server_side/api/modules/user_info.py

@@ -43,7 +43,7 @@ class Users(Resource):
         users.append(user)
 
         with open(db_path, 'w') as f:
-            json.dump(users, f, indent=4)
+            json.dump(users, f, indent=2)
 
         return user
 
@@ -95,7 +95,7 @@ class ReducePoints(Resource):
 
                 users[username]['points'] -= int(request.form['reduce'])
                 with open(db_path, 'w') as f:
-                    json.dump(users, f, indent=4)
+                    json.dump(users, f, indent=2)
             else:
                 abort(404, error="User {} doesn't exist".format(user_id))
 

server_side/api/modules/user_set.py

@@ -31,6 +31,6 @@ for file in os.listdir("images"):
 					users[k]['face_encoding'] = list(face_encoding)
 
 			with open('modules/databases/users.json', 'w') as f:
-				users = json.dump(users, f, indent=4)
+				users = json.dump(users, f, indent=2)
 
 			os.remove(full_path)

server_side/api/modules/voting_system.py

@@ -73,7 +73,7 @@ class Votings(Resource):
         votings.append(voting)
 
         with open(db_path, 'w') as f:
-            json.dump(votings, f, indent=4)
+            json.dump(votings, f, indent=2)
 
         return {'message': 'Success'}
 
@@ -105,7 +105,7 @@ class Vote(Resource):
                 votings[voting_id]['votes'][str(vote_id)]['votes'] += 1
                 votings[voting_id]['voters'].append(voter_id)
                 with open(db_path, 'w') as f:
-                    json.dump(votings, f, indent=4)
+                    json.dump(votings, f, indent=2)
 
                 return {'message': 'Success'}
 

traffic_analyzer/ambulance_detect.py

@@ -25,6 +25,9 @@ import json
 import base64
 from PIL import Image
 from io import BytesIO
+from urllib.parse import urlencode
+from urllib.request import Request, urlopen
+import ssl
 
 switch = 1
 
@@ -35,6 +38,9 @@ sys.path.append("..")
 import time
 from object_detection.utils import ops as utils_ops
 
+AI_IP = '10.10.26.161'
+context = ssl._create_unverified_context()
+
 
 
 # What model to download.
@@ -42,47 +48,17 @@ from object_detection.utils import ops as utils_ops
 
 encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]
 
-#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17' #not even worth trying
-#MODEL_NAME = "ssd_inception_v2_coco_2017_11_17" # not bad and fast
-MODEL_NAME = "rfcn_resnet101_coco_2018_01_28" # WORKS BEST BUT takes 4 times longer per image
-#MODEL_NAME = "faster_rcnn_resnet101_coco_11_06_2017" # too slow
-#MODEL_NAME = "ssd_resnet101_v1_fpn_shared_box_predictor_oid_512x512_sync_2019_01_20"
-MODEL_FILE = MODEL_NAME + '.tar.gz'
-DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
-
-# Path to frozen detection graph. This is the actual model that is used for the object detection.
-PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'
-
-# List of the strings that is used to add correct label for each box.
 PATH_TO_LABELS = os.path.join('object_detection/data', 'mscoco_label_map.pbtxt')
 
 
 category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
 
-if sys.platform == "win32":
-    detection_graph = tf.Graph()
-    with detection_graph.as_default():
-      od_graph_def = tf.GraphDef()
-      with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
-        serialized_graph = fid.read()
-        od_graph_def.ParseFromString(serialized_graph)
-        tf.import_graph_def(od_graph_def, name='')
-
-
-    def load_image_into_numpy_array(image):
-      (im_width, im_height) = image.size
-      return np.array(image.getdata()).reshape(
-          (im_height, im_width, 3)).astype(np.uint8)
-
-    # For the sake of simplicity we will use only 2 images:
-    # image1.jpg
-    # image2.jpg
-    # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
-    PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'
-    TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(3, 6) ]
-
-    # Size, in inches, of the output images.
-sess = 0
+
+
+def load_image_into_numpy_array(image):
+  (im_width, im_height) = image.size
+  return np.array(image.getdata()).reshape(
+      (im_height, im_width, 3)).astype(np.uint8)
 
 data = {"gpu_temp":"10C","gpu_load":"15%","cpu_temp":"47C","cpu_load":"15%","mem_temp":"NaN","mem_load":"17%","fan_speed":"10000RPM"}
 
@@ -96,54 +72,15 @@ def get_temps():
         data["fan_speed"] = str(psutil.sensors_fans()["dell_smm"][0][1])+"RPM"
 
 
-def run_inference_for_single_image(image, graph):
-  global switch
-  global sess
-  with graph.as_default():
-    if(switch):
-        sess = tf.Session()
-        switch = 0
-      # Get handles to input and output tensors
-    ops = tf.get_default_graph().get_operations()
-    all_tensor_names = {output.name for op in ops for output in op.outputs}
-    tensor_dict = {}
-    for key in [
-        'num_detections', 'detection_boxes', 'detection_scores',
-        'detection_classes', 'detection_masks'
-    ]:
-      tensor_name = key + ':0'
-      if tensor_name in all_tensor_names:
-        tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
-            tensor_name)
-    if 'detection_masks' in tensor_dict:
-      # The following processing is only for single image
-      detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
-      detection_masks = tf.squeeze(tensor_dict['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(tensor_dict['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.shape[1], image.shape[2])
-      detection_masks_reframed = tf.cast(
-          tf.greater(detection_masks_reframed, 0.5), tf.uint8)
-      # Follow the convention by adding back the batch dimension
-      tensor_dict['detection_masks'] = tf.expand_dims(
-          detection_masks_reframed, 0)
-    image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
-    # Run inference
-    output_dict = sess.run(tensor_dict,
-                           feed_dict={image_tensor: image})
-    # all outputs are float32 numpy arrays, so convert types as appropriate
-    output_dict['num_detections'] = int(output_dict['num_detections'][0])
-    output_dict['detection_classes'] = output_dict[
-        'detection_classes'][0].astype(np.int64)
-    output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
-    output_dict['detection_scores'] = output_dict['detection_scores'][0]
-    if 'detection_masks' in output_dict:
-      output_dict['detection_masks'] = output_dict['detection_masks'][0]
-
-  return output_dict
+def run_inference_for_single_image(image):
+    _, buffer = cv2.imencode('.jpg', image)
+    img_base64 =  base64.b64encode(buffer).decode('ascii')
+    url = 'https://%s:5001/ai' % AI_IP # Set destination URL here
+    post_fields = {'img': img_base64,"type":"coco"}     # Set POST fields here
+    request = Request(url, urlencode(post_fields).encode())
+    data = urlopen(request, context=context).read().decode("ascii")
+    output_dict = json.loads(json.loads(data))
+    return output_dict
 
 kill = True
 
@@ -164,14 +101,9 @@ socket_switch = True
 thread = threading.Thread(target=listener)
 thread.start()
 
-if sys.platform == "win32":
-    with detection_graph.as_default():
-        sess = tf.Session()
-    cam = cv2.VideoCapture(0)
-else:
-    cam = cv2.VideoCapture('debug_data/amb_1.mp4')
-    with open("debug_data/frame_data.pkl","rb") as pkl_file:
-        frame_data = pickle.load(pkl_file)
+
+cam = cv2.VideoCapture(2)
+
 
 switch = 0
 get_temps()
@@ -183,28 +115,22 @@ reps_vid = 0
 while 1:
     ret,image = cam.read()
     reps_vid += 1
-    if not sys.platform == "win32" and not reps_vid % 2 == 0:
-        continue
+
     reps += 1
     try:  # Kavşak
         t1 = time.time()
         image_np = image
-        image_np_expanded = np.expand_dims(image_np, axis=0)
-        if sys.platform == "win32":
-            output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
-        else:
-            output_dict = frame_data[reps]
+        output_dict = run_inference_for_single_image(image_np)
 
         height, width, channels = image_np.shape
 
         out_dict = {'detection_boxes': [], 'detection_classes': [], 'detection_scores': []}
-        for i in output_dict['detection_classes']:
+        for index,i in enumerate(output_dict['detection_classes']):
             cont = False
-            if i in [3, 6, 8]:  # Car, bus, truck
-                index = np.where(output_dict['detection_classes'] == i)[0][0]
+            if i in [3, 6, 8,44,77]:  # Car, bus, truck
                 score = output_dict['detection_scores'][index]
                 if score > 0.3:
-                    if not any((output_dict['detection_boxes'][index] == b).all() for b in out_dict['detection_boxes']):
+                    if not any((output_dict['detection_boxes'][index] == b) for b in out_dict['detection_boxes']):
                         avg_x = (output_dict['detection_boxes'][index][0] + output_dict['detection_boxes'][index][2])/2
                         avg_y = (output_dict['detection_boxes'][index][1] + output_dict['detection_boxes'][index][3])/2
                         for box in out_dict['detection_boxes']:

traffic_analyzer/images/train_image_taker.py

@@ -87,9 +87,9 @@ def cut_image():
     return cut_rects
 
 coordinates = cut_image()
-print(json.dumps(coordinates,indent=4))
+print(json.dumps(coordinates,indent=2))
 with open("coordinates.json","w") as file:
-    file.write(json.dumps(coordinates,indent=4))
+    file.write(json.dumps(coordinates,indent=2))
 
 
 

traffic_analyzer/object_detection/data/mscoco_label_map.pbtxt

@@ -196,7 +196,7 @@ item {
 item {
   name: "/m/04dr76w"
   id: 44
-  display_name: "bottle"
+  display_name: "Ambulance"
 }
 item {
   name: "/m/09tvcd"
@@ -336,7 +336,7 @@ item {
 item {
   name: "/m/050k8"
   id: 77
-  display_name: "cell phone"
+  display_name: "ambulance"
 }
 item {
   name: "/m/0fx9l"