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from __future__ import division
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import cv2
from yolo.utils import *
import argparse
import os
import os.path as osp
from yolo.darknet import Darknet
# from preprocess import prep_image, inp_to_image
import pandas as pd
import random
import pickle as pkl
import itertools
import os
import base64
from PIL import Image
from io import BytesIO
class yolo_model():
batch_size = int(1)
confidence = float(0.5)
nms_thesh = float(0.4)
reso = 416
start = 0
CUDA = torch.cuda.is_available()
num_classes = 80
def __init__(self):
self.classes = load_classes( os.path.join( 'yolo' , 'data', 'coco.names' ) )
# self.colors = pkl.load( get_data_s3( "pallete" ) )
# Set up the neural network
self.model = Darknet( os.path.join( 'yolo' , 'yolov3-tiny.cfg' ) )
self.model.load_weights( os.path.join( 'yolo' , 'yolov3-tiny.weights' ) )
print(' [*] Model Loaded Successfuly')
# set model resolution
self.model.net_info["height"] = self.reso
self.inp_dim = int(self.model.net_info["height"])
assert self.inp_dim % 32 == 0
assert self.inp_dim > 32
# If there's a GPU availible, put the model on GPU
if self.CUDA:
self.model.cuda()
# Set the model in evaluation mode
self.model.eval()
def write( self , x , batches , results , colors=[] ):
c1 = tuple(x[1:3].int())
c2 = tuple(x[3:5].int())
img = results[int(x[0])]
print( 'img' , int( x[0] ) )
print( 'cls' , int( x[-1] ) )
cls = int(x[-1])
label = "{0}".format(self.classes[cls])
color = random.choice(colors)
cv2.rectangle(img, c1, c2,color, 1)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1 , 1)[0]
c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4
cv2.rectangle(img, c1, c2,color, -1)
cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 1, [225,255,255], 1)
return img
def img_to_base64_str(self,img):
buffered = BytesIO()
img.save(buffered, format="PNG")
buffered.seek(0)
img_byte = buffered.getvalue()
img_str = "data:image/png;base64," + base64.b64encode(img_byte).decode()
return img_str
def predict( self , image ):
imlist = []
imlist.append( image )
batches = list( map( prep_image_org , imlist , [ self.inp_dim for x in range( len(imlist) ) ] ) )
im_batches = [x[0] for x in batches]
orig_ims = [x[1] for x in batches]
im_dim_list = [x[2] for x in batches]
print( 'im_dim_list : ' , im_dim_list )
im_dim_list = torch.FloatTensor(im_dim_list).repeat(1,2)
if self.CUDA:
im_dim_list = im_dim_list.cuda()
print('im_batches' , len(im_batches))
batch = im_batches[0]
if self.CUDA:
batch = batch.cuda()
#Apply offsets to the result predictions
#Tranform the predictions as described in the YOLO paper
#flatten the prediction vector
# B x (bbox cord x no. of anchors) x grid_w x grid_h --> B x bbox x (all the boxes)
# Put every proposed box as a row.
with torch.no_grad():
prediction = self.model(Variable(batch), self.CUDA)
# prediction = prediction[:,scale_indices]
#get the boxes with object confidence > threshold
#Convert the cordinates to absolute coordinates
#perform NMS on these boxes, and save the results
#I could have done NMS and saving seperately to have a better abstraction
#But both these operations require looping, hence
#clubbing these ops in one loop instead of two.
#loops are slower than vectorised operations.
prediction = write_results(prediction, self.confidence, self.num_classes, nms = True, nms_conf = self.nms_thesh)
end = time.time()
# print(end - start)
# prediction[:,0] += i*batch_size
output = prediction
# 1, 1, 1
# print( 'enumerate : ' , batch_size , len(imlist) , min( batch_size , len(imlist) ) )
for im_num, image in enumerate( imlist ):
im_id = im_num
objs = [self.classes[int(x[-1])] for x in output if int(x[0]) == im_id]
# print("{0:20s} predicted in {1:6.3f} seconds".format(image.split("/")[-1], (end - self.start)/self.batch_size))
print("{0:20s} {1:s}".format("Objects Detected:", " ".join(objs)))
print("----------------------------------------------------------")
im_dim_list = torch.index_select(im_dim_list, 0, output[:,0].long())
scaling_factor = torch.min(self.inp_dim/im_dim_list,1)[0].view(-1,1)
output[:,[1,3]] -= (self.inp_dim - scaling_factor*im_dim_list[:,0].view(-1,1))/2
output[:,[2,4]] -= (self.inp_dim - scaling_factor*im_dim_list[:,1].view(-1,1))/2
output[:,1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1,3]] = torch.clamp(output[i, [1,3]], 0.0, im_dim_list[i,0])
output[i, [2,4]] = torch.clamp(output[i, [2,4]], 0.0, im_dim_list[i,1])
colors = pkl.load( open( "yolo/pallete", "rb") )
list(map(lambda x: self.write( x , im_batches , orig_ims , colors=colors ) , output ) )
print('orig_ims : shape ',orig_ims[0].shape)
# print('orig_ims : ',orig_ims[0])
output_image = Image.fromarray(orig_ims[0])
img_str = self.img_to_base64_str(output_image)
# im_bytes = orig_ims[0].tobytes()
# im_b64 = base64.b64encode(im_bytes)
# im_b64 = im_b64.decode('utf-8')
# print( 'im_b64' , im_b64 )
payload = dict({ 'image' : img_str , 'objects' : objs })
return payload,output_image
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