yolo/utils.py

from __future__ import division

import torch 
import torch.nn as nn
import torch.nn.functional as F 
from torch.autograd import Variable
import numpy as np
import cv2 
import boto3
from io import BytesIO

def get_data_s3(filename):
    
    ACCESS_KEY = "AKIAUKUH7S3OIVOEIRWY"
    SECRET_KEY = "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"
    bucket = "root-models"

    s3 = boto3.client( "s3" , aws_access_key_id=ACCESS_KEY , aws_secret_access_key=SECRET_KEY )
    
    response = s3.get_object(Bucket=bucket, Key=filename)
    
    data = BytesIO( response["Body"].read() )

    return data

def parse_cfg_url(filename='yolov3.cfg'):

    data = get_data_s3(filename)

    lines = data.getvalue().decode().rstrip().lstrip().split('\n')     #store the lines in a list
    lines = [x.rstrip().lstrip() for x in lines]

    lines = [x for x in lines if len(x) > 0] #get read of the empty lines 
    lines = [x for x in lines if x[0] != '#']  
    lines = [x.rstrip().lstrip() for x in lines]

    
    block = {}
    blocks = []
    
    for line in lines:
        # print('line:' , line)
        if line[0] == "[":               #This marks the start of a new block
            if len(block) != 0:
                blocks.append(block)
                block = {}
            block["type"] = line[1:-1].rstrip()
        else:
            key,value = line.split("=")
            block[key.rstrip()] = value.lstrip()
    blocks.append(block)

    # print('blocks : 2 ' , blocks )

    return blocks



def predict_transform(prediction, inp_dim, anchors, num_classes, CUDA = True):
    batch_size = prediction.size(0)
    stride =  inp_dim // prediction.size(2)
    grid_size = inp_dim // stride
    bbox_attrs = 5 + num_classes
    num_anchors = len(anchors)
    
    anchors = [(a[0]/stride, a[1]/stride) for a in anchors]



    prediction = prediction.view(batch_size, bbox_attrs*num_anchors, grid_size*grid_size)
    prediction = prediction.transpose(1,2).contiguous()
    prediction = prediction.view(batch_size, grid_size*grid_size*num_anchors, bbox_attrs)


    #Sigmoid the  centre_X, centre_Y. and object confidencce
    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])
    

    
    #Add the center offsets
    grid_len = np.arange(grid_size)
    a,b = np.meshgrid(grid_len, grid_len)
    
    x_offset = torch.FloatTensor(a).view(-1,1)
    y_offset = torch.FloatTensor(b).view(-1,1)
    
    if CUDA:
        x_offset = x_offset.cuda()
        y_offset = y_offset.cuda()
    
    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1,num_anchors).view(-1,2).unsqueeze(0)
    
    prediction[:,:,:2] += x_y_offset
      
    #log space transform height and the width
    anchors = torch.FloatTensor(anchors)
    
    if CUDA:
        anchors = anchors.cuda()
    
    anchors = anchors.repeat(grid_size*grid_size, 1).unsqueeze(0)
    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4])*anchors

    #Softmax the class scores
    prediction[:,:,5: 5 + num_classes] = torch.sigmoid((prediction[:,:, 5 : 5 + num_classes]))

    prediction[:,:,:4] *= stride
   
    
    return prediction

def write_results(prediction, confidence, num_classes, nms = True, nms_conf = 0.4):
    conf_mask = (prediction[:,:,4] > confidence).float().unsqueeze(2)
    prediction = prediction*conf_mask
    

    try:
        ind_nz = torch.nonzero(prediction[:,:,4]).transpose(0,1).contiguous()
    except:
        return 0
    
    
    box_a = prediction.new(prediction.shape)
    box_a[:,:,0] = (prediction[:,:,0] - prediction[:,:,2]/2)
    box_a[:,:,1] = (prediction[:,:,1] - prediction[:,:,3]/2)
    box_a[:,:,2] = (prediction[:,:,0] + prediction[:,:,2]/2) 
    box_a[:,:,3] = (prediction[:,:,1] + prediction[:,:,3]/2)
    prediction[:,:,:4] = box_a[:,:,:4]
    

    
    batch_size = prediction.size(0)
    
    output = prediction.new(1, prediction.size(2) + 1)
    write = False


    for ind in range(batch_size):
        #select the image from the batch
        image_pred = prediction[ind]
        

        
        #Get the class having maximum score, and the index of that class
        #Get rid of num_classes softmax scores 
        #Add the class index and the class score of class having maximum score
        max_conf, max_conf_score = torch.max(image_pred[:,5:5+ num_classes], 1)
        max_conf = max_conf.float().unsqueeze(1)
        max_conf_score = max_conf_score.float().unsqueeze(1)
        seq = (image_pred[:,:5], max_conf, max_conf_score)
        image_pred = torch.cat(seq, 1)
        

        
        #Get rid of the zero entries
        non_zero_ind =  (torch.nonzero(image_pred[:,4]))

        
        image_pred_ = image_pred[non_zero_ind.squeeze(),:].view(-1,7)
        
        #Get the various classes detected in the image
        try:
            img_classes = unique(image_pred_[:,-1])
        except:
             continue
        #WE will do NMS classwise
        for cls in img_classes:
            #get the detections with one particular class
            cls_mask = image_pred_*(image_pred_[:,-1] == cls).float().unsqueeze(1)
            class_mask_ind = torch.nonzero(cls_mask[:,-2]).squeeze()
            

            image_pred_class = image_pred_[class_mask_ind].view(-1,7)

		
        
             #sort the detections such that the entry with the maximum objectness
             #confidence is at the top
            conf_sort_index = torch.sort(image_pred_class[:,4], descending = True )[1]
            image_pred_class = image_pred_class[conf_sort_index]
            idx = image_pred_class.size(0)
            
            #if nms has to be done
            if nms:
                #For each detection
                for i in range(idx):
                    #Get the IOUs of all boxes that come after the one we are looking at 
                    #in the loop
                    try:
                        ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i+1:])
                    except ValueError:
                        break
        
                    except IndexError:
                        break
                    
                    #Zero out all the detections that have IoU > treshhold
                    iou_mask = (ious < nms_conf).float().unsqueeze(1)
                    image_pred_class[i+1:] *= iou_mask       
                    
                    #Remove the non-zero entries
                    non_zero_ind = torch.nonzero(image_pred_class[:,4]).squeeze()
                    image_pred_class = image_pred_class[non_zero_ind].view(-1,7)
                    
                    

            #Concatenate the batch_id of the image to the detection
            #this helps us identify which image does the detection correspond to 
            #We use a linear straucture to hold ALL the detections from the batch
            #the batch_dim is flattened
            #batch is identified by extra batch column
            
            
            batch_ind = image_pred_class.new(image_pred_class.size(0), 1).fill_(ind)
            seq = batch_ind, image_pred_class
            if not write:
                output = torch.cat(seq,1)
                write = True
            else:
                out = torch.cat(seq,1)
                output = torch.cat((output,out))
    
    try:
        return output
    except:
        return 0

def unique(tensor):
    tensor_np = tensor.cpu().numpy()
    unique_np = np.unique(tensor_np)
    unique_tensor = torch.from_numpy(unique_np)
    
    tensor_res = tensor.new(unique_tensor.shape)
    tensor_res.copy_(unique_tensor)
    return tensor_res

def load_classes_url(namesfile):
    fp = get_data_s3(namesfile)
    names = fp.getvalue().decode().split("\n")[:-1]
    return names


def load_classes(namesfile):
    fp = open(namesfile, "r")
    names = fp.read().split("\n")[:-1]
    return names

def bbox_iou(box1, box2):
    """
    Returns the IoU of two bounding boxes 
    
    
    """
    #Get the coordinates of bounding boxes
    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]
    
    #get the corrdinates of the intersection rectangle
    inter_rect_x1 =  torch.max(b1_x1, b2_x1)
    inter_rect_y1 =  torch.max(b1_y1, b2_y1)
    inter_rect_x2 =  torch.min(b1_x2, b2_x2)
    inter_rect_y2 =  torch.min(b1_y2, b2_y2)
    
    #Intersection area
    if torch.cuda.is_available():
            inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape).cuda())*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape).cuda())
    else:
            inter_area = torch.max(inter_rect_x2 - inter_rect_x1 + 1,torch.zeros(inter_rect_x2.shape))*torch.max(inter_rect_y2 - inter_rect_y1 + 1, torch.zeros(inter_rect_x2.shape))
    
    #Union Area
    b1_area = (b1_x2 - b1_x1 + 1)*(b1_y2 - b1_y1 + 1)
    b2_area = (b2_x2 - b2_x1 + 1)*(b2_y2 - b2_y1 + 1)
    
    iou = inter_area / (b1_area + b2_area - inter_area)
    
    return iou

def letterbox_image(img, inp_dim):
    '''resize image with unchanged aspect ratio using padding'''
    img_w, img_h = img.shape[1], img.shape[0]
    w, h = inp_dim
    new_w = int(img_w * min(w/img_w, h/img_h))
    new_h = int(img_h * min(w/img_w, h/img_h))
    resized_image = cv2.resize(img, (new_w,new_h), interpolation = cv2.INTER_CUBIC)
    
    canvas = np.full((inp_dim[1], inp_dim[0], 3), 128)

    canvas[(h-new_h)//2:(h-new_h)//2 + new_h,(w-new_w)//2:(w-new_w)//2 + new_w,  :] = resized_image
    
    return canvas


def prep_image_org(orig_im, inp_dim):
    """
    Prepare image for inputting to the neural network. 
    
    Returns a Variable 
    """

    # orig_im = cv2.imread(img)
    dim = orig_im.shape[1], orig_im.shape[0]
    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
    return img_, orig_im, dim



def prep_image(img, inp_dim):
    """
    Prepare image for inputting to the neural network. 
    
    Returns a Variable 
    """

    orig_im = cv2.imread(img)
    dim = orig_im.shape[1], orig_im.shape[0]
    img = (letterbox_image(orig_im, (inp_dim, inp_dim)))
    img_ = img[:,:,::-1].transpose((2,0,1)).copy()
    img_ = torch.from_numpy(img_).float().div(255.0).unsqueeze(0)
    return img_, orig_im, dim