{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import argparse\nimport glob\nimport os\nimport sys\nimport time\nimport warnings\nimport json, codecs\nimport random\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import linear_sum_assignment as linear_assignment\nimport math\n\nimport cycler\nfrom matplotlib import colors\nfrom matplotlib import pyplot as plt\nimport matplotlib.patches as patches\nimport tensorflow as tf\n# import humanfriendly\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image, ImageFile, ImageFont, ImageDraw\nimport statistics\nfrom torch.utils.data import Dataset,DataLoader\nimport torch\n# import tensorflow.compat.v1 as tf \n# tf.disable_v2_behavior()\nfrom tqdm import tqdm\n%matplotlib inline\n\n# from CameraTraps.ct_utils import truncate_float\nprint('TensorFlow version:', tf.__version__)\nprint('Is GPU available? tf.test.is_gpu_available:', tf.test.is_gpu_available())","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:47:46.319432Z","iopub.execute_input":"2022-07-29T10:47:46.319886Z","iopub.status.idle":"2022-07-29T10:47:58.570128Z","shell.execute_reply.started":"2022-07-29T10:47:46.319794Z","shell.execute_reply":"2022-07-29T10:47:58.569130Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load metadata information","metadata":{}},{"cell_type":"code","source":"def image_name_to_id(name):\n    return name.rstrip('.jpg')\n\ndef read_image(path):\n    with tf.io.gfile.GFile(path, 'rb') as f:\n        return np.array(Image.open(f))\n    \ndef read_json(path):\n    with tf.io.gfile.GFile(path) as f:\n        return json.load(f)\n    \ndef create_detection_map(annotations,mode=\"train\"):\n    \"\"\"Creates a dict mapping IDs ---> detections.\"\"\"\n\n    ann_map = {}\n    for image in annotations['images']:\n        if image['file'].split('/')[0] == mode:\n            ann_map[image['file'].split('/')[-1].rstrip('.jpg')] = image['detections']\n    return ann_map\n\n# TRAIN\nIMAGES_DIR_TRAIN = \"/kaggle/input/iwildcam2022-fgvc9/train/train\"\nBOX_ANNOTATION_FILE = \"/kaggle/input/iwildcam2022-fgvc9/metadata/metadata/iwildcam2022_mdv4_detections.json\"\nMASKS_DIR = \"/kaggle/input/iwildcam2022-fgvc9/instance_masks/instance_masks\"\n\nimages_train = tf.io.gfile.listdir(IMAGES_DIR_TRAIN)\n# The annotations file contains annotations for all images in train and test\nannotations = read_json(BOX_ANNOTATION_FILE)\ndetection_train_map = create_detection_map(annotations)\nimages_train_ids = list(detection_train_map.keys())\n\n#TEST\nIMAGES_DIR_TEST = \"/kaggle/input/iwildcam2022-fgvc9/test/test\"\nimages_test = tf.io.gfile.listdir(IMAGES_DIR_TEST)\ndetection_test_map = create_detection_map(annotations,mode=\"test\")\nimages_test_ids = list(detection_test_map.keys())\n\nprint(f'length of detection map for train = {len(detection_train_map)}\\nlength of detection map for test = {len(detection_test_map)}\\n')\nprint(f'length of images for train = {len(images_train)}\\nlength of images for test = {len(images_test)}\\n')\nprint(f'total annotations from megaDetector model = {len(annotations[\"images\"])}')","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:47:58.572144Z","iopub.execute_input":"2022-07-29T10:47:58.573882Z","iopub.status.idle":"2022-07-29T10:48:07.594419Z","shell.execute_reply.started":"2022-07-29T10:47:58.573838Z","shell.execute_reply":"2022-07-29T10:48:07.593347Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with codecs.open(\"../input/iwildcam2022-fgvc9/metadata/metadata/iwildcam2022_train_annotations.json\", 'r',\n                 encoding='utf-8', errors='ignore') as f:\n    train_meta = json.load(f)\n    \nwith codecs.open(\"../input/iwildcam2022-fgvc9/metadata/metadata/iwildcam2022_test_information.json\", 'r',\n                 encoding='utf-8', errors='ignore') as f:\n    test_meta = json.load(f)\nseq_test = pd.DataFrame(test_meta['images'])\n#train_cat.columns = [ 'category_id', 'scientificName','family', 'genus']\ndisplay(seq_test)\nseq_train = pd.DataFrame(train_meta['images'])\n#train_cat.columns = [ 'category_id', 'scientificName','family', 'genus']\ndisplay(seq_train)","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:48:07.595830Z","iopub.execute_input":"2022-07-29T10:48:07.597610Z","iopub.status.idle":"2022-07-29T10:48:10.575557Z","shell.execute_reply.started":"2022-07-29T10:48:07.597552Z","shell.execute_reply":"2022-07-29T10:48:10.573916Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Show DATA","metadata":{}},{"cell_type":"code","source":"COLOR_CYCLER = cycler.cycler(color=['tab:blue', 'tab:green', 'tab:orange',\n                                    'tab:red', 'tab:purple'])\npd_example = seq_train[seq_train['seq_id'] == \"30048d32-7d42-11eb-8fb5-0242ac1c0002\"]\ndef  get_image_annotation(image, detection_annotations, categories,instance_id_image,ax):\n    \"\"\"Plot boxes and mask annotations for a given image.\n\n            Args:\n            image: An image array of shape [H, W, 3]\n            detection_annotations: A list of detections. Each detection is a dict\n              containing the keys 'category', 'bbox' and 'conf'.\n            categories: A dict mapping category IDs to names.\n            instance_id_image: An array of shape [H, W] containing the instance ID\n              at each pixel. IDs are expected to be 1-indexed, with 0 reserved for\n              the background.\"\"\"\n        \n    cycle_iter = COLOR_CYCLER()\n    image_height, image_width = image.shape[:2]\n    ax.imshow(image)\n    for i, annotation in enumerate(detection_annotations):\n        xmin, ymin, width, height = annotation['bbox']\n        xmin *= image_width\n        ymin *= image_height\n        width *= image_width\n        height *= image_height\n        color = next(cycle_iter)['color']\n        rect = patches.Rectangle((xmin, ymin), width, height,\n                                 linewidth=3, edgecolor=color, facecolor='none')\n        ax.add_patch(rect)\n        label = '{}:{:.2f}'.format(categories[annotation['category']],\n                                   annotation['conf'])\n        ax.text(xmin, ymin - 5, label, fontsize=30, color='white',\n                  bbox=dict(boxstyle='square,pad=0.0', facecolor=color, alpha=0.75,\n                            ec='none'))\n        r, g, b, _ = colors.to_rgba(color)\n        color_array = np.array([r, g, b]).reshape(1, 1, 3)\n        color_image = np.ones((image_height, image_width, 3)) * color_array\n        mask = (instance_id_image == (i + 1)).astype(np.float32)[:, :, np.newaxis]\n        color_mask = np.concatenate([color_image, mask], axis=2)\n        \n        ax.imshow(color_mask, alpha=0.5)\n    return color_mask\n        \ndef show_images_seq(data,detections,train = True):\n    rows = data.shape[0]\n    cols = data.shape[1]\n    fig, axs = plt.subplots(rows, dpi=80,  figsize=(rows*5, rows*5))\n    for i in range(rows):\n        image_name = data[\"file_name\"][i]\n        image_path = os.path.join(IMAGES_DIR_TRAIN if train else IMAGES_DIR_TEST, image_name)\n        image_id = image_name_to_id(image_name)\n        mask_path = os.path.join(MASKS_DIR, f'{image_id}.png')\n        image = read_image(image_path)\n        if (image_id not in detections) or (len(detections[image_id]) == 0) or ( not tf.io.gfile.exists(mask_path)):\n            plt.title(f'{image_name} missing detection data.')\n            axs[i].imshow(image)\n        else:\n            detection_annotations = detections[image_id]\n            instance_id_image = read_image(mask_path)\n            image = get_image_annotation(image,detection_annotations,annotations['detection_categories'],instance_id_image,axs[i])   \n            \n        axs[i].axis(\"off\")    \n    plt.show()\n            \nshow_images_seq(pd_example,detection_train_map)","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:48:10.578337Z","iopub.execute_input":"2022-07-29T10:48:10.579012Z","iopub.status.idle":"2022-07-29T10:48:14.909136Z","shell.execute_reply.started":"2022-07-29T10:48:10.578968Z","shell.execute_reply":"2022-07-29T10:48:14.908273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Possible solutions include:\n* Solution 1: Iwildcam competition winners in 2021 counted the maximum number of bboxes per frame as part of their sequence.\n* Solution 2. The identified objects will be tracked using an object tracker, which gives each object a unique ID number.","metadata":{}},{"cell_type":"markdown","source":"## Solution 1: Maximum detections in sequence","metadata":{}},{"cell_type":"code","source":"def count_detections(row,detections):\n    image_id = row['file_name'].split('.')[0]\n    threshold = 0.95\n    count = 0\n    \n    for bbox in detections[image_id]:\n        if bbox['conf'] > threshold:\n            count += 1   \n    return count\n\ndef generate_zero_submission(seq_ids):\n    sub = pd.DataFrame(seq_ids, columns=['Id'])\n    sub['Predicted'] = 0\n    return sub","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:48:14.910327Z","iopub.execute_input":"2022-07-29T10:48:14.910740Z","iopub.status.idle":"2022-07-29T10:48:14.918634Z","shell.execute_reply.started":"2022-07-29T10:48:14.910702Z","shell.execute_reply":"2022-07-29T10:48:14.917735Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"seq_test[\"detections_count\"] = np.nan\nfor idx,row in tqdm(seq_test.iterrows()):\n    seq_test.at[idx, 'detections_count'] = count_detections(row,detection_test_map)\n\nsubmission_res_by_max = generate_zero_submission(seq_test.seq_id.unique())\nfor seq_id in tqdm(seq_test.seq_id.unique()):\n    max_count = seq_test[seq_test.seq_id == seq_id]['detections_count'].max()\n    submission_res_by_max.loc[submission_res_by_max.Id == seq_id, 'Predicted'] = max_count\n    \ndisplay(submission_res_by_max)\nprint(max(submission_res_by_max.Predicted))\nsubmission_res_by_max.to_csv (r'res_by_max.csv', index = False, header=True) \n","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:35:29.672641Z","iopub.execute_input":"2022-07-29T10:35:29.673262Z","iopub.status.idle":"2022-07-29T10:37:45.277266Z","shell.execute_reply.started":"2022-07-29T10:35:29.673216Z","shell.execute_reply":"2022-07-29T10:37:45.276449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Results: \n* My result on leaderboard (0.494) is the result of a random solution.\n* Maximum number of detections in each sequence according to conf> 0.85  \n    ![image.png](attachment:ab7dd19f-1a53-4682-a3d5-a479112944fc.png)    \n* Maximum number of detections in each sequence according to conf> 0.9  \n    ![image.png](attachment:8ce0f2d0-6990-41ed-87b6-f1bda13f044c.png)\n* Maximum number of detections in each sequence according to conf> 0.95  \n    ![image.png](attachment:31813a9c-0534-49d7-8ce5-be5f330aa5bc.png)\n* Maximum number of detections in each sequence according to conf> 0.97   \n    ![image.png](attachment:2ca81390-8afa-422c-b877-59a3792e6594.png)\n\n\n* As you can see - when the solution is to count the number of maximum detections of the megadetector model, the best results come when extracting the detections with conf> 0.95\n\n","metadata":{},"attachments":{"ab7dd19f-1a53-4682-a3d5-a479112944fc.png":{"image/png":"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"},"31813a9c-0534-49d7-8ce5-be5f330aa5bc.png":{"image/png":"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"},"2ca81390-8afa-422c-b877-59a3792e6594.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## Solution 2: using Object-Tracker-Model\n","metadata":{}},{"cell_type":"markdown","source":"### Intro to Tracking\nTracking in deep learning is the task of predicting the positions of objects throughout a video using their spatial as well as temporal features. More technically, Tracking is getting the initial set of detections, assigning unique ids, and tracking them throughout frames of the video (or sequence of frames) feed while maintaining the assigned ids. Tracking is generally a two-step process:  \n    **1. A detection module for target localization: The module responsible for detecting and localization of the object in the frame using some object detector like YOLOv4, CenterNet, etc (In our case- the detections is given part of db).**  \n    **2. A motion predictor: This module is responsible for predicting the future motion of the object using its past information.**\n\n### Types of Trackers\n\n1. **Single Object Tracker**- These types of trackers track only a single object even if there are many other objects present in the frame. (We will not use here).\n2. **Multiple Object Tracker -**\nThese types of trackers can track multiple objects present in a frame. Some of the algorithms include DeepSORT, JDE, and CenterTrack which are very powerful algorithms and handle most of the challenges faced by trackers. (We will present the idea below).  \n\nDeepSORT: [arXiv:1703.07402](https://arxiv.org/abs/1703.07402)  \nCentroid: [centroid tracking](https://pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/)","metadata":{}},{"cell_type":"markdown","source":"### Centroid-Tracker","metadata":{}},{"cell_type":"code","source":"                                                                                                                               #######  \nclass EuclideanDistTracker:\n    def __init__(self):\n        self.center_points = {}\n        self.id_count = 0\n    \n    def update(self, objects_rect):\n        \"\"\"\n        Parameters:\n        -----------\n        object_rect:  array of bounding box coordinates.\n        --------\n        Returns:\n            list containing [x,y,w,h,object_id].\n                x,y,w,h are the bounding box coordinates, and object_id is the id assigned to that particular bounding box.\n        --------\n        \"\"\"\n        # Objects boxes and ids\n        objects_bbs_ids = []\n\n        # Get center point of new object\n        for rect in objects_rect:\n            x, y, w, h = rect\n            cx,cy = (x+w)/2, (y+h)/2\n#             cx = (x + x + w) // 2 # Center x\n#             cy = (y + y + h) // 2 # Center y\n            # Find out if that object was detected already\n            same_object_detected = False\n            for id, pt in self.center_points.items():\n                dist = math.hypot(cx - pt[0], cy - pt[1])\n\n                if dist < 25:\n                    self.center_points[id] = (cx, cy)\n                    objects_bbs_ids.append([x, y, w, h, id])\n                    same_object_detected = True\n                    break\n\n            # New object is detected we assign the ID to that object\n            if same_object_detected is False:\n                self.center_points[self.id_count] = (cx, cy)\n                objects_bbs_ids.append([x, y, w, h, self.id_count])\n                self.id_count += 1\n\n        # Clean the dictionary by center points to remove IDS not used anymore\n        new_center_points = {}\n        for obj_bb_id in objects_bbs_ids:\n            _, _, _, _, object_id = obj_bb_id\n            center = self.center_points[object_id]\n            new_center_points[object_id] = center\n\n        # Update dictionary with IDs not used removed\n        self.center_points = new_center_points.copy()\n        return objects_bbs_ids\n    \ndef get_detections(detections_dict,height,width):\n    detections = []\n    for diction in detections_dict:\n        if diction['conf'] > 0.95:\n            detections.append(diction['bbox'])\n\n    return detections \n    \ntracker = EuclideanDistTracker()\nsubmission_res_by_tracks = generate_zero_submission(seq_test.seq_id.unique())\ni=0\nfor seq_id in tqdm(seq_test.seq_id.unique()):\n    tracker.__init__()\n    seq_frames = seq_test[seq_test.seq_id == seq_id].sort_values(by=['seq_frame_num']).reset_index( drop = True)\n    for _,frame in seq_frames.iterrows():\n        detections = get_detections(detection_test_map[frame['id']], frame['height'],frame['width'])\n        boxes_ids = tracker.update(detections)\n    if i < 3 and tracker.id_count > 1 :\n        display(seq_frames)\n        show_images_seq(seq_frames,detection_test_map,train=False)\n        print(f'id_count = {tracker.id_count}')\n        i+=1\n        \n    submission_res_by_tracks.loc[submission_res_by_tracks.Id == seq_id, 'Predicted'] = tracker.id_count\n\ndisplay(submission_res_by_tracks)\nprint(max(submission_res_by_tracks.Predicted))\nsubmission_res_by_tracks.to_csv (r'res_by_tracks.csv', index = False, header=True) \n","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:48:22.996310Z","iopub.execute_input":"2022-07-29T10:48:22.997337Z","iopub.status.idle":"2022-07-29T10:51:21.462188Z","shell.execute_reply.started":"2022-07-29T10:48:22.997283Z","shell.execute_reply":"2022-07-29T10:51:21.461273Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Results: \n* As you remember, when the solution was the maximum number of consecutive identifications - the best result achieved:\n    - Private = 0.264, Public = 0.277\n\nNow with centroid-tracker model:\n* with the limit of conf>0.95 on the detections:    \n![image.png](attachment:fed2a0c8-97b3-4f18-b2e4-5265e0c6e6bf.png)  \n    - Private = 0.348  Public = 0.360\n* without the conf restriction on the detections:  \n![image.png](attachment:31805a26-a4f2-47d4-8f0e-297c12ff857f.png)\n    - Private = 0.595 Public = 0.604\n","metadata":{},"attachments":{"fed2a0c8-97b3-4f18-b2e4-5265e0c6e6bf.png":{"image/png":"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"},"31805a26-a4f2-47d4-8f0e-297c12ff857f.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"DEEP-SORT-TRACKER\nhttps://pypi.org/project/deep-sort-realtime/","metadata":{}},{"cell_type":"markdown","source":"# Conclusion\n* It is obvious that the performance of the object tracker is not as good as the counting heuristic. There could be several reasons for this.\n- Also, in order to get better performance - we can produce the identifications ourselves by our own model.\n\n    * It makes sense that the counting heuristic works best because we are counting animals in this project and since they were taken with a cameratrap (which takes many pictures in sequence when it detects movement) it could be that in one frame all the animals that passed by in sequence were captured. Therefore, the simple and working solution is to count the maximum number of identifications in the sequence of images.\n\n* You can also try the deep sort tracker which uses a Kalman filter on the image sequences.\n    * !git clone https://github.com/Jordan-me/DeepSort_Tracker.git\n    * here is a code for start.","metadata":{}},{"cell_type":"code","source":"\n\"\"\"\nThis code taken from https://github.com/alcunha/iwildcam2021ufam\n\"\"\"\n!git clone https://github.com/Jordan-me/DeepSort_Tracker.git\nfrom DeepSort_Tracker.deep_sort import nn_matching\nfrom DeepSort_Tracker.deep_sort.detection import Detection\nfrom DeepSort_Tracker.deep_sort.tracker import Tracker\n\nmax_cosine_distance = 0.2\nnn_budget = None\ndef create_detections(annot, image_id):\n    \"\"\"\n    This method return array of Detection objec (see class above)\n    Parameters\n    ----------\n    annot : dataframe_like of bounding boxes for given images\n    img_id : string desctibe image id  \n    \"\"\"\n    detection_list = []\n    for bbox in annot[image_id]:\n        detection_list.append(Detection(bbox['bbox'],bbox['conf'],None))\n\n    return detection_list\n\ndef run_deepsort_on_seq(detections_list, frames_ids):\n    metric = nn_matching.NearestNeighborDistanceMetric(\"cosine\", max_cosine_distance, nn_budget)\n    tracker = Tracker(metric)\n    confirmed_tracks = []\n    all_tracks = []\n\n    for frame_id, detections in zip(frames_ids, detections_list):\n        tracker.predict()\n        for i in  detections:\n            print(i.tlwh)\n        tracker.update(detections)\n\n    for track in tracker.tracks:\n        if track.is_confirmed():\n            confirmed_tracks.append(track.track_id)\n\n        if track.time_since_update > 1:\n            continue\n        bbox = track.to_tlwh()\n        all_tracks.append([frame_id, track.track_id, bbox[0], bbox[1], bbox[2], bbox[3]])\n        \n    confirmed_tracks = set(confirmed_tracks)\n    print(confirmed_tracks)\n    results = [track_bbox for track_bbox in all_tracks\n             if track_bbox[1] in confirmed_tracks]\n    print(results)\n    return results\n\ndef track_iwildcam(data_set, annotations):\n    tracks_list = []\n    for seq_id in data_set.seq_id.unique():\n        seq_info = data_set[data_set.seq_id == seq_id]\n        seq_info = seq_info.sort_values(by=['seq_frame_num']) # [1st frame, 2st frame ,...]\n\n        detections_list = []\n        frames_ids = []\n        for _, row in seq_info.iterrows():     # for each farme..\n            detections = create_detections(annotations, row['id'])\n            detections_list.append(detections)\n            frames_ids.append(row['id'])\n\n        results = run_deepsort_on_seq(detections_list, frames_ids)\n\n    for bbox in results:\n        bbox_info = {'seq_id': seq_id,\n                     'img_id': bbox[0],\n                     'track_id': seq_id + str(bbox[1]),\n                     'bbox_tlwh': bbox[2:]      }\n        tracks_list.append(bbox_info)\n        \n    return tracks_list\n\n# confirmed_tracks = track_iwildcam(seq_test, detection_test_map)\n# tracker = DeepSort(max_age=30, nn_budget=70, override_track_class=None)","metadata":{"execution":{"iopub.status.busy":"2022-07-29T10:40:51.787662Z","iopub.execute_input":"2022-07-29T10:40:51.788071Z","iopub.status.idle":"2022-07-29T10:40:53.393977Z","shell.execute_reply.started":"2022-07-29T10:40:51.788035Z","shell.execute_reply":"2022-07-29T10:40:53.393023Z"},"trusted":true},"execution_count":null,"outputs":[]}]}