Add utils modules

utils/__init__.py

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+#

utils/callbacks.py

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+import datetime
+import os
+import torch
+import matplotlib
+matplotlib.use('Agg')
+import scipy.signal
+from matplotlib import pyplot as plt
+from torch.utils.tensorboard import SummaryWriter
+import shutil
+import numpy as np
+from PIL import Image
+from tqdm import tqdm
+from .utils import cvtColor, preprocess_input, resize_image
+from .utils_bbox import DecodeBox
+from .utils_map import get_coco_map, get_map
+class LossHistory():
+    
+    def __init__(self, log_dir, model, input_shape):
+        self.log_dir    = log_dir
+        self.losses     = []
+        os.makedirs(self.log_dir)
+        self.writer     = SummaryWriter(self.log_dir)
+        try:
+            dummy_input     = torch.randn(2, 3, input_shape[0], input_shape[1])
+            self.writer.add_graph(model, dummy_input)
+        except:
+            pass
+    def append_loss(self, epoch, loss):
+        if not os.path.exists(self.log_dir):
+            os.makedirs(self.log_dir)
+        self.losses.append(loss)
+
+        with open(os.path.join(self.log_dir, "epoch_loss.txt"), 'a') as f:
+            f.write(str(loss))
+            f.write("\n")
+        
+        self.writer.add_scalar('loss', loss, epoch)
+        self.loss_plot()
+
+    def loss_plot(self):
+        iters = range(len(self.losses))
+        plt.figure()
+        plt.plot(iters, self.losses, 'red', linewidth = 2, label='train loss')
+        try:
+            if len(self.losses) < 25:
+                num = 5
+            else:
+                num = 15
+            
+            plt.plot(iters, scipy.signal.savgol_filter(self.losses, num, 3), 'green', linestyle = '--', linewidth = 2, label='smooth train loss')
+        except:
+            pass
+        plt.grid(True)
+        plt.xlabel('Epoch')
+        plt.ylabel('Loss')
+        plt.legend(loc="upper right")
+        plt.savefig(os.path.join(self.log_dir, "epoch_loss.png"))
+        plt.cla()
+        plt.close("all")
+
+class EvalCallback():
+    def __init__(self, net, input_shape, anchors, anchors_mask, class_names, num_classes, val_lines, log_dir, cuda, \
+            map_out_path=".temp_map_out", max_boxes=100, confidence=0.05, nms_iou=0.5, letterbox_image=True, MINOVERLAP=0.5, eval_flag=True, period=1):
+        super(EvalCallback, self).__init__()
+        self.net                = net
+        self.input_shape        = input_shape
+        self.anchors            = anchors
+        self.anchors_mask       = anchors_mask
+        self.class_names        = class_names
+        self.num_classes        = num_classes
+        self.val_lines          = val_lines
+        self.log_dir            = log_dir
+        self.cuda               = cuda
+        self.map_out_path       = map_out_path
+        self.max_boxes          = max_boxes
+        self.confidence         = confidence
+        self.nms_iou            = nms_iou
+        self.letterbox_image    = letterbox_image
+        self.MINOVERLAP         = MINOVERLAP
+        self.eval_flag          = eval_flag
+        self.period             = period
+        self.bbox_util          = DecodeBox(self.anchors, self.num_classes, (self.input_shape[0], self.input_shape[1]), self.anchors_mask)
+        self.maps       = [0]
+        self.epoches    = [0]
+        if self.eval_flag:
+            with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f:
+                f.write(str(0))
+                f.write("\n")
+
+    def get_map_txt(self, image_id, image, class_names, map_out_path):
+        f = open(os.path.join(map_out_path, "detection-results/"+image_id+".txt"), "w", encoding='utf-8')
+        image_shape = np.array(np.shape(image)[0:2])
+        image       = cvtColor(image)
+        image_data  = resize_image(image, (self.input_shape[1], self.input_shape[0]), self.letterbox_image)
+        image_data  = np.expand_dims(np.transpose(preprocess_input(np.array(image_data, dtype='float32')), (2, 0, 1)), 0)
+        with torch.no_grad():
+            images = torch.from_numpy(image_data)
+            if self.cuda:
+                images = images.cuda()
+            outputs = self.net(images)
+            outputs = self.bbox_util.decode_box(outputs)
+            results = self.bbox_util.non_max_suppression(torch.cat(outputs, 1), self.num_classes, self.input_shape,
+                        image_shape, self.letterbox_image, conf_thres = self.confidence, nms_thres = self.nms_iou)
+            if results[0] is None:
+                return
+            top_label   = np.array(results[0][:, 6], dtype = 'int32')
+            top_conf    = results[0][:, 4] * results[0][:, 5]
+            top_boxes   = results[0][:, :4]
+        top_100     = np.argsort(top_conf)[::-1][:self.max_boxes]
+        top_boxes   = top_boxes[top_100]
+        top_conf    = top_conf[top_100]
+        top_label   = top_label[top_100]
+        for i, c in list(enumerate(top_label)):
+            predicted_class = self.class_names[int(c)]
+            box             = top_boxes[i]
+            score           = str(top_conf[i])
+            top, left, bottom, right = box
+            if predicted_class not in class_names:
+                continue
+            f.write("%s %s %s %s %s %s\n" % (predicted_class, score[:6], str(int(left)), str(int(top)), str(int(right)),str(int(bottom))))
+        f.close()
+        return
+    
+    def on_epoch_end(self, epoch, model_eval):
+        if epoch % self.period == 0 and self.eval_flag:
+            self.net = model_eval
+            if not os.path.exists(self.map_out_path):
+                os.makedirs(self.map_out_path)
+            if not os.path.exists(os.path.join(self.map_out_path, "ground-truth")):
+                os.makedirs(os.path.join(self.map_out_path, "ground-truth"))
+            if not os.path.exists(os.path.join(self.map_out_path, "detection-results")):
+                os.makedirs(os.path.join(self.map_out_path, "detection-results"))
+            print("Get map.")
+            for annotation_line in tqdm(self.val_lines):
+                line        = annotation_line.split()
+                image_id    = os.path.basename(line[0]).split('.')[0]
+                image       = Image.open(line[0])
+                gt_boxes    = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
+                self.get_map_txt(image_id, image, self.class_names, self.map_out_path)
+                with open(os.path.join(self.map_out_path, "ground-truth/"+image_id+".txt"), "w") as new_f:
+                    for box in gt_boxes:
+                        left, top, right, bottom, obj = box
+                        obj_name = self.class_names[obj]
+                        new_f.write("%s %s %s %s %s\n" % (obj_name, left, top, right, bottom))
+            print("Calculate Map.")
+            try:
+                temp_map = get_coco_map(class_names = self.class_names, path = self.map_out_path)[1]
+            except:
+                temp_map = get_map(self.MINOVERLAP, False, path = self.map_out_path)
+            self.maps.append(temp_map)
+            self.epoches.append(epoch)
+            with open(os.path.join(self.log_dir, "epoch_map.txt"), 'a') as f:
+                f.write(str(temp_map))
+                f.write("\n")
+            plt.figure()
+            plt.plot(self.epoches, self.maps, 'red', linewidth = 2, label='train map')
+            plt.grid(True)
+            plt.xlabel('Epoch')
+            plt.ylabel('Map %s'%str(self.MINOVERLAP))
+            plt.title('A Map Curve')
+            plt.legend(loc="upper right")
+            plt.savefig(os.path.join(self.log_dir, "epoch_map.png"))
+            plt.cla()
+            plt.close("all")
+            print("Get map done.")
+            shutil.rmtree(self.map_out_path)

utils/dataloader.py

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+from random import sample, shuffle
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data.dataset import Dataset
+from utils.utils import cvtColor, preprocess_input
+
+class YoloDataset(Dataset):
+    def __init__(self, annotation_lines, clean_lines, input_shape, num_classes, anchors, anchors_mask, epoch_length, train):
+        super(YoloDataset, self).__init__()
+        self.annotation_lines   = annotation_lines
+        self.clean_lines = clean_lines
+        self.input_shape        = input_shape
+        self.num_classes        = num_classes
+        self.anchors            = anchors
+        self.anchors_mask       = anchors_mask
+        self.epoch_length       = epoch_length
+        self.train              = train
+        self.epoch_now          = -1
+        self.length             = len(self.annotation_lines)
+        self.bbox_attrs         = 5 + num_classes
+
+    def __len__(self):
+        return self.length
+    
+    def __getitem__(self, index):
+        index  = index % self.length
+        image, box, clearimg= self.get_random_data(self.annotation_lines[index],self.clean_lines[index], self.input_shape, random = self.train)
+        image       = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1))
+        box         = np.array(box, dtype=np.float32)
+        clearimg    = np.transpose(preprocess_input(np.array(clearimg, dtype=np.float32)), (2, 0, 1))
+        nL          = len(box)
+        labels_out  = np.zeros((nL, 6))
+        if nL:
+            box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1]
+            box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0]
+            box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
+            box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
+            labels_out[:, 1] = box[:, -1]
+            labels_out[:, 2:] = box[:, :4]
+        return image, labels_out, clearimg
+    
+    def rand(self, a=0, b=1):
+        return np.random.rand()*(b-a) + a
+    
+    def get_random_data(self, annotation_line,clean_line, input_shape, jitter=.3, hue=.1, sat=0.7, val=0.4, random=True):
+        line    = annotation_line.split()
+        clearline = clean_line.split()
+        image   = Image.open(line[0])
+        image   = cvtColor(image)
+        clearimg = Image.open(clearline[0])
+        clearimg = cvtColor(clearimg)
+        iw, ih  = image.size
+        h, w    = input_shape
+        box     = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])
+        if not random:
+            scale = min(w/iw, h/ih)
+            nw = int(iw*scale)
+            nh = int(ih*scale)
+            dx = (w-nw)//2
+            dy = (h-nh)//2
+            image       = image.resize((nw,nh), Image.BICUBIC)
+            new_image   = Image.new('RGB', (w,h), (128,128,128))
+            new_image.paste(image, (dx, dy))
+            image_data  = np.array(new_image, np.float32)
+            clearimg = clearimg.resize((nw, nh), Image.BICUBIC)
+            new_clearimg = Image.new('RGB', (w, h), (128, 128, 128))
+            new_clearimg.paste(clearimg, (dx, dy))
+            clear_image_data = np.array(new_clearimg, np.float32)
+            if len(box)>0:
+                np.random.shuffle(box)
+                box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
+                box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
+                box[:, 0:2][box[:, 0:2]<0] = 0
+                box[:, 2][box[:, 2]>w] = w
+                box[:, 3][box[:, 3]>h] = h
+                box_w = box[:, 2] - box[:, 0]
+                box_h = box[:, 3] - box[:, 1]
+                box = box[np.logical_and(box_w>1, box_h>1)]
+            return image_data, box, clear_image_data
+        new_ar = iw/ih * self.rand(1-jitter,1+jitter) / self.rand(1-jitter,1+jitter)
+        scale = self.rand(.25, 2)
+        if new_ar < 1:
+            nh = int(scale*h)
+            nw = int(nh*new_ar)
+        else:
+            nw = int(scale*w)
+            nh = int(nw/new_ar)
+        image = image.resize((nw,nh), Image.BICUBIC)
+        clearimg = clearimg.resize((nw, nh), Image.BICUBIC)
+        dx = int(self.rand(0, w-nw))
+        dy = int(self.rand(0, h-nh))
+        new_image = Image.new('RGB', (w,h), (128,128,128))
+        new_image.paste(image, (dx, dy))
+        image = new_image
+        new_clearimg = Image.new('RGB', (w, h), (128, 128, 128))
+        new_clearimg.paste(clearimg, (dx, dy))
+        clearimg = new_clearimg
+        flip = self.rand()<.5
+        if flip:
+            image = image.transpose(Image.FLIP_LEFT_RIGHT)
+            clearimg = clearimg.transpose(Image.FLIP_LEFT_RIGHT)
+        image_data      = np.array(image, np.uint8)
+        clear_image_data = np.array(clearimg, np.uint8)
+        r               = np.random.uniform(-1, 1, 3) * [hue, sat, val] + 1
+        hue, sat, val   = cv2.split(cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV))
+        dtype           = image_data.dtype
+        hue1, sat1, val1 = cv2.split(cv2.cvtColor(clear_image_data, cv2.COLOR_RGB2HSV))
+        dtype1 = clear_image_data.dtype
+        x       = np.arange(0, 256, dtype=r.dtype)
+        lut_hue = ((x * r[0]) % 180).astype(dtype)
+        lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+        lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+        x1 = np.arange(0, 256, dtype=r.dtype)
+        lut_hue1 = ((x1 * r[0]) % 180).astype(dtype)
+        lut_sat1 = np.clip(x1 * r[1], 0, 255).astype(dtype)
+        lut_val1 = np.clip(x1 * r[2], 0, 255).astype(dtype)
+        image_data = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
+        image_data = cv2.cvtColor(image_data, cv2.COLOR_HSV2RGB)
+        clear_image_data = cv2.merge((cv2.LUT(hue1, lut_hue1), cv2.LUT(sat1, lut_sat1), cv2.LUT(val1, lut_val1)))
+        clear_image_data = cv2.cvtColor(clear_image_data, cv2.COLOR_HSV2RGB)
+        if len(box)>0:
+            np.random.shuffle(box)
+            box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx
+            box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy
+            if flip: box[:, [0,2]] = w - box[:, [2,0]]
+            box[:, 0:2][box[:, 0:2]<0] = 0
+            box[:, 2][box[:, 2]>w] = w
+            box[:, 3][box[:, 3]>h] = h
+            box_w = box[:, 2] - box[:, 0]
+            box_h = box[:, 3] - box[:, 1]
+            box = box[np.logical_and(box_w>1, box_h>1)]
+        return image_data, box, clear_image_data
+    
+    def merge_bboxes(self, bboxes, cutx, cuty):
+        merge_bbox = []
+        for i in range(len(bboxes)):
+            for box in bboxes[i]:
+                tmp_box = []
+                x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
+                if i == 0:
+                    if y1 > cuty or x1 > cutx:
+                        continue
+                    if y2 >= cuty and y1 <= cuty:
+                        y2 = cuty
+                    if x2 >= cutx and x1 <= cutx:
+                        x2 = cutx
+                if i == 1:
+                    if y2 < cuty or x1 > cutx:
+                        continue
+                    if y2 >= cuty and y1 <= cuty:
+                        y1 = cuty
+                    if x2 >= cutx and x1 <= cutx:
+                        x2 = cutx
+                if i == 2:
+                    if y2 < cuty or x2 < cutx:
+                        continue
+                    if y2 >= cuty and y1 <= cuty:
+                        y1 = cuty
+                    if x2 >= cutx and x1 <= cutx:
+                        x1 = cutx
+                if i == 3:
+                    if y1 > cuty or x2 < cutx:
+                        continue
+                    if y2 >= cuty and y1 <= cuty:
+                        y2 = cuty
+                    if x2 >= cutx and x1 <= cutx:
+                        x1 = cutx
+                tmp_box.append(x1)
+                tmp_box.append(y1)
+                tmp_box.append(x2)
+                tmp_box.append(y2)
+                tmp_box.append(box[-1])
+                merge_bbox.append(tmp_box)
+        return merge_bbox
+        
+def yolo_dataset_collate(batch):
+    images  = []
+    bboxes  = []
+    clearimg = []
+    for i, (img, box, clear) in enumerate(batch):
+        images.append(img)
+        box[:, 0] = i
+        bboxes.append(box)
+        clearimg.append(clear)
+    images  = torch.from_numpy(np.array(images)).type(torch.FloatTensor)
+    bboxes  = torch.from_numpy(np.concatenate(bboxes, 0)).type(torch.FloatTensor)
+    clearimg = torch.from_numpy(np.array(clearimg)).type(torch.FloatTensor)
+    return images, bboxes, clearimg

utils/utils.py

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+import random
+import numpy as np
+import torch
+from PIL import Image
+
+def cvtColor(image):
+    if len(np.shape(image)) == 3 and np.shape(image)[2] == 3:
+        return image
+    else:
+        image = image.convert('RGB')
+        return image
+    
+def resize_image(image, size, letterbox_image):
+    iw, ih  = image.size
+    w, h    = size
+    if letterbox_image:
+        scale   = min(w/iw, h/ih)
+        nw      = int(iw*scale)
+        nh      = int(ih*scale)
+        image   = image.resize((nw,nh), Image.BICUBIC)
+        new_image = Image.new('RGB', size, (128,128,128))
+        new_image.paste(image, ((w-nw)//2, (h-nh)//2))
+    else:
+        new_image = image.resize((w, h), Image.BICUBIC)
+    return new_image
+
+def get_classes(classes_path):
+    with open(classes_path, encoding='utf-8') as f:
+        class_names = f.readlines()
+    class_names = [c.strip() for c in class_names]
+    return class_names, len(class_names)
+
+def get_anchors(anchors_path):
+    '''loads the anchors from a file'''
+    with open(anchors_path, encoding='utf-8') as f:
+        anchors = f.readline()
+    anchors = [float(x) for x in anchors.split(',')]
+    anchors = np.array(anchors).reshape(-1, 2)
+    return anchors, len(anchors)
+
+def get_lr(optimizer):
+    for param_group in optimizer.param_groups:
+        return param_group['lr']
+    
+def seed_everything(seed=11):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+def worker_init_fn(worker_id, rank, seed):
+    worker_seed = rank + seed
+    random.seed(worker_seed)
+    np.random.seed(worker_seed)
+    torch.manual_seed(worker_seed)
+
+def preprocess_input(image):
+    image /= 255.0
+    return image
+
+def show_config(**kwargs):
+    print('Configurations:')
+    print('-' * 70)
+    print('|%25s | %40s|' % ('keys', 'values'))
+    print('-' * 70)
+    for key, value in kwargs.items():
+        print('|%25s | %40s|' % (str(key), str(value)))
+    print('-' * 70)

utils/utils_bbox.py

@@ -0,0 +1,310 @@
+import numpy as np
+import torch
+from torchvision.ops import nms
+
+class DecodeBox():
+    def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
+        super(DecodeBox, self).__init__()
+        self.anchors        = anchors
+        self.num_classes    = num_classes
+        self.bbox_attrs     = 5 + num_classes
+        self.input_shape    = input_shape
+        self.anchors_mask   = anchors_mask
+    def decode_box(self, inputs):
+        outputs = []
+        detect = [inputs[0],inputs[1],inputs[2]]
+        for i, input in enumerate(detect):
+            batch_size      = input.size(0)
+            input_height    = input.size(2)
+            input_width     = input.size(3)
+            stride_h = self.input_shape[0] / input_height
+            stride_w = self.input_shape[1] / input_width
+            scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors[self.anchors_mask[i]]]
+            prediction = input.view(batch_size, len(self.anchors_mask[i]),
+                                    self.bbox_attrs, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()
+            x = torch.sigmoid(prediction[..., 0])
+            y = torch.sigmoid(prediction[..., 1])
+            w = torch.sigmoid(prediction[..., 2])
+            h = torch.sigmoid(prediction[..., 3])
+            conf        = torch.sigmoid(prediction[..., 4])
+            pred_cls    = torch.sigmoid(prediction[..., 5:])
+            FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
+            LongTensor  = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
+            grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(
+                batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)
+            grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(
+                batch_size * len(self.anchors_mask[i]), 1, 1).view(y.shape).type(FloatTensor)
+            anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
+            anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
+            anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)
+            anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)
+            pred_boxes          = FloatTensor(prediction[..., :4].shape)
+            pred_boxes[..., 0]  = x.data * 2. - 0.5 + grid_x
+            pred_boxes[..., 1]  = y.data * 2. - 0.5 + grid_y
+            pred_boxes[..., 2]  = (w.data * 2) ** 2 * anchor_w
+            pred_boxes[..., 3]  = (h.data * 2) ** 2 * anchor_h
+            _scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)
+            output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,
+                                conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)
+            outputs.append(output.data)
+        return outputs
+    def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
+        box_yx = box_xy[..., ::-1]
+        box_hw = box_wh[..., ::-1]
+        input_shape = np.array(input_shape)
+        image_shape = np.array(image_shape)
+        if letterbox_image:
+            new_shape = np.round(image_shape * np.min(input_shape/image_shape))
+            offset  = (input_shape - new_shape)/2./input_shape
+            scale   = input_shape/new_shape
+            box_yx  = (box_yx - offset) * scale
+            box_hw *= scale
+        box_mins    = box_yx - (box_hw / 2.)
+        box_maxes   = box_yx + (box_hw / 2.)
+        boxes  = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
+        boxes *= np.concatenate([image_shape, image_shape], axis=-1)
+        return boxes
+    def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
+        box_corner          = prediction.new(prediction.shape)
+        box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
+        box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
+        box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
+        box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
+        prediction[:, :, :4] = box_corner[:, :, :4]
+        output = [None for _ in range(len(prediction))]
+        for i, image_pred in enumerate(prediction):
+            class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
+            conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
+            image_pred = image_pred[conf_mask]
+            class_conf = class_conf[conf_mask]
+            class_pred = class_pred[conf_mask]
+            if not image_pred.size(0):
+                continue
+            detections = torch.cat((image_pred[:, :5], class_conf.float(), class_pred.float()), 1)
+            unique_labels = detections[:, -1].cpu().unique()
+            if prediction.is_cuda:
+                unique_labels = unique_labels.cuda()
+                detections = detections.cuda()
+            for c in unique_labels:
+                detections_class = detections[detections[:, -1] == c]
+                keep = nms(
+                    detections_class[:, :4],
+                    detections_class[:, 4] * detections_class[:, 5],
+                    nms_thres
+                )
+                max_detections = detections_class[keep]
+                output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
+            if output[i] is not None:
+                output[i]           = output[i].cpu().numpy()
+                box_xy, box_wh      = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
+                output[i][:, :4]    = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
+        return output
+    
+class DecodeBoxNP():
+    def __init__(self, anchors, num_classes, input_shape, anchors_mask = [[6,7,8], [3,4,5], [0,1,2]]):
+        super(DecodeBoxNP, self).__init__()
+        self.anchors        = anchors
+        self.num_classes    = num_classes
+        self.bbox_attrs     = 5 + num_classes
+        self.input_shape    = input_shape
+        self.anchors_mask   = anchors_mask
+    def sigmoid(self, x):
+        return 1 / (1 + np.exp(-x))
+    def decode_box(self, inputs):
+        outputs = []
+        for i, input in enumerate(inputs):
+            batch_size      = np.shape(input)[0]
+            input_height    = np.shape(input)[2]
+            input_width     = np.shape(input)[3]
+            stride_h = self.input_shape[0] / input_height
+            stride_w = self.input_shape[1] / input_width
+            scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in self.anchors[self.anchors_mask[i]]]
+            prediction = np.transpose(np.reshape(input, (batch_size, len(self.anchors_mask[i]), self.bbox_attrs, input_height, input_width)), (0, 1, 3, 4, 2))
+            x = self.sigmoid(prediction[..., 0])
+            y = self.sigmoid(prediction[..., 1])
+            w = self.sigmoid(prediction[..., 2])
+            h = self.sigmoid(prediction[..., 3])
+            conf        = self.sigmoid(prediction[..., 4])
+            pred_cls    = self.sigmoid(prediction[..., 5:])
+            grid_x = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.linspace(0, input_width - 1, input_width), 0), input_height, axis=0), 0), batch_size * len(self.anchors_mask[i]), axis=0)
+            grid_x = np.reshape(grid_x, np.shape(x))
+            grid_y = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.linspace(0, input_height - 1, input_height), 0), input_width, axis=0).T, 0), batch_size * len(self.anchors_mask[i]), axis=0)
+            grid_y = np.reshape(grid_y, np.shape(y))
+            anchor_w = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.array(scaled_anchors)[:, 0], 0), batch_size, axis=0), -1), input_height * input_width, axis=-1)
+            anchor_h = np.repeat(np.expand_dims(np.repeat(np.expand_dims(np.array(scaled_anchors)[:, 1], 0), batch_size, axis=0), -1), input_height * input_width, axis=-1)
+            anchor_w = np.reshape(anchor_w, np.shape(w))
+            anchor_h = np.reshape(anchor_h, np.shape(h))
+            pred_boxes          = np.zeros(np.shape(prediction[..., :4]))
+            pred_boxes[..., 0]  = x * 2. - 0.5 + grid_x
+            pred_boxes[..., 1]  = y * 2. - 0.5 + grid_y
+            pred_boxes[..., 2]  = (w * 2) ** 2 * anchor_w
+            pred_boxes[..., 3]  = (h * 2) ** 2 * anchor_h
+            _scale = np.array([input_width, input_height, input_width, input_height])
+            output = np.concatenate([np.reshape(pred_boxes, (batch_size, -1, 4)) / _scale,
+                                np.reshape(conf, (batch_size, -1, 1)), np.reshape(pred_cls, (batch_size, -1, self.num_classes))], -1)
+            outputs.append(output)
+        return outputs
+    
+    def bbox_iou(self, box1, box2, x1y1x2y2=True):
+        if not x1y1x2y2:
+            b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
+            b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
+            b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
+            b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
+        else:
+            b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
+            b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
+        inter_rect_x1 = np.maximum(b1_x1, b2_x1)
+        inter_rect_y1 = np.maximum(b1_y1, b2_y1)
+        inter_rect_x2 = np.minimum(b1_x2, b2_x2)
+        inter_rect_y2 = np.minimum(b1_y2, b2_y2)
+        inter_area = np.maximum(inter_rect_x2 - inter_rect_x1, 0) * \
+                    np.maximum(inter_rect_y2 - inter_rect_y1, 0)
+        b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
+        b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
+        iou = inter_area / np.maximum(b1_area + b2_area - inter_area, 1e-6)
+        return iou
+    
+    def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
+        box_yx = box_xy[..., ::-1]
+        box_hw = box_wh[..., ::-1]
+        input_shape = np.array(input_shape)
+        image_shape = np.array(image_shape)
+        if letterbox_image:
+            new_shape = np.round(image_shape * np.min(input_shape/image_shape))
+            offset  = (input_shape - new_shape)/2./input_shape
+            scale   = input_shape/new_shape
+            box_yx  = (box_yx - offset) * scale
+            box_hw *= scale
+        box_mins    = box_yx - (box_hw / 2.)
+        box_maxes   = box_yx + (box_hw / 2.)
+        boxes  = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]], axis=-1)
+        boxes *= np.concatenate([image_shape, image_shape], axis=-1)
+        return boxes
+    
+    def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5, nms_thres=0.4):
+        box_corner          = np.zeros_like(prediction)
+        box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
+        box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
+        box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
+        box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
+        prediction[:, :, :4] = box_corner[:, :, :4]
+        output = [None for _ in range(len(prediction))]
+        for i, image_pred in enumerate(prediction):
+            class_conf = np.max(image_pred[:, 5:5 + num_classes], 1, keepdims=True)
+            class_pred = np.expand_dims(np.argmax(image_pred[:, 5:5 + num_classes], 1), -1)
+            conf_mask = np.squeeze((image_pred[:, 4] * class_conf[:, 0] >= conf_thres))
+            image_pred = image_pred[conf_mask]
+            class_conf = class_conf[conf_mask]
+            class_pred = class_pred[conf_mask]
+            if not np.shape(image_pred)[0]:
+                continue
+            detections = np.concatenate((image_pred[:, :5], class_conf, class_pred), 1)
+            unique_labels = np.unique(detections[:, -1])
+            for c in unique_labels:
+                detections_class = detections[detections[:, -1] == c]
+                conf_sort_index     = np.argsort(detections_class[:, 4] * detections_class[:, 5])[::-1]
+                detections_class    = detections_class[conf_sort_index]
+                max_detections = []
+                while np.shape(detections_class)[0]:
+                    max_detections.append(detections_class[0:1])
+                    if len(detections_class) == 1:
+                        break
+                    ious                = self.bbox_iou(max_detections[-1], detections_class[1:])
+                    detections_class    = detections_class[1:][ious < nms_thres]
+                max_detections = np.concatenate(max_detections, 0)
+                output[i] = max_detections if output[i] is None else np.concatenate((output[i], max_detections))
+            if output[i] is not None:
+                output[i]           = output[i]
+                box_xy, box_wh      = (output[i][:, 0:2] + output[i][:, 2:4])/2, output[i][:, 2:4] - output[i][:, 0:2]
+                output[i][:, :4]    = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
+        return output
+    
+if __name__ == "__main__":
+    
+    import matplotlib.pyplot as plt
+    import numpy as np
+
+    def get_anchors_and_decode(input, input_shape, anchors, anchors_mask, num_classes):
+        batch_size      = input.size(0)
+        input_height    = input.size(2)
+        input_width     = input.size(3)
+        stride_h = input_shape[0] / input_height
+        stride_w = input_shape[1] / input_width
+        scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in anchors[anchors_mask[2]]]
+        prediction = input.view(batch_size, len(anchors_mask[2]),
+                                num_classes + 5, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()
+        x = torch.sigmoid(prediction[..., 0])
+        y = torch.sigmoid(prediction[..., 1])
+        w = torch.sigmoid(prediction[..., 2])
+        h = torch.sigmoid(prediction[..., 3])
+        conf        = torch.sigmoid(prediction[..., 4])
+        pred_cls    = torch.sigmoid(prediction[..., 5:])
+        FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
+        LongTensor  = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
+        grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(
+            batch_size * len(anchors_mask[2]), 1, 1).view(x.shape).type(FloatTensor)
+        grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(
+            batch_size * len(anchors_mask[2]), 1, 1).view(y.shape).type(FloatTensor)
+        anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
+        anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
+        anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)
+        anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)
+        pred_boxes          = FloatTensor(prediction[..., :4].shape)
+        pred_boxes[..., 0]  = x.data * 2. - 0.5 + grid_x
+        pred_boxes[..., 1]  = y.data * 2. - 0.5 + grid_y
+        pred_boxes[..., 2]  = (w.data * 2) ** 2 * anchor_w
+        pred_boxes[..., 3]  = (h.data * 2) ** 2 * anchor_h
+        point_h = 5
+        point_w = 5
+        box_xy          = pred_boxes[..., 0:2].cpu().numpy() * 32
+        box_wh          = pred_boxes[..., 2:4].cpu().numpy() * 32
+        grid_x          = grid_x.cpu().numpy() * 32
+        grid_y          = grid_y.cpu().numpy() * 32
+        anchor_w        = anchor_w.cpu().numpy() * 32
+        anchor_h        = anchor_h.cpu().numpy() * 32
+        fig = plt.figure()
+        ax  = fig.add_subplot(121)
+        from PIL import Image
+        img = Image.open("img/street.jpg").resize([640, 640])
+        plt.imshow(img, alpha=0.5)
+        plt.ylim(-30, 650)
+        plt.xlim(-30, 650)
+        plt.scatter(grid_x, grid_y)
+        plt.scatter(point_h * 32, point_w * 32, c='black')
+        plt.gca().invert_yaxis()
+        anchor_left = grid_x - anchor_w / 2
+        anchor_top  = grid_y - anchor_h / 2
+        rect1 = plt.Rectangle([anchor_left[0, 0, point_h, point_w],anchor_top[0, 0, point_h, point_w]], \
+            anchor_w[0, 0, point_h, point_w],anchor_h[0, 0, point_h, point_w],color="r",fill=False)
+        rect2 = plt.Rectangle([anchor_left[0, 1, point_h, point_w],anchor_top[0, 1, point_h, point_w]], \
+            anchor_w[0, 1, point_h, point_w],anchor_h[0, 1, point_h, point_w],color="r",fill=False)
+        rect3 = plt.Rectangle([anchor_left[0, 2, point_h, point_w],anchor_top[0, 2, point_h, point_w]], \
+            anchor_w[0, 2, point_h, point_w],anchor_h[0, 2, point_h, point_w],color="r",fill=False)
+        ax.add_patch(rect1)
+        ax.add_patch(rect2)
+        ax.add_patch(rect3)
+        ax  = fig.add_subplot(122)
+        plt.imshow(img, alpha=0.5)
+        plt.ylim(-30, 650)
+        plt.xlim(-30, 650)
+        plt.scatter(grid_x, grid_y)
+        plt.scatter(point_h * 32, point_w * 32, c='black')
+        plt.scatter(box_xy[0, :, point_h, point_w, 0], box_xy[0, :, point_h, point_w, 1], c='r')
+        plt.gca().invert_yaxis()
+        pre_left    = box_xy[...,0] - box_wh[...,0] / 2
+        pre_top     = box_xy[...,1] - box_wh[...,1] / 2
+        rect1 = plt.Rectangle([pre_left[0, 0, point_h, point_w], pre_top[0, 0, point_h, point_w]],\
+            box_wh[0, 0, point_h, point_w,0], box_wh[0, 0, point_h, point_w,1],color="r",fill=False)
+        rect2 = plt.Rectangle([pre_left[0, 1, point_h, point_w], pre_top[0, 1, point_h, point_w]],\
+            box_wh[0, 1, point_h, point_w,0], box_wh[0, 1, point_h, point_w,1],color="r",fill=False)
+        rect3 = plt.Rectangle([pre_left[0, 2, point_h, point_w], pre_top[0, 2, point_h, point_w]],\
+            box_wh[0, 2, point_h, point_w,0], box_wh[0, 2, point_h, point_w,1],color="r",fill=False)
+        ax.add_patch(rect1)
+        ax.add_patch(rect2)
+        ax.add_patch(rect3)
+        plt.show()
+    feat            = torch.from_numpy(np.random.normal(0.2, 0.5, [4, 255, 20, 20])).float()
+    anchors         = np.array([[116, 90], [156, 198], [373, 326], [30,61], [62,45], [59,119], [10,13], [16,30], [33,23]])
+    anchors_mask    = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
+    get_anchors_and_decode(feat, [640, 640], anchors, anchors_mask, 80)

utils/utils_fit.py

@@ -0,0 +1,78 @@
+import os
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from utils.utils import get_lr
+
+def fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, gen, Epoch, cuda, fp16, scaler, save_period, save_dir, local_rank=0):
+    loss        = 0
+    Dehazy_loss = 0
+    loss_detection = 0
+    criterion = nn.MSELoss()
+
+    if local_rank == 0:
+        print('Start Train')
+        pbar = tqdm(total=epoch_step,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3)
+        model_train.train()
+
+    for iteration, batch in enumerate(gen):
+        if iteration >= epoch_step:
+            break
+        images, targets, clean = batch[0], batch[1], batch[2]
+        with torch.no_grad():
+            if cuda:
+                images  = images.cuda(local_rank)
+                targets = targets.cuda(local_rank)
+                clean = clean.cuda(local_rank)
+                hazy_and_clear = torch.cat([images, clean], dim = 0).cuda()
+        optimizer.zero_grad()
+
+        if not fp16:
+            outputs         = model_train(hazy_and_clear)
+            detect_outputs = [outputs[0],outputs[1],outputs[2]]
+            loss_detection      = yolo_loss(detect_outputs, targets, images)
+            loss_dehazy     = criterion(outputs[3], clean)
+            loss_value      = 1 * loss_detection + 0.1 * loss_dehazy
+            loss_value.backward()
+            optimizer.step()
+        else:
+            from torch.cuda.amp import autocast
+            with autocast():
+                outputs         = model_train(images)
+                loss_value      = yolo_loss(outputs, targets, images)
+            scaler.scale(loss_value).backward()
+            scaler.step(optimizer)
+            scaler.update()
+        if ema:
+            ema.update(model_train)
+        Dehazy_loss += loss_dehazy.item()
+        loss += loss_value.item()
+        loss_detection = (loss - 0.1 * Dehazy_loss)
+        if local_rank == 0:
+            pbar.set_postfix(**{'loss'  : loss / (iteration + 1),
+                                'loss_detection'  : loss_detection / (iteration + 1),
+                                'Dehazy_loss': Dehazy_loss / (iteration + 1),
+                                'lr'    : get_lr(optimizer)})
+            pbar.update(1)
+
+    if ema:
+        model_train_eval = ema.ema
+    else:
+        model_train_eval = model_train.eval()
+
+    if local_rank == 0:
+        pbar.close()
+        loss_history.append_loss(epoch + 1, loss / epoch_step)
+        eval_callback.on_epoch_end(epoch + 1, model_train_eval)
+        print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
+        print('Total Loss: %.3f' % (loss / epoch_step))
+        if ema:
+            save_state_dict = ema.ema.state_dict()
+        else:
+            save_state_dict = model.state_dict()
+        if (epoch + 1) % save_period == 0 or epoch + 1 == Epoch:
+            torch.save(save_state_dict, os.path.join(save_dir, "ep%03d-loss%.3f.pth" % (epoch + 1, loss / epoch_step)))
+        if loss / epoch_step <= min(loss_history.losses):
+            print('Save best model to best_epoch_weights.pth')
+            torch.save(save_state_dict, os.path.join(save_dir, "best_epoch_weights.pth"))
+        torch.save(save_state_dict, os.path.join(save_dir, "last_epoch_weights.pth"))

utils/utils_map.py

@@ -0,0 +1,723 @@
+import glob
+import json
+import math
+import operator
+import os
+import shutil
+import sys
+try:
+    from pycocotools.coco import COCO
+    from pycocotools.cocoeval import COCOeval
+except:
+    pass
+import cv2
+import matplotlib
+matplotlib.use('Agg')
+from matplotlib import pyplot as plt
+import numpy as np
+
+def log_average_miss_rate(precision, fp_cumsum, num_images):
+    if precision.size == 0:
+        lamr = 0
+        mr = 1
+        fppi = 0
+        return lamr, mr, fppi
+    fppi = fp_cumsum / float(num_images)
+    mr = (1 - precision)
+    fppi_tmp = np.insert(fppi, 0, -1.0)
+    mr_tmp = np.insert(mr, 0, 1.0)
+    ref = np.logspace(-2.0, 0.0, num = 9)
+    for i, ref_i in enumerate(ref):
+        j = np.where(fppi_tmp <= ref_i)[-1][-1]
+        ref[i] = mr_tmp[j]
+    lamr = math.exp(np.mean(np.log(np.maximum(1e-10, ref))))
+    return lamr, mr, fppi
+"""
+ throw error and exit
+"""
+def error(msg):
+    print(msg)
+    sys.exit(0)
+"""
+ check if the number is a float between 0.0 and 1.0
+"""
+def is_float_between_0_and_1(value):
+    try:
+        val = float(value)
+        if val > 0.0 and val < 1.0:
+            return True
+        else:
+            return False
+    except ValueError:
+        return False
+
+def voc_ap(rec, prec):
+    rec.insert(0, 0.0)
+    rec.append(1.0)
+    mrec = rec[:]
+    prec.insert(0, 0.0)
+    prec.append(0.0)
+    mpre = prec[:]
+
+    for i in range(len(mpre)-2, -1, -1):
+        mpre[i] = max(mpre[i], mpre[i+1])
+
+    i_list = []
+    for i in range(1, len(mrec)):
+        if mrec[i] != mrec[i-1]:
+            i_list.append(i)
+
+    ap = 0.0
+    for i in i_list:
+        ap += ((mrec[i]-mrec[i-1])*mpre[i])
+    return ap, mrec, mpre
+
+def file_lines_to_list(path):
+    with open(path) as f:
+        content = f.readlines()
+    content = [x.strip() for x in content]
+    return content
+
+def draw_text_in_image(img, text, pos, color, line_width):
+    font = cv2.FONT_HERSHEY_PLAIN
+    fontScale = 1
+    lineType = 1
+    bottomLeftCornerOfText = pos
+    cv2.putText(img, text,
+            bottomLeftCornerOfText,
+            font,
+            fontScale,
+            color,
+            lineType)
+    text_width, _ = cv2.getTextSize(text, font, fontScale, lineType)[0]
+    return img, (line_width + text_width)
+
+def adjust_axes(r, t, fig, axes):
+    bb = t.get_window_extent(renderer=r)
+    text_width_inches = bb.width / fig.dpi
+    current_fig_width = fig.get_figwidth()
+    new_fig_width = current_fig_width + text_width_inches
+    propotion = new_fig_width / current_fig_width
+    x_lim = axes.get_xlim()
+    axes.set_xlim([x_lim[0], x_lim[1]*propotion])
+
+def draw_plot_func(dictionary, n_classes, window_title, plot_title, x_label, output_path, to_show, plot_color, true_p_bar):
+    sorted_dic_by_value = sorted(dictionary.items(), key=operator.itemgetter(1))
+    sorted_keys, sorted_values = zip(*sorted_dic_by_value)
+    if true_p_bar != "":
+        fp_sorted = []
+        tp_sorted = []
+        for key in sorted_keys:
+            fp_sorted.append(dictionary[key] - true_p_bar[key])
+            tp_sorted.append(true_p_bar[key])
+        plt.barh(range(n_classes), fp_sorted, align='center', color='crimson', label='False Positive')
+        plt.barh(range(n_classes), tp_sorted, align='center', color='forestgreen', label='True Positive', left=fp_sorted)
+        plt.legend(loc='lower right')
+        """
+         Write number on side of bar
+        """
+        fig = plt.gcf()
+        axes = plt.gca()
+        # r = fig.canvas.get_renderer()
+        for i, val in enumerate(sorted_values):
+            fp_val = fp_sorted[i]
+            tp_val = tp_sorted[i]
+            fp_str_val = " " + str(fp_val)
+            tp_str_val = fp_str_val + " " + str(tp_val)
+            t = plt.text(val, i, tp_str_val, color='forestgreen', va='center', fontweight='bold')
+            plt.text(val, i, fp_str_val, color='crimson', va='center', fontweight='bold')
+            if i == (len(sorted_values)-1):
+                adjust_axes(r, t, fig, axes)
+    else:
+        plt.barh(range(n_classes), sorted_values, color=plot_color)
+        """
+         Write number on side of bar
+        """
+        fig = plt.gcf()
+        axes = plt.gca()
+        r = fig.canvas.get_renderer()
+        for i, val in enumerate(sorted_values):
+            str_val = " " + str(val)
+            if val < 1.0:
+                str_val = " {0:.2f}".format(val)
+            t = plt.text(val, i, str_val, color=plot_color, va='center', fontweight='bold')
+            if i == (len(sorted_values)-1):
+                adjust_axes(r, t, fig, axes)
+    # fig.canvas.set_window_title(window_title)
+    tick_font_size = 12
+    plt.yticks(range(n_classes), sorted_keys, fontsize=tick_font_size)
+    """
+     Re-scale height accordingly
+    """
+    init_height = fig.get_figheight()
+    dpi = fig.dpi
+    height_pt = n_classes * (tick_font_size * 1.4)
+    height_in = height_pt / dpi
+    top_margin = 0.15
+    bottom_margin = 0.05
+    figure_height = height_in / (1 - top_margin - bottom_margin)
+    if figure_height > init_height:
+        fig.set_figheight(figure_height)
+    plt.title(plot_title, fontsize=14)
+    plt.xlabel(x_label, fontsize='large')
+    fig.tight_layout()
+    fig.savefig(output_path)
+    if to_show:
+        plt.show()
+    plt.close()
+def get_map(MINOVERLAP, draw_plot, score_threhold=0.5, path = './map_out'):
+    GT_PATH             = os.path.join(path, 'ground-truth')
+    DR_PATH             = os.path.join(path, 'detection-results')
+    IMG_PATH            = os.path.join(path, 'images-optional')
+    TEMP_FILES_PATH     = os.path.join(path, '.temp_files')
+    RESULTS_FILES_PATH  = os.path.join(path, 'results')
+    show_animation = True
+    if os.path.exists(IMG_PATH):
+        for dirpath, dirnames, files in os.walk(IMG_PATH):
+            if not files:
+                show_animation = False
+    else:
+        show_animation = False
+    if not os.path.exists(TEMP_FILES_PATH):
+        os.makedirs(TEMP_FILES_PATH)
+    if os.path.exists(RESULTS_FILES_PATH):
+        shutil.rmtree(RESULTS_FILES_PATH)
+    else:
+        os.makedirs(RESULTS_FILES_PATH)
+    if draw_plot:
+        try:
+            matplotlib.use('TkAgg')
+        except:
+            pass
+        os.makedirs(os.path.join(RESULTS_FILES_PATH, "AP"))
+        os.makedirs(os.path.join(RESULTS_FILES_PATH, "F1"))
+        os.makedirs(os.path.join(RESULTS_FILES_PATH, "Recall"))
+        os.makedirs(os.path.join(RESULTS_FILES_PATH, "Precision"))
+    if show_animation:
+        os.makedirs(os.path.join(RESULTS_FILES_PATH, "images", "detections_one_by_one"))
+    ground_truth_files_list = glob.glob(GT_PATH + '/*.txt')
+    if len(ground_truth_files_list) == 0:
+        error("Error: No ground-truth files found!")
+    ground_truth_files_list.sort()
+    gt_counter_per_class     = {}
+    counter_images_per_class = {}
+    for txt_file in ground_truth_files_list:
+        file_id     = txt_file.split(".txt", 1)[0]
+        file_id     = os.path.basename(os.path.normpath(file_id))
+        temp_path   = os.path.join(DR_PATH, (file_id + ".txt"))
+        if not os.path.exists(temp_path):
+            error_msg = "Error. File not found: {}\n".format(temp_path)
+            error(error_msg)
+        lines_list      = file_lines_to_list(txt_file)
+        bounding_boxes  = []
+        is_difficult    = False
+        already_seen_classes = []
+        for line in lines_list:
+            try:
+                if "difficult" in line:
+                    class_name, left, top, right, bottom, _difficult = line.split()
+                    is_difficult = True
+                else:
+                    class_name, left, top, right, bottom = line.split()
+            except:
+                if "difficult" in line:
+                    line_split  = line.split()
+                    _difficult  = line_split[-1]
+                    bottom      = line_split[-2]
+                    right       = line_split[-3]
+                    top         = line_split[-4]
+                    left        = line_split[-5]
+                    class_name  = ""
+                    for name in line_split[:-5]:
+                        class_name += name + " "
+                    class_name  = class_name[:-1]
+                    is_difficult = True
+                else:
+                    line_split  = line.split()
+                    bottom      = line_split[-1]
+                    right       = line_split[-2]
+                    top         = line_split[-3]
+                    left        = line_split[-4]
+                    class_name  = ""
+                    for name in line_split[:-4]:
+                        class_name += name + " "
+                    class_name = class_name[:-1]
+            bbox = left + " " + top + " " + right + " " + bottom
+            if is_difficult:
+                bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False, "difficult":True})
+                is_difficult = False
+            else:
+                bounding_boxes.append({"class_name":class_name, "bbox":bbox, "used":False})
+                if class_name in gt_counter_per_class:
+                    gt_counter_per_class[class_name] += 1
+                else:
+                    gt_counter_per_class[class_name] = 1
+                if class_name not in already_seen_classes:
+                    if class_name in counter_images_per_class:
+                        counter_images_per_class[class_name] += 1
+                    else:
+                        counter_images_per_class[class_name] = 1
+                    already_seen_classes.append(class_name)
+        with open(TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json", 'w') as outfile:
+            json.dump(bounding_boxes, outfile)
+    gt_classes  = list(gt_counter_per_class.keys())
+    gt_classes  = sorted(gt_classes)
+    n_classes   = len(gt_classes)
+    dr_files_list = glob.glob(DR_PATH + '/*.txt')
+    dr_files_list.sort()
+    for class_index, class_name in enumerate(gt_classes):
+        bounding_boxes = []
+        for txt_file in dr_files_list:
+            file_id = txt_file.split(".txt",1)[0]
+            file_id = os.path.basename(os.path.normpath(file_id))
+            temp_path = os.path.join(GT_PATH, (file_id + ".txt"))
+            if class_index == 0:
+                if not os.path.exists(temp_path):
+                    error_msg = "Error. File not found: {}\n".format(temp_path)
+                    error(error_msg)
+            lines = file_lines_to_list(txt_file)
+            for line in lines:
+                try:
+                    tmp_class_name, confidence, left, top, right, bottom = line.split()
+                except:
+                    line_split      = line.split()
+                    bottom          = line_split[-1]
+                    right           = line_split[-2]
+                    top             = line_split[-3]
+                    left            = line_split[-4]
+                    confidence      = line_split[-5]
+                    tmp_class_name  = ""
+                    for name in line_split[:-5]:
+                        tmp_class_name += name + " "
+                    tmp_class_name  = tmp_class_name[:-1]
+                if tmp_class_name == class_name:
+                    bbox = left + " " + top + " " + right + " " +bottom
+                    bounding_boxes.append({"confidence":confidence, "file_id":file_id, "bbox":bbox})
+        bounding_boxes.sort(key=lambda x:float(x['confidence']), reverse=True)
+        with open(TEMP_FILES_PATH + "/" + class_name + "_dr.json", 'w') as outfile:
+            json.dump(bounding_boxes, outfile)
+    sum_AP = 0.0
+    ap_dictionary = {}
+    lamr_dictionary = {}
+    with open(RESULTS_FILES_PATH + "/results.txt", 'w') as results_file:
+        results_file.write("# AP and precision/recall per class\n")
+        count_true_positives = {}
+
+        for class_index, class_name in enumerate(gt_classes):
+            count_true_positives[class_name] = 0
+            dr_file = TEMP_FILES_PATH + "/" + class_name + "_dr.json"
+            dr_data = json.load(open(dr_file))
+
+            nd          = len(dr_data)
+            tp          = [0] * nd
+            fp          = [0] * nd
+            score       = [0] * nd
+            score_threhold_idx = 0
+            for idx, detection in enumerate(dr_data):
+                file_id     = detection["file_id"]
+                score[idx]  = float(detection["confidence"])
+                if score[idx] >= score_threhold:
+                    score_threhold_idx = idx
+                if show_animation:
+                    ground_truth_img = glob.glob1(IMG_PATH, file_id + ".*")
+                    if len(ground_truth_img) == 0:
+                        error("Error. Image not found with id: " + file_id)
+                    elif len(ground_truth_img) > 1:
+                        error("Error. Multiple image with id: " + file_id)
+                    else:
+                        img = cv2.imread(IMG_PATH + "/" + ground_truth_img[0])
+                        img_cumulative_path = RESULTS_FILES_PATH + "/images/" + ground_truth_img[0]
+                        if os.path.isfile(img_cumulative_path):
+                            img_cumulative = cv2.imread(img_cumulative_path)
+                        else:
+                            img_cumulative = img.copy()
+                        bottom_border = 60
+                        BLACK = [0, 0, 0]
+                        img = cv2.copyMakeBorder(img, 0, bottom_border, 0, 0, cv2.BORDER_CONSTANT, value=BLACK)
+                gt_file             = TEMP_FILES_PATH + "/" + file_id + "_ground_truth.json"
+                ground_truth_data   = json.load(open(gt_file))
+                ovmax       = -1
+                gt_match    = -1
+                bb          = [float(x) for x in detection["bbox"].split()]
+                for obj in ground_truth_data:
+                    if obj["class_name"] == class_name:
+                        bbgt    = [ float(x) for x in obj["bbox"].split() ]
+                        bi      = [max(bb[0],bbgt[0]), max(bb[1],bbgt[1]), min(bb[2],bbgt[2]), min(bb[3],bbgt[3])]
+                        iw      = bi[2] - bi[0] + 1
+                        ih      = bi[3] - bi[1] + 1
+                        if iw > 0 and ih > 0:
+                            ua = (bb[2] - bb[0] + 1) * (bb[3] - bb[1] + 1) + (bbgt[2] - bbgt[0]
+                                            + 1) * (bbgt[3] - bbgt[1] + 1) - iw * ih
+                            ov = iw * ih / ua
+                            if ov > ovmax:
+                                ovmax = ov
+                                gt_match = obj
+                if show_animation:
+                    status = "NO MATCH FOUND!"
+                min_overlap = MINOVERLAP
+                if ovmax >= min_overlap:
+                    if "difficult" not in gt_match:
+                        if not bool(gt_match["used"]):
+                            tp[idx] = 1
+                            gt_match["used"] = True
+                            count_true_positives[class_name] += 1
+                            with open(gt_file, 'w') as f:
+                                    f.write(json.dumps(ground_truth_data))
+                            if show_animation:
+                                status = "MATCH!"
+                        else:
+                            fp[idx] = 1
+                            if show_animation:
+                                status = "REPEATED MATCH!"
+                else:
+                    fp[idx] = 1
+                    if ovmax > 0:
+                        status = "INSUFFICIENT OVERLAP"
+                """
+                Draw image to show animation
+                """
+                if show_animation:
+                    height, widht = img.shape[:2]
+                    white           = (255,255,255)
+                    light_blue      = (255,200,100)
+                    green           = (0,255,0)
+                    light_red       = (30,30,255)
+                    margin          = 10
+                    v_pos           = int(height - margin - (bottom_border / 2.0))
+                    text            = "Image: " + ground_truth_img[0] + " "
+                    img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0)
+                    text            = "Class [" + str(class_index) + "/" + str(n_classes) + "]: " + class_name + " "
+                    img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), light_blue, line_width)
+                    if ovmax != -1:
+                        color       = light_red
+                        if status   == "INSUFFICIENT OVERLAP":
+                            text    = "IoU: {0:.2f}% ".format(ovmax*100) + "< {0:.2f}% ".format(min_overlap*100)
+                        else:
+                            text    = "IoU: {0:.2f}% ".format(ovmax*100) + ">= {0:.2f}% ".format(min_overlap*100)
+                            color   = green
+                        img, _ = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)
+                    v_pos           += int(bottom_border / 2.0)
+                    rank_pos        = str(idx+1)
+                    text            = "Detection #rank: " + rank_pos + " confidence: {0:.2f}% ".format(float(detection["confidence"])*100)
+                    img, line_width = draw_text_in_image(img, text, (margin, v_pos), white, 0)
+                    color           = light_red
+                    if status == "MATCH!":
+                        color = green
+                    text            = "Result: " + status + " "
+                    img, line_width = draw_text_in_image(img, text, (margin + line_width, v_pos), color, line_width)
+
+                    font = cv2.FONT_HERSHEY_SIMPLEX
+                    if ovmax > 0:
+                        bbgt = [ int(round(float(x))) for x in gt_match["bbox"].split() ]
+                        cv2.rectangle(img,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2)
+                        cv2.rectangle(img_cumulative,(bbgt[0],bbgt[1]),(bbgt[2],bbgt[3]),light_blue,2)
+                        cv2.putText(img_cumulative, class_name, (bbgt[0],bbgt[1] - 5), font, 0.6, light_blue, 1, cv2.LINE_AA)
+                    bb = [int(i) for i in bb]
+                    cv2.rectangle(img,(bb[0],bb[1]),(bb[2],bb[3]),color,2)
+                    cv2.rectangle(img_cumulative,(bb[0],bb[1]),(bb[2],bb[3]),color,2)
+                    cv2.putText(img_cumulative, class_name, (bb[0],bb[1] - 5), font, 0.6, color, 1, cv2.LINE_AA)
+                    cv2.imshow("Animation", img)
+                    cv2.waitKey(20)
+                    output_img_path = RESULTS_FILES_PATH + "/images/detections_one_by_one/" + class_name + "_detection" + str(idx) + ".jpg"
+                    cv2.imwrite(output_img_path, img)
+                    cv2.imwrite(img_cumulative_path, img_cumulative)
+            cumsum = 0
+            for idx, val in enumerate(fp):
+                fp[idx] += cumsum
+                cumsum += val
+            cumsum = 0
+            for idx, val in enumerate(tp):
+                tp[idx] += cumsum
+                cumsum += val
+            rec = tp[:]
+            for idx, val in enumerate(tp):
+                rec[idx] = float(tp[idx]) / np.maximum(gt_counter_per_class[class_name], 1)
+            prec = tp[:]
+            for idx, val in enumerate(tp):
+                prec[idx] = float(tp[idx]) / np.maximum((fp[idx] + tp[idx]), 1)
+            ap, mrec, mprec = voc_ap(rec[:], prec[:])
+            F1  = np.array(rec)*np.array(prec)*2 / np.where((np.array(prec)+np.array(rec))==0, 1, (np.array(prec)+np.array(rec)))
+            sum_AP  += ap
+            text    = "{0:.2f}%".format(ap*100) + " = " + class_name + " AP "
+            if len(prec)>0:
+                F1_text         = "{0:.2f}".format(F1[score_threhold_idx]) + " = " + class_name + " F1 "
+                Recall_text     = "{0:.2f}%".format(rec[score_threhold_idx]*100) + " = " + class_name + " Recall "
+                Precision_text  = "{0:.2f}%".format(prec[score_threhold_idx]*100) + " = " + class_name + " Precision "
+            else:
+                F1_text         = "0.00" + " = " + class_name + " F1 "
+                Recall_text     = "0.00%" + " = " + class_name + " Recall "
+                Precision_text  = "0.00%" + " = " + class_name + " Precision "
+            rounded_prec    = [ '%.2f' % elem for elem in prec ]
+            rounded_rec     = [ '%.2f' % elem for elem in rec ]
+            results_file.write(text + "\n Precision: " + str(rounded_prec) + "\n Recall :" + str(rounded_rec) + "\n\n")
+            if len(prec)>0:
+                print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=" + "{0:.2f}".format(F1[score_threhold_idx])\
+                    + " ; Recall=" + "{0:.2f}%".format(rec[score_threhold_idx]*100) + " ; Precision=" + "{0:.2f}%".format(prec[score_threhold_idx]*100))
+            else:
+                print(text + "\t||\tscore_threhold=" + str(score_threhold) + " : " + "F1=0.00% ; Recall=0.00% ; Precision=0.00%")
+            ap_dictionary[class_name] = ap
+            n_images = counter_images_per_class[class_name]
+            lamr, mr, fppi = log_average_miss_rate(np.array(rec), np.array(fp), n_images)
+            lamr_dictionary[class_name] = lamr
+            if draw_plot:
+                plt.plot(rec, prec, '-o')
+                area_under_curve_x = mrec[:-1] + [mrec[-2]] + [mrec[-1]]
+                area_under_curve_y = mprec[:-1] + [0.0] + [mprec[-1]]
+                plt.fill_between(area_under_curve_x, 0, area_under_curve_y, alpha=0.2, edgecolor='r')
+                fig = plt.gcf()
+                # fig.canvas.set_window_title('AP ' + class_name)
+                plt.title('class: ' + text)
+                plt.xlabel('Recall')
+                plt.ylabel('Precision')
+                axes = plt.gca()
+                axes.set_xlim([0.0,1.0])
+                axes.set_ylim([0.0,1.05])
+                fig.savefig(RESULTS_FILES_PATH + "/AP/" + class_name + ".png")
+                plt.cla()
+                plt.plot(score, F1, "-", color='orangered')
+                plt.title('class: ' + F1_text + "\nscore_threhold=" + str(score_threhold))
+                plt.xlabel('Score_Threhold')
+                plt.ylabel('F1')
+                axes = plt.gca()
+                axes.set_xlim([0.0,1.0])
+                axes.set_ylim([0.0,1.05])
+                fig.savefig(RESULTS_FILES_PATH + "/F1/" + class_name + ".png")
+                plt.cla()
+                plt.plot(score, rec, "-H", color='gold')
+                plt.title('class: ' + Recall_text + "\nscore_threhold=" + str(score_threhold))
+                plt.xlabel('Score_Threhold')
+                plt.ylabel('Recall')
+                axes = plt.gca()
+                axes.set_xlim([0.0,1.0])
+                axes.set_ylim([0.0,1.05])
+                fig.savefig(RESULTS_FILES_PATH + "/Recall/" + class_name + ".png")
+                plt.cla()
+                plt.plot(score, prec, "-s", color='palevioletred')
+                plt.title('class: ' + Precision_text + "\nscore_threhold=" + str(score_threhold))
+                plt.xlabel('Score_Threhold')
+                plt.ylabel('Precision')
+                axes = plt.gca()
+                axes.set_xlim([0.0,1.0])
+                axes.set_ylim([0.0,1.05])
+                fig.savefig(RESULTS_FILES_PATH + "/Precision/" + class_name + ".png")
+                plt.cla()
+        if show_animation:
+            cv2.destroyAllWindows()
+        if n_classes == 0:
+            print("No species detected, check if the label information is modified with the classes_path in get_map.py.")
+            return 0
+        results_file.write("\n# mAP of all classes\n")
+        mAP     = sum_AP / n_classes
+        text    = "mAP = {0:.2f}%".format(mAP*100)
+        results_file.write(text + "\n")
+        print(text)
+    shutil.rmtree(TEMP_FILES_PATH)
+    """
+    Count total of detection-results
+    """
+    det_counter_per_class = {}
+    for txt_file in dr_files_list:
+        lines_list = file_lines_to_list(txt_file)
+        for line in lines_list:
+            class_name = line.split()[0]
+            if class_name in det_counter_per_class:
+                det_counter_per_class[class_name] += 1
+            else:
+                det_counter_per_class[class_name] = 1
+    dr_classes = list(det_counter_per_class.keys())
+    """
+    Write number of ground-truth objects per class to results.txt
+    """
+    with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file:
+        results_file.write("\n# Number of ground-truth objects per class\n")
+        for class_name in sorted(gt_counter_per_class):
+            results_file.write(class_name + ": " + str(gt_counter_per_class[class_name]) + "\n")
+    """
+    Finish counting true positives
+    """
+    for class_name in dr_classes:
+        if class_name not in gt_classes:
+            count_true_positives[class_name] = 0
+    """
+    Write number of detected objects per class to results.txt
+    """
+    with open(RESULTS_FILES_PATH + "/results.txt", 'a') as results_file:
+        results_file.write("\n# Number of detected objects per class\n")
+        for class_name in sorted(dr_classes):
+            n_det = det_counter_per_class[class_name]
+            text = class_name + ": " + str(n_det)
+            text += " (tp:" + str(count_true_positives[class_name]) + ""
+            text += ", fp:" + str(n_det - count_true_positives[class_name]) + ")\n"
+            results_file.write(text)
+    """
+    Plot the total number of occurences of each class in the ground-truth
+    """
+    if draw_plot:
+        window_title = "ground-truth-info"
+        plot_title = "ground-truth\n"
+        plot_title += "(" + str(len(ground_truth_files_list)) + " files and " + str(n_classes) + " classes)"
+        x_label = "Number of objects per class"
+        output_path = RESULTS_FILES_PATH + "/ground-truth-info.png"
+        to_show = False
+        plot_color = 'forestgreen'
+        draw_plot_func(
+            gt_counter_per_class,
+            n_classes,
+            window_title,
+            plot_title,
+            x_label,
+            output_path,
+            to_show,
+            plot_color,
+            '',
+            )
+    """
+    Draw log-average miss rate plot (Show lamr of all classes in decreasing order)
+    """
+    if draw_plot:
+        window_title = "lamr"
+        plot_title = "log-average miss rate"
+        x_label = "log-average miss rate"
+        output_path = RESULTS_FILES_PATH + "/lamr.png"
+        to_show = False
+        plot_color = 'royalblue'
+        draw_plot_func(
+            lamr_dictionary,
+            n_classes,
+            window_title,
+            plot_title,
+            x_label,
+            output_path,
+            to_show,
+            plot_color,
+            ""
+            )
+    """
+    Draw mAP plot (Show AP's of all classes in decreasing order)
+    """
+    if draw_plot:
+        window_title = "mAP"
+        plot_title = "mAP = {0:.2f}%".format(mAP*100)
+        x_label = "Average Precision"
+        output_path = RESULTS_FILES_PATH + "/mAP.png"
+        to_show = True
+        plot_color = 'royalblue'
+        draw_plot_func(
+            ap_dictionary,
+            n_classes,
+            window_title,
+            plot_title,
+            x_label,
+            output_path,
+            to_show,
+            plot_color,
+            ""
+            )
+    return mAP
+def preprocess_gt(gt_path, class_names):
+    image_ids   = os.listdir(gt_path)
+    results = {}
+    images = []
+    bboxes = []
+    for i, image_id in enumerate(image_ids):
+        lines_list      = file_lines_to_list(os.path.join(gt_path, image_id))
+        boxes_per_image = []
+        image           = {}
+        image_id        = os.path.splitext(image_id)[0]
+        image['file_name'] = image_id + '.jpg'
+        image['width']     = 1
+        image['height']    = 1
+        image['id']        = str(image_id)
+        for line in lines_list:
+            difficult = 0
+            if "difficult" in line:
+                line_split  = line.split()
+                left, top, right, bottom, _difficult = line_split[-5:]
+                class_name  = ""
+                for name in line_split[:-5]:
+                    class_name += name + " "
+                class_name  = class_name[:-1]
+                difficult = 1
+            else:
+                line_split  = line.split()
+                left, top, right, bottom = line_split[-4:]
+                class_name  = ""
+                for name in line_split[:-4]:
+                    class_name += name + " "
+                class_name = class_name[:-1]
+            left, top, right, bottom = float(left), float(top), float(right), float(bottom)
+            if class_name not in class_names:
+                continue
+            cls_id  = class_names.index(class_name) + 1
+            bbox    = [left, top, right - left, bottom - top, difficult, str(image_id), cls_id, (right - left) * (bottom - top) - 10.0]
+            boxes_per_image.append(bbox)
+        images.append(image)
+        bboxes.extend(boxes_per_image)
+    results['images']        = images
+    categories = []
+    for i, cls in enumerate(class_names):
+        category = {}
+        category['supercategory']   = cls
+        category['name']            = cls
+        category['id']              = i + 1
+        categories.append(category)
+    results['categories']   = categories
+    annotations = []
+    for i, box in enumerate(bboxes):
+        annotation = {}
+        annotation['area']        = box[-1]
+        annotation['category_id'] = box[-2]
+        annotation['image_id']    = box[-3]
+        annotation['iscrowd']     = box[-4]
+        annotation['bbox']        = box[:4]
+        annotation['id']          = i
+        annotations.append(annotation)
+    results['annotations'] = annotations
+    return results
+def preprocess_dr(dr_path, class_names):
+    image_ids = os.listdir(dr_path)
+    results = []
+    for image_id in image_ids:
+        lines_list      = file_lines_to_list(os.path.join(dr_path, image_id))
+        image_id        = os.path.splitext(image_id)[0]
+        for line in lines_list:
+            line_split  = line.split()
+            confidence, left, top, right, bottom = line_split[-5:]
+            class_name  = ""
+            for name in line_split[:-5]:
+                class_name += name + " "
+            class_name  = class_name[:-1]
+            left, top, right, bottom = float(left), float(top), float(right), float(bottom)
+            result                  = {}
+            result["image_id"]      = str(image_id)
+            if class_name not in class_names:
+                continue
+            result["category_id"]   = class_names.index(class_name) + 1
+            result["bbox"]          = [left, top, right - left, bottom - top]
+            result["score"]         = float(confidence)
+            results.append(result)
+    return results
+def get_coco_map(class_names, path):
+    GT_PATH     = os.path.join(path, 'ground-truth')
+    DR_PATH     = os.path.join(path, 'detection-results')
+    COCO_PATH   = os.path.join(path, 'coco_eval')
+    if not os.path.exists(COCO_PATH):
+        os.makedirs(COCO_PATH)
+    GT_JSON_PATH = os.path.join(COCO_PATH, 'instances_gt.json')
+    DR_JSON_PATH = os.path.join(COCO_PATH, 'instances_dr.json')
+    with open(GT_JSON_PATH, "w") as f:
+        results_gt  = preprocess_gt(GT_PATH, class_names)
+        json.dump(results_gt, f, indent=4)
+    with open(DR_JSON_PATH, "w") as f:
+        results_dr  = preprocess_dr(DR_PATH, class_names)
+        json.dump(results_dr, f, indent=4)
+        if len(results_dr) == 0:
+            print("No targets detected.")
+            return [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    cocoGt      = COCO(GT_JSON_PATH)
+    cocoDt      = cocoGt.loadRes(DR_JSON_PATH)
+    cocoEval    = COCOeval(cocoGt, cocoDt, 'bbox')
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    cocoEval.summarize()
+    return cocoEval.stats