diff --git a/server_side/api/modules/car_crash.py b/server_side/api/modules/car_crash.py
index f62b07a..9f6ba84 100644
--- a/server_side/api/modules/car_crash.py
+++ b/server_side/api/modules/car_crash.py
@@ -51,8 +51,55 @@ if sys.platform == "win32":
         return np.array(image.getdata()).reshape(
             (im_height, im_width, 3)).astype(np.uint8)
 
-def process_img(img):
-    pass
+def process_img(img_base64):
+    if sys.platform == "win32":
+        img = Image.open(io.BytesIO(base64.b64decode(img_base64)))
+        with detection_graph.as_default():
+            with tf.Session(graph=detection_graph) as sess:
+                # Definite input and output Tensors for detection_graph
+                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+                # Each box represents a part of the image where a particular object was detected.
+                detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+                # Each score represent how level of confidence for each of the objects.
+                # Score is shown on the result image, together with the class label.
+                detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
+                detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
+                num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+                # the array based representation of the image will be used later in order to prepare the
+                # result image with boxes and labels on it.
+                image_np = load_image_into_numpy_array(img)
+                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+                image_np_expanded = np.expand_dims(image_np, axis=0)
+                # Actual detection.
+                (boxes, scores, classes, num) = sess.run(
+                    [detection_boxes, detection_scores, detection_classes, num_detections],
+                    feed_dict={image_tensor: image_np_expanded})
+                # Visualization of the results of a detection.
+                vis_util.visualize_boxes_and_labels_on_image_array(
+                    image_np,
+                    np.squeeze(boxes),
+                    np.squeeze(classes).astype(np.int32),
+                    np.squeeze(scores),
+                    category_index,
+                    min_score_thresh=0.3,
+                    use_normalized_coordinates=True,
+                    line_thickness=8)
+
+        output_dict = {'detection_classes': classes, 'detection_scores': scores[0], 'detection_boxes': boxes}
+        cars_involved = 0
+        injured_people = 0
+        for i in output_dict['detection_classes']:
+            index = np.where(output_dict['detection_classes'] == i)[0][0]
+            score = output_dict['detection_scores'][index]
+            if score > 0.3:
+                if output_dict['detection_classes'] == 1:
+                    cars_involved += 1
+                else:
+                    pass
+
+        return base64.b64encode(pickle.dumps(image_np)).decode('ascii'), cars_involved, injured_people
+
+    return img_base64, 7, ["unprocessed"]
 
 class Crash(Resource):
     def post(self):
@@ -60,7 +107,7 @@ class Crash(Resource):
         base64_img = request.form['img']
         id = request.form['id']
 
-        process_img(Image.open(io.BytesIO(base64.b64decode(base64_img))))
+        process_img(base64_img)
 
 
         return id