التعرف على الصور لحظيا

import cv2

video_capture = cv2.VideoCapture(0)
while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()
    cv2.imshow('Video', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, cleanup
video_capture.release()
cv2.destroyAllWindows()

# Threaded class for performance improvement
class VideoStream:
	
	def __init__(self, src=0):
		self.stream = cv2.VideoCapture(src)
		(self.grabbed, self.frame) = self.stream.read()
		self.stopped = False
		
	def start(self):
		Thread(target=self.update, args=()).start()
		return self

	def update(self):
                while True:
			if self.stopped:
				return
			(self.grabbed, self.frame) = self.stream.read()
	
	def read(self):
                # Return the latest frame
		return self.frame
	
	def stop(self):
		self.stopped = True

class NodeLookup(object):
  def __init__(self,
               label_lookup_path=None,
               uid_lookup_path=None):
    if not label_lookup_path:
      label_lookup_path = os.path.join(
          model_dir, 'imagenet_2012_challenge_label_map_proto.pbtxt')
    if not uid_lookup_path:
      uid_lookup_path = os.path.join(
          model_dir, 'imagenet_synset_to_human_label_map.txt')
    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)

  def load(self, label_lookup_path, uid_lookup_path):

    if not tf.gfile.Exists(uid_lookup_path):
      tf.logging.fatal('File does not exist %s', uid_lookup_path)
    if not tf.gfile.Exists(label_lookup_path):
      tf.logging.fatal('File does not exist %s', label_lookup_path)

    # Loads mapping from string UID to human-readable string
    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
    uid_to_human = {}
    p = re.compile(r'[n\d]*[ \S,]*')
    for line in proto_as_ascii_lines:
      parsed_items = p.findall(line)
      uid = parsed_items[0]
      human_string = parsed_items[2]
      uid_to_human[uid] = human_string

    # Loads mapping from string UID to integer node ID.
    node_id_to_uid = {}
    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
    for line in proto_as_ascii:
      if line.startswith('  target_class:'):
        target_class = int(line.split(': ')[1])
      if line.startswith('  target_class_string:'):
        target_class_string = line.split(': ')[1]
        node_id_to_uid[target_class] = target_class_string[1:-2]

    # Loads the final mapping of integer node ID to human-readable string
    node_id_to_name = {}
    for key, val in node_id_to_uid.items():
      if val not in uid_to_human:
        tf.logging.fatal('Failed to locate: %s', val)
      name = uid_to_human[val]
      node_id_to_name[key] = name

    return node_id_to_name

  def id_to_string(self, node_id):
    if node_id not in self.node_lookup:
      return ''
    return self.node_lookup[node_id]


def create_graph():

  # Creates graph from saved graph_def.pb.
  with tf.gfile.FastGFile(os.path.join(
      model_dir, 'classify_image_graph_def.pb'), 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')


def maybe_download_and_extract():
  # Download and extract model tar file
  dest_directory = model_dir
  if not os.path.exists(dest_directory):
    os.makedirs(dest_directory)
  filename = DATA_URL.split('/')[-1]
  filepath = os.path.join(dest_directory, filename)
  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
  tarfile.open(filepath, 'r:gz').extractall(dest_directory)

from gtts import gTTS
import pygame

tts = gTTS(text='Hello', lang='en')
tts.save("hello.mp3")
pygame.mixer.init()
pygame.mixer.music.load("hello.mp3")
pygame.mixer.music.play()
while pygame.mixer.music.get_busy() == True:
    continue

# Download and create graph
maybe_download_and_extract()
create_graph()

# Variables declarations
frame_count=0
score=0
start = time.time()
pygame.mixer.init()
pred=0
last=0
human_str=None
font=cv2.FONT_HERSHEY_TRIPLEX
font_color=(255,255,255)

# Init video stream
vs = VideoStream(src=0).start()

# Start tensroflow session
with tf.Session() as sess:
        softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')

        while True:
                frame = vs.read()
                frame_count+=1

                # Only run every 5 frames
                if frame_count%5==0:

                        # Save the image as the fist layer of inception is a DecodeJpeg
                        cv2.imwrite("current_frame.jpg",frame)

                        image_data = tf.gfile.FastGFile("./current_frame.jpg", 'rb').read()
                        predictions=sess.run(softmax_tensor,{'DecodeJpeg/contents:0':image_data})

                        predictions = np.squeeze(predictions)
                        node_lookup = NodeLookup()

                        # change n_pred for more predictions
                        n_pred=1
                        top_k = predictions.argsort()[-n_pred:][::-1]
                        for node_id in top_k:
                                human_str_n = node_lookup.id_to_string(node_id)
                                score = predictions[node_id]
                        if score>.5:
                                # Some manual corrections
                                if human_str_n=="stethoscope":human_str_n="Headphones"
                                if human_str_n=="spatula":human_str_n="fork"
                                if human_str_n=="iPod":human_str_n="iPhone"
                                human_str=human_str_n

                                lst=human_str.split()
                                human_str=" ".join(lst[0:2])
                                human_str_filename=str(lst[0])

                        current= time.time()
                        fps=frame_count/(current-start)

                # Speech module        
                if last>40 and not pygame.mixer.music.get_busy() and human_str==human_str_n:
                        pred+=1
                        name=human_str_filename+".mp3"

                        # Only get from google if we dont have it
                        if not os.path.isfile(name):
                                tts = gTTS(text="I see a "+human_str, lang='en')
                                tts.save(name)

                        last=0
                        pygame.mixer.music.load(name)
                        pygame.mixer.music.play()

                # Show info during some time              
                if last<40 and frame_count>10:
                        # Change the text position depending on your camera resolution
                        cv2.putText(frame,human_str, (20,400),font, 1, font_color)
                        cv2.putText(frame,str(np.round(score,2))+"%",(20,440),font,1,font_color)

                if frame_count>20:
                        fps_text="fps: "+str(np.round(fps,2))
                        cv2.putText(frame, fps_text, (460,460), font, 1, font_color)

                cv2.imshow("Frame", frame)
                last+=1


                # if the 'q' key is pressed, stop the loop
                if cv2.waitKey(1) & 0xFF == ord("q"):break

# cleanup everything
vs.stop()
cv2.destroyAllWindows()     
sess.close()
print("Done")