Python cv2.imencode方法代碼示例

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def make_web(queue):
    app = Flask(__name__)

    @app.route('/')
    def index():
        return render_template('index.html')

    def gen():
        while True:
            frame = queue.get()
            _, frame = cv2.imencode('.JPEG', frame)
            yield (b'--frame\r\n'
                   b'Content-Type: image/jpeg\r\n\r\n' + frame.tostring() + b'\r\n')

    @app.route('/video_feed')
    def video_feed():
        return Response(gen(),
                        mimetype='multipart/x-mixed-replace; boundary=frame')

    try:
        app.run(host='0.0.0.0', port=8889)
    except:
        print('unable to open port') 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def main():
    args = common.parse_num_sources_server_host()
    capture = cv2.VideoCapture(0)
    def gen_producer(n):
        text = 'client {}'.format(n)
        async def producer():
            _, frame = capture.read()
            cv2.putText(frame, text, ORG, FONT_FACE, FONT_SCALE, COLOR)
            _, jpeg_frame=cv2.imencode('.jpg', frame)
            input_frame = gabriel_pb2.InputFrame()
            input_frame.payload_type = gabriel_pb2.PayloadType.IMAGE
            input_frame.payloads.append(jpeg_frame.tostring())

            return input_frame
        return producer

    producer_wrappers = [
        ProducerWrapper(producer=gen_producer(i), source_name=str(i))
        for i in range(args.num_sources)
    ]
    client = WebsocketClient(
        args.server_host, common.WEBSOCKET_PORT, producer_wrappers, consumer)
    client.launch() 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def get_producer_wrappers(self):
        async def producer():
            _, frame = self._video_capture.read()
            if frame is None:
                return None

            frame = self._preprocess(frame)
            _, jpeg_frame = cv2.imencode('.jpg', frame)

            input_frame = gabriel_pb2.InputFrame()
            input_frame.payload_type = gabriel_pb2.PayloadType.IMAGE
            input_frame.payloads.append(jpeg_frame.tostring())

            extras = self._produce_extras()
            if extras is not None:
                input_frame.extras.Pack(extras)

            return input_frame

        return [
            ProducerWrapper(producer=producer, source_name=self._source_name)
        ] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def mini_crop_by_landmarks(self, sample_list, pad_rate, img_format):
        """
        Crop full image to mini. Only keep valid image to save
        Args:
            sample_list: (image, landmarks)
            pad_rate: up scale rate
            img_format: "RGB" or "BGR"
        Return:
            new sample list
        Raises:
            No
        """
        new_sample_list = []
        for sample in sample_list:
            image = cv2.imread(sample[0])
            if img_format == 'RGB':
                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            landmarks = sample[1]
            (x1, y1, x2, y2), _, _, _ = self.get_bbox_of_landmarks(image, landmarks, pad_rate, 0.5)
            new_sample_list.append(
                (cv2.imencode(".jpg", image[y1:y2, x1:x2])[1], landmarks - (x1, y1))
            )
            return new_sample_list 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def callback(self, image_msg):
        cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")
        # copy from
        # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/models/image/imagenet/classify_image.py
        image_data = cv2.imencode('.jpg', cv_image)[1].tostring()
        # Creates graph from saved GraphDef.
        softmax_tensor = self._session.graph.get_tensor_by_name('softmax:0')
        predictions = self._session.run(
            softmax_tensor, {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)
        # Creates node ID --> English string lookup.
        node_lookup = classify_image.NodeLookup()
        top_k = predictions.argsort()[-self.use_top_k:][::-1]
        for node_id in top_k:
            human_string = node_lookup.id_to_string(node_id)
            score = predictions[node_id]
            if score > self.score_threshold:
                rospy.loginfo('%s (score = %.5f)' % (human_string, score))
                self._pub.publish(human_string) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def gen_livestream():
    global last_frame
    while True:
        if app.queue.qsize():
            frame = base64.b64decode(app.queue.get().split('base64')[-1])
            last_frame = frame
        else:
            if last_frame is None:
                fh = open(d+"/static/black.jpg", "rb")
                frame = fh.read()
                fh.close()
            else:
                frame = last_frame
        if last_frame:
            img_np = np.array(Image.open(io.BytesIO(frame)))
            img_np = detect_object(img_np)
            frame = cv2.imencode('.jpg', cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB))[1].tobytes()
        yield (b'--frame\r\n'
               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n') 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def get_frame(self):
        _, frame = self.video.read()
        img = cv2.cvtColor(frame.copy(), cv2.COLOR_BGR2RGB)
        image_np_expanded = np.expand_dims(img, axis = 0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        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')
        (boxes, scores, classes, num_detections) = sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict = {image_tensor: image_np_expanded},
        )
        vis_util.visualize_boxes_and_labels_on_image_array(
            frame,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates = True,
            line_thickness = 8,
        )
        # return BGR, cv2 will returned BGR to RGB
        return cv2.imencode('.jpg', frame)[1].tobytes() 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def write_log(self, results):
    """Process results
    Args:
      results: y_out, s_out
    """
    inp = results['_batches'][0]
    y_out = results['y_out']
    s_out = results['s_out']
    with h5py.File(self.dataset.h5_fname, 'r+') as h5f:
      print inp['idx_map']
      for ii in xrange(y_out.shape[0]):
        idx = inp['idx_map'][ii]
        group = h5f[self.dataset.get_str_id(idx)]
        if 'instance_pred' in group:
          del group['instance_pred']
        for ins in xrange(y_out.shape[1]):
          y_out_arr = y_out[ii, ins]
          y_out_arr = (y_out_arr * 255).astype('uint8')
          y_out_str = cv2.imencode('.png', y_out_arr)[1]
          group['instance_pred/{:02d}'.format(ins)] = y_out_str
        if 'score_pred' in group:
          del group['score_pred']
        group['score_pred'] = s_out[ii] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def recoginize_weapons(self, weapons_list):
        payload = []

        for img_weapon in weapons_list:
            result, img_weapon_png = cv2.imencode('.png', img_weapon)
            payload.append(img_weapon_png.tostring())

        response = self._request_func(
            '/api/v1/recoginizer/weapon',
            payload,
        )

        # Validate the response.
        assert response is not None, 'NO API Response.'
        assert response.get('status', None) == 'ok', 'API Error.'

        # Decode the response.
        ret = []
        for entry in response['weapons']:
            ret.append(entry.get('weapon', None))

        return ret 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def recoginize_abilities(self, abilities_list):
        payload = []

        for img_ability in abilities_list:
            result, img_ability_png = cv2.imencode('.png', img_ability)
            payload.append(img_ability_png.tostring())

        response = self._request_func(
            '/api/v1/recoginizer/ability',
            payload,
        )

        # Validate the response.
        assert response is not None, 'NO API Response.'
        assert response.get('status', None) == 'ok', 'API Error.'

        # Decode the response.
        ret = []
        for entry in response['abilities']:
            ret.append(entry.get('ability', None))

        return ret 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def recoginize_deadly_weapons(self, deadly_weapons_list):
        if len(deadly_weapons_list) == 0:
            return None
        images = self.pack_deadly_weapons_image(deadly_weapons_list)

        payload = {
            'game_language': Localization.get_game_languages()[0],
            'sample_height': deadly_weapons_list[0].shape[0],
            'sample_width': deadly_weapons_list[0].shape[1],
            'samples': cv2.imencode('.png', images)[1].tostring()
        }

        response = self._request_func(
            '/api/v1/recoginizer/deadly_weapon',
            payload,
        )

        if response.get('status', None) != 'ok':
            return {'status': 'error'}

        return response 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def callback(self, image_msg):
        cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")
        image_data = cv2.imencode('.jpg', cv_image)[1].tostring()

        # Creates graph from saved GraphDef.
        softmax_tensor = self._session.graph.get_tensor_by_name('softmax:0')
        predictions = self._session.run(
            softmax_tensor, {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        # Creates node ID --> English string lookup.
        node_lookup = self.load(PATH_TO_LABELS, PATH_TO_UID)
        top_k = predictions.argsort()[-self.use_top_k:][::-1]
        for node_id in top_k:
            
            if node_id not in node_lookup:
                human_string = ''
            else:
                human_string = node_lookup[node_id]

            score = predictions[node_id]
            if score > self.score_threshold:
                rospy.loginfo('%s (score = %.5f)' % (human_string, score))
                self._pub.publish(human_string) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def infer(image, model='cmu', resize='0x0', resize_out_ratio=4.0):
    """

    :param image:
    :param model:
    :param resize:
    :param resize_out_ratio:
    :return: coco_style_keypoints array
    """
    w, h = model_wh(resize)
    e = get_estimator(model, resize)

    # estimate human poses from a single image !
    image = common.read_imgfile(image, None, None)
    if image is None:
        raise Exception('Image can not be read, path=%s' % image)
    humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=resize_out_ratio)
    image_h, image_w = image.shape[:2]

    if "TERM_PROGRAM" in os.environ and 'iTerm' in os.environ["TERM_PROGRAM"]:
        image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
        image_str = cv2.imencode(".jpg", image)[1].tostring()
        print("\033]1337;File=name=;inline=1:" + base64.b64encode(image_str).decode("utf-8") + "\a")

    return [(eval.write_coco_json(human, image_w, image_h), human.score) for human in humans] 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def pack_img(header, img, quality=95, img_fmt='.jpg'):
    """Pack an image into ``MXImageRecord``.

    Parameters
    ----------
    header : IRHeader
        Header of the image record.
        ``header.label`` can be a number or an array. See more detail in ``IRHeader``.
    img : numpy.ndarray
        Image to be packed.
    quality : int
        Quality for JPEG encoding in range 1-100, or compression for PNG encoding in range 1-9.
    img_fmt : str
        Encoding of the image (.jpg for JPEG, .png for PNG).

    Returns
    -------
    s : str
        The packed string.

    Examples
    --------
    >>> label = 4 # label can also be a 1-D array, for example: label = [1,2,3]
    >>> id = 2574
    >>> header = mx.recordio.IRHeader(0, label, id, 0)
    >>> img = cv2.imread('test.jpg')
    >>> packed_s = mx.recordio.pack_img(header, img)
    """
    assert cv2 is not None
    jpg_formats = ['.JPG', '.JPEG']
    png_formats = ['.PNG']
    encode_params = None
    if img_fmt.upper() in jpg_formats:
        encode_params = [cv2.IMWRITE_JPEG_QUALITY, quality]
    elif img_fmt.upper() in png_formats:
        encode_params = [cv2.IMWRITE_PNG_COMPRESSION, quality]

    ret, buf = cv2.imencode(img_fmt, img, encode_params)
    assert ret, 'failed to encode image'
    return pack(header, buf.tostring()) 

# 需要導入模塊: import cv2 [as 別名]
# 或者: from cv2 import imencode [as 別名]
def write_image(image_path, rgb):
  ext = os.path.splitext(image_path)[1]
  with gfile.GFile(image_path, 'w') as f:
    img_str = cv2.imencode(ext, rgb[:,:,::-1])[1].tostring()
    f.write(img_str)