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video_markup.py
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video_markup.py
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import re
import os
import sys
import random
import math
import numpy as np
import scipy.misc
import cv2
import json
from moviepy.editor import VideoFileClip
from IPython.display import HTML
import logging
#import matplotlib
#import matplotlib.pyplot as plt
#import matplotlib.patches as patches
#import matplotlib.lines as lines
from matplotlib.patches import Polygon
import IPython.display
import colorsys
import boto3
#import coco
import csv
from PIL import Image
from pytesseract import *
sys.path.insert(0,'./pothole') #Relative import
import pothole
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.model import log
#0v1# JC Sept 9, 2018 Initial setup
#FIX FOR MULTITHREADED?
from keras import backend #See get_session()
#>https://github.com/keras-team/keras/issues/2397
#tf# import tensorflow as tf
#tf# global graph,model
#tf# graph = tf.get_default_graph()
#tf# #K.clear_session()
# Root directory of the project
ROOT_DIR = os.getcwd()
# Config ENV
#######################
#import ConfigParser
import configparser as ConfigParser
#BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))
Config = ConfigParser.ConfigParser()
Config.read(ROOT_DIR+"/settings.ini")
AWS_ACCESS_KEY_ID=Config.get('aws','AWS_ACCESS_KEY_ID')
AWS_SECRET_ACCESS_KEY=Config.get('aws','AWS_SECRET_ACCESS_KEY')
os.environ['AWS_ACCESS_KEY_ID'] = AWS_ACCESS_KEY_ID
os.environ['AWS_SECRET_ACCESS_KEY'] = AWS_SECRET_ACCESS_KEY
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Path to trained weights file
# Download this file and place in the root of your
# project (See README file for details)
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "pothole/mask_rcnn_pothole_0005.h5")
# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
config = pothole.PotholeConfig()
TEMP_DIR=ROOT_DIR+"/temp"
# Interface between Sagemaker caller/lambda and backend resources
#- extends flask predictor.py (context request)
#- implements pothole-detection.ipynb
class InferenceConfig(config.__class__):
# Set batch size to 1 since we'll be running inference on
# one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
GPU_COUNT = 1
IMAGES_PER_GPU = 1
def process_image(image, RCNN_MODEL='',title="", figsize=(16, 16), ax=None):
#> pass model through lambda call
# NOTE: The output you return should be a color image (3 channel) for processing video below
# you should return the final output (image with lines are drawn on lanes
#results = VNN.RCNN_MODEL.detect([image], verbose=0)
with backend.get_session().graph.as_default() as g:
#model = load_model(MODEL_PATH)
results = RCNN_MODEL.detect([image], verbose=0) #Throws error if threaded model
r = results[0]
boxes = r['rois']
class_ids = r['class_ids']
scores = r['scores']
N = boxes.shape[0]
# Show area outside image boundaries.
font = cv2.FONT_HERSHEY_DUPLEX
for i in range(N):
class_id = class_ids[i]
score = scores[i] if scores is not None else None
label = 'pothole'
y1, x1, y2, x2 = boxes[i]
#cv2.rectangle(frame, (face_rect.left(),face_rect.top()), (face_rect.right(), face_rect.bottom()), (255,0,0), 3)
cv2.rectangle(image, (x1, y1), (x2, y2), (255,0,0), 3)
x = random.randint(x1, (x1 + x2) // 2)
caption = "{} {:.3f}".format(label, score) if score else label
cv2.putText(image, caption, (x1 + 6, y2 - 6), font, 0.5, (255, 255, 255), 1)
im = Image.fromarray(image)
text = image_to_string(im)
data = GetCoordinates.GetDMS(text)
height = y2-y1
width = x2-x1
area = width * height #in pixels, we don't know height and width of known reference to calculate pixels/inch
if data:
row = 'pothole' + ',' + str(area) + ',' + str(data)
rows.append(row)
return image
def test_s3():
branches=['upload']
#Recall S3_Interface
bucket_name='tests-road-damage'
s3 = boto3.client( 's3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
if 'upload' in branches:
local_filename="small_pot.mp4"
target_filename="sagemaker/"+local_filename
mb_size = os.path.getsize(local_filename) / 1e6
print ("Uploading "+local_filename+" size: "+str(mb_size)+" MB...")
s3.upload_file(local_filename, bucket_name, target_filename)
if 'download' in branches:
local_filename="delthis"
source_filename="sagemaker/sage_handler.py"
response = s3.get_object(Bucket=bucket_name,Key=source_filename)
file_contents = response['Body'].read()
print ("Loaded: "+str(file_contents))
print ("DONE test_s3")
return
def clip_movie():
#https://zulko.github.io/moviepy/getting_started/videoclips.html
filename='C:/scripts-18/sagemaker/pothole-master/samples/pothole/datasets/pothole/potholes_42_miles.mp4'
clip1 = VideoFileClip(filename).subclip(55,58)
clip1.write_videofile("small_pot.mp4") # the gif will have 30 fps
return
class Video_NN_Service(object):
def __init__(self):
return
def initialize(self):
#GLOBAL MODEL
config = InferenceConfig()
# Create model object in inference mode.
self.RCNN_MODEL = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)
# Load weights trained on MS-COCO
self.RCNN_MODEL.load_weights(COCO_MODEL_PATH, by_name=True)
logging.info("Loaded weights...")
return
def _fetch_filenames(self,source_filename):
global TEMP_DIR
if not os.path.exists(TEMP_DIR):hard_fail=no_temp
target_s3_filename=re.sub(r'\.(.{2,5})$',r'_output.\1',source_filename)
temp_input_filename=TEMP_DIR+'/'+re.sub(r'.*\/','',source_filename)
temp_output_filename=re.sub(r'\.(.{2,5})$',r'_output.\1',source_filename)
temp_output_filename=TEMP_DIR+"/"+re.sub(r'.*\/','',temp_output_filename)
return target_s3_filename,temp_input_filename,temp_output_filename
def process_video(self,source_filename="sagemaker/small_pot.mp4",bucket_name='tests-road-damage',is_live=False):
#Generic flow
#Fetch and validate filename
target_s3_filename,temp_input_filename,temp_output_filename=self._fetch_filenames(source_filename)
self.s32temp(source_filename,temp_input_filename,bucket_name)
if not is_live: #debug_force_short_clip:
clip1 = VideoFileClip(temp_input_filename).subclip(0,0.01) #read() -- alternatively, via url
else:
clip1 = VideoFileClip(temp_input_filename)
print ("loaded video clip duration: "+str(clip1.duration))
print ("Writing video to temp: "+temp_output_filename)
#white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s
white_clip = clip1.fl_image(lambda image: process_image(image,RCNN_MODEL=self.RCNN_MODEL)) #NOTE: this function expects color images!!s
white_clip.write_videofile(temp_output_filename, audio=False, bitrate="5000k")
print ("Upload results to s3: "+target_s3_filename)
self.temp2s3(temp_output_filename,target_s3_filename,bucket_name)
if False:
try: os.remove(temp_input_filename)
except:pass
try: os.remove(temp_output_filename)
except:pass
return
def ping(self):
return True
def shutdown(self):
#[] release model/memory
return
def s32temp(self,source_filename,temp_filename,bucket_name,use_cache=True):
#Ideally not "in mem"
#> VideoFileClip requires filename (otherwise url) -- but no file objects
if not os.path.exists(temp_filename) or not use_cache:
s3 = boto3.client( 's3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
print ("Downloading video clip: "+str(source_filename))
video_source=s3.get_object(Bucket=bucket_name,Key=source_filename)['Body'] #StreamingBody then #.read() for file byte string
#Expects filename# self.reader = FFMPEG_VideoReader(filename, pix_fmt=pix_fmt,
fp=open(temp_filename,'wb')
fp.write(video_source.read())
fp.close()
return
def temp2s3(self,local_filename,target_filename,bucket_name):
s3 = boto3.client( 's3', aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)
mb_size = os.path.getsize(local_filename) / 1e6
print ("Uploading "+local_filename+" size: "+str(mb_size)+" MB...")
s3.upload_file(local_filename, bucket_name, target_filename)
return
#Move out here because of multithreads
VNN=Video_NN_Service()
VNN.initialize()
def flask_process_video(s3_source_filename='',s3_bucket='',is_live=False):
global VNN
VNN.process_video(source_filename=s3_source_filename,bucket_name=s3_bucket,is_live=is_live)
return
def run_process_video(source_filename="sagemaker/small_pot.mp4"):
global VNN
VNN=Video_NN_Service()
VNN.initialize()
VNN.process_video(source_filename=source_filename)
VNN.shutdown()
return
def test():
dd='{"input": {"s3_source_filename" : "/sagemaker/small_pot.mp4", "s3_bucket" : "tests-road-damange", "is_live": false} }'
dd=json.loads(dd)
input_json = dd['input']
print ("FO: "+str(input_json))
return
if __name__=='__main__':
branches=['clip_movie']
branches=['test_s3']
branches=['test']
branches=['run_process_video']
for b in branches:
globals()[b]()