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webcam_blind_voice.py
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webcam_blind_voice.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Apr 28 15:22:14 2020
@author: AryanG
"""
import numpy as np
import os
import six.moves.urllib as urllib
import urllib.request as allib
import sys
import tarfile
import tensorflow as tf
import zipfile
import time
import pytesseract
import engineio
import torch
from torch.autograd import Variable as V
import models as models
from torchvision import transforms as trn
from torch.nn import functional as F
import pyttsx3
#from .engine import Engine
engine =pyttsx3.init()
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
arch = 'resnet18'
model_file = 'whole_%s_places365_python36.pth.tar' % arch
if not os.access(model_file, os.W_OK):
weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
os.system('wget ' + weight_url)
#= label_map_util.create_category_index(categories)
pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files (x86)\\Tesseract-OCR\\tesseract'
from utils import label_map_util
#/object_detection/' m2
from utils import visualization_utils as vis_util
MODEL_NAME = 'ssd_inception_v2_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
#DOWNLOAD_BASE = 'http://download.tensorflow.org/models m1
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
if not os.path.exists(MODEL_NAME + '/frozen_inference_graph.pb'):
print ('Downloading the model')
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
print ('Download complete')
else:
print ('Model already exists')
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.compat.v1.GraphDef()
with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index =
#
url='http://10.67.208.240:8080//shot.jpg'
import cv2
cap = cv2.VideoCapture(0)
with detection_graph.as_default():
with tf.compat.v1.Session(graph=detection_graph) as sess:
ret = True
while (ret):
ret,image_np = cap.read()
if cv2.waitKey(20) & 0xFF == ord('b'):
cv2.imwrite('opencv'+'.jpg', image_np)
model_file = 'whole_%s_places365_python36.pth.tar' % arch
if not os.access(model_file, os.W_OK):
weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
os.system('wget ' + weight_url)
useGPU = 1
if useGPU == 1:
model = torch.load(model_file)
else:
model = torch.load(model_file, map_location=lambda storage, loc: storage) # model trained in GPU could be deployed in CPU machine like this!
model.eval()
centre_crop = trn.Compose([
trn.Resize((256,256)),
trn.CenterCrop(224),
trn.ToTensor(),
trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
file_name = 'categories_places365.txt'
if not os.access(file_name, os.W_OK):
synset_url = 'https://raw.githubusercontent.com/csailvision/places365/master/categories_places365.txt'
os.system('wget ' + synset_url)
classes = list()
with open(file_name) as class_file:
for line in class_file:
classes.append(line.strip().split(' ')[0][3:])
classes = tuple(classes)
img_name = 'opencv.jpg'
if not os.access(img_name, os.W_OK):
img_url = 'http://places.csail.mit.edu/demo/' + img_name
os.system('wget ' + img_url)
img = Image.open(img_name)
input_img = V(centre_crop(img).unsqueeze(0), volatile=True)
logit = model.forward(input_img)
h_x = F.softmax(logit, 1).data.squeeze()
probs, idx = h_x.sort(0, True)
print('POSSIBLE SCENES ARE: ' + img_name)
engine.say("Possible Scene may be")
engine.say(img_name)
for i in range(0, 5):
engine.say(classes[idx[i]])
print('{}'.format(classes[idx[i]]))
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
if cv2.waitKey(2) & 0xFF == ord('a'):
vis_util.vislize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
else:
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
if cv2.waitKey(2) & 0xFF == ord('r'):
text=pytesseract.image_to_string(image_np)
print(text)
engine.say(text)
engine.runAndWait()
for i,b in enumerate(boxes[0]):
# car bus truck
if classes[0][i] == 3 or classes[0][i] == 6 or classes[0][i] == 8:
if scores[0][i] >= 0.5:
mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
if apx_distance <=0.5:
if mid_x > 0.3 and mid_x < 0.7:
cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
print("Warning -Vehicles Approaching")
engine.say("Warning -Vehicles Approaching")
engine.runAndWait()
if classes[0][i] ==44:
if scores[0][i] >= 0.5:
mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
print(apx_distance)
engine.say(apx_distance)
engine.say("units")
engine.say("BOTTLE IS AT A SAFER DISTANCE")
if apx_distance <=0.5:
if mid_x > 0.3 and mid_x < 0.7:
cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
print("Warning -BOTTLE very close to the frame")
engine.say("Warning -BOTTLE very close to the frame")
engine.runAndWait()
if classes[0][i] ==1:
if scores[0][i] >= 0.5:
mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
apx_distance = round(((1 - (boxes[0][i][3] - boxes[0][i][1]))**4),1)
cv2.putText(image_np, '{}'.format(apx_distance), (int(mid_x*800),int(mid_y*450)), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
print(apx_distance)
engine.say(apx_distance)
engine.say("units")
engine.say("Person is AT A SAFER DISTANCE")
if apx_distance <=0.5:
if mid_x > 0.3 and mid_x < 0.7:
cv2.putText(image_np, 'WARNING!!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0,0,255), 3)
print("Warning -Person very close to the frame")
engine.say("Warning -Person very close to the frame")
engine.runAndWait()
# plt.figure(figsize=IMAGE_SIZE)
# plt.imshow(image_np)
#cv2.imshow('IPWebcam',image_np)
cv2.imshow('image',cv2.resize(image_np,(1024,768)))
if cv2.waitKey(2) & 0xFF == ord('t'):
cv2.destroyAllWindows()
cap.release()
break
#{1: {'id': 1, 'name': 'person'}, 2: {'id': 2, 'name': 'bicycle'}, 3: {'id': 3, 'name': 'car'}, 4: {'id': 4, 'name': 'motorcycle'}, 5: {'id': 5, 'name': 'airplane'}, 6: {'id': 6, 'name': 'bus'}, 7: {'id': 7, 'name': 'train'}, 8: {'id': 8, 'name': 'truck'}, 9: {'id': 9, 'name': 'boat'}, 10: {'id': 10, 'name': 'traffic light'}, 11: {'id': 11, 'name': 'fire hydrant'}, 13: {'id': 13, 'name': 'stop sign'}, 14: {'id': 14, 'name': 'parking meter'}, 15: {'id': 15, 'name': 'bench'}, 16: {'id': 16, 'name': 'bird'}, 17: {'id': 17, 'name': 'cat'}, 18: {'id': 18, 'name': 'dog'}, 19: {'id': 19, 'name': 'horse'}, 20: {'id': 20, 'name': 'sheep'}, 21: {'id': 21, 'name': 'cow'}, 22: {'id': 22, 'name': 'elephant'}, 23: {'id': 23, 'name': 'bear'}, 24: {'id': 24, 'name': 'zebra'}, 25: {'id': 25, 'name': 'giraffe'}, 27: {'id': 27, 'name': 'backpack'}, 28: {'id': 28, 'name': 'umbrella'}, 31: {'id': 31, 'name': 'handbag'}, 32: {'id': 32, 'name': 'tie'}, 33: {'id': 33, 'name': 'suitcase'}, 34: {'id': 34, 'name': 'frisbee'}, 35: {'id': 35, 'name': 'skis'}, 36: {'id': 36, 'name': 'snowboard'}, 37: {'id': 37, 'name': 'sports ball'}, 38: {'id': 38, 'name': 'kite'}, 39: {'id': 39, 'name': 'baseball bat'}, 40: {'id': 40, 'name': 'baseball glove'}, 41: {'id': 41, 'name': 'skateboard'}, 42: {'id': 42, 'name': 'surfboard'}, 43: {'id': 43, 'name': 'tennis racket'}, 44: {'id': 44, 'name': 'bottle'}, 46: {'id': 46, 'name': 'wine glass'}, 47: {'id': 47, 'name': 'cup'}, 48: {'id': 48, 'name': 'fork'}, 49: {'id': 49, 'name': 'knife'}, 50: {'id': 50, 'name': 'spoon'}, 51: {'id': 51, 'name': 'bowl'}, 52: {'id': 52, 'name': 'banana'}, 53: {'id': 53, 'name': 'apple'}, 54: {'id': 54, 'name': 'sandwich'}, 55: {'id': 55, 'name': 'orange'}, 56: {'id': 56, 'name': 'broccoli'}, 57: {'id': 57, 'name': 'carrot'}, 58: {'id': 58, 'name': 'hot dog'}, 59: {'id': 59, 'name': 'pizza'}, 60: {'id': 60, 'name': 'donut'}, 61: {'id': 61, 'name': 'cake'}, 62: {'id': 62, 'name': 'chair'}, 63: {'id': 63, 'name': 'couch'}, 64: {'id': 64, 'name': 'potted plant'}, 65: {'id': 65, 'name': 'bed'}, 67: {'id': 67, 'name': 'dining table'}, 70: {'id': 70, 'name': 'toilet'}, 72: {'id': 72, 'name': 'tv'}, 73: {'id': 73, 'name': 'laptop'}, 74: {'id': 74, 'name': 'mouse'}, 75: {'id': 75, 'name': 'remote'}, 76: {'id': 76, 'name': 'keyboard'}, 77: {'id': 77, 'name': 'cell phone'}, 78: {'id': 78, 'name': 'microwave'}, 79: {'id': 79, 'name': 'oven'}, 80: {'id': 80, 'name': 'toaster'}, 81: {'id': 81, 'name': 'sink'}, 82: {'id': 82, 'name': 'refrigerator'}, 84: {'id': 84, 'name': 'book'}, 85: {'id': 85, 'name': 'clock'}, 86: {'id': 86, 'name': 'vase'}, 87: {'id': 87, 'name': 'scissors'}, 88: {'id': 88, 'name': 'teddy bear'}, 89: {'id': 89, 'name': 'hair drier'}, 90: {'id': 90, 'name': 'toothbrush'}}
#open("yolo-coco/coco.names").read().strip().split("\n")