In [1]:
import numpy as np
import matplotlib.pyplot as plt
import cv2
import glob
from moviepy.editor import VideoFileClip
from IPython.display import HTML
%matplotlib inline

import keras
from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.core import Flatten, Dense, Activation, Reshape

from utils import load_weights, Box, yolo_net_out_to_car_boxes, draw_box


Using TensorFlow backend.

Set the following path to your working directory for this project


In [2]:
WorkingDir = '/media/feliz/Safira/GitHub/Gridkey/Car-Detection/'

Constructing the tiny-yolo model


In [3]:
keras.backend.set_image_dim_ordering('th')

In [4]:
model = Sequential()
model.add(Convolution2D(16, 3, 3,input_shape=(3,448,448),border_mode='same',subsample=(1,1)))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2),border_mode='valid'))
model.add(Convolution2D(64,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2),border_mode='valid'))
model.add(Convolution2D(128,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2),border_mode='valid'))
model.add(Convolution2D(256,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2),border_mode='valid'))
model.add(Convolution2D(512,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(MaxPooling2D(pool_size=(2, 2),border_mode='valid'))
model.add(Convolution2D(1024,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(Convolution2D(1024,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(Convolution2D(1024,3,3 ,border_mode='same'))
model.add(LeakyReLU(alpha=0.1))
model.add(Flatten())
model.add(Dense(256))
model.add(Dense(4096))
model.add(LeakyReLU(alpha=0.1))
model.add(Dense(1470))

# model.summary()

Loading the weights


In [5]:
load_weights(model,WorkingDir+'tiny-yolo.mod.weights')
# load_weights(model,'tiny-yolo.weights')

Applying the model on images

Single Image

In [7]:
imagePath = WorkingDir+'Testing/Images/004.jpg'
image = plt.imread(imagePath)
image_crop = image[300:650,500:,:]
resized = cv2.resize(image_crop,(448,448))
batch = np.transpose(resized,(2,0,1))
batch = 2*(batch/255.) - 1
batch = np.expand_dims(batch, axis=0)
out = model.predict(batch)
boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
f,(ax1,ax2) = plt.subplots(1,2,figsize=(16,6))
ax1.imshow(image)
ax2.imshow(draw_box(boxes,plt.imread(imagePath),[[500,1280],[300,650]]))
ax2.text(1200,100,str(len(boxes)),FontSize = 25, Color = 'yellow', FontWeight = 'bold')
with open(WorkingDir+'Output/VechileCOunt.txt', 'w') as f:
    f.write(str(len(boxes)))


Multiple Image

In [31]:
images = [plt.imread(file) for file in glob.glob(WorkingDir+'Testing/Images/*.jpg')]
batch = np.array([np.transpose(cv2.resize(image[300:650,500:,:],(448,448)),(2,0,1)) 
                  for image in images])
batch = 2*(batch/255.) - 1
out = model.predict(batch)
f,((ax1,ax2),(ax3,ax4),(ax5,ax6)) = plt.subplots(3,2,figsize=(11,10))
for i,ax in zip(range(len(batch)),[ax1,ax2,ax3,ax4,ax5,ax6]):
    boxes = yolo_net_out_to_car_boxes(out[i], threshold = 0.17)
    ax.imshow(draw_box(boxes,images[i],[[500,1280],[300,650]]))
    ax.text(1200,100,str(len(boxes)),FontSize = 25, Color = 'yellow', FontWeight = 'bold')


Applying the model on Video


In [11]:
def frame_func(image):
    crop = image[300:650,500:,:]
    resized = cv2.resize(crop,(448,448))
    batch = np.array([resized[:,:,0],resized[:,:,1],resized[:,:,2]])
    batch = 2*(batch/255.) - 1
    batch = np.expand_dims(batch, axis=0)
    out = model.predict(batch)
    boxes = yolo_net_out_to_car_boxes(out[0], threshold = 0.17)
    return draw_box(boxes,image,[[500,1280],[300,650]])

In [14]:
project_video_output = WorkingDir+'Output/Videos/output-4.mp4'
clip1 = VideoFileClip(WorkingDir+'Testing/Videos/4.mp4')

In [ ]:
lane_clip = clip1.fl_image(frame_func)
%time lane_clip.write_videofile(project_video_output, audio=False)


[MoviePy] >>>> Building video /media/feliz/Safira/GitHub/GridKey/Car Detection/Output/Videos/output-4.mp4
[MoviePy] Writing video /media/feliz/Safira/GitHub/GridKey/Car Detection/Output/Videos/output-4.mp4

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In [ ]: