realtime_webcam.py

import os
import cv2
import numpy as np
import tensorflow as tf
import sys


import argparse
import logging
import time


from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path, model_wh


fps_time = 0

import win32com.client as wincl
speak = wincl.Dispatch("SAPI.SpVoice")

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")

# Import utilites
from utils import label_map_util
#from utils import visualization_utils as vis_util
from utils import visualization_utils_speech as vis_util







speak.Speak('action detection starting, please wait')
if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam')
    parser.add_argument('--camera', type=int, default=0)

    parser.add_argument('--resize', type=str, default='1280x720',
                        help='if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ')
    parser.add_argument('--resize-out-ratio', type=float, default=4.0,
                        help='if provided, resize heatmaps before they are post-processed. default=1.0')

    parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin')
    parser.add_argument('--show-process', type=bool, default=False,
                        help='for debug purpose, if enabled, speed for inference is dropped.')
    args = parser.parse_args()









    # Name of the directory containing the object detection module we're using
    MODEL_NAME = 'inference_graph'

    # Grab path to current working directory
    CWD_PATH = os.getcwd()

    # Path to frozen detection graph .pb file, which contains the model that is used
    # for object detection.
    PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')

    # Path to label map file
    PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')

    # Number of classes the object detector can identify
    NUM_CLASSES = 10

    ## Load the label map.
    # Label maps map indices to category names, so that when our convolution
    # network predicts `5`, we know that this corresponds to `king`.
    # Here we use internal utility functions, but anything that returns a
    # dictionary mapping integers to appropriate string labels would be fine
    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 = label_map_util.create_category_index(categories)

    # Load the Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.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='')

        sess = tf.Session(graph=detection_graph)

    # Define input and output tensors (i.e. data) for the object detection classifier

    # Input tensor is the image
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')

    # Output tensors are the detection boxes, scores, and classes
    # Each box represents a part of the image where a particular object was detected
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')

    # Each score represents level of confidence for each of the objects.
    # The score is shown on the result image, tog ether with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')

    # Number of objects detected
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')

    # Initialize webcam feed
    video = cv2.VideoCapture(0)
    ret = video.set(3,1280)
    ret = video.set(4,720)

    start_time = time.time()


    w, h = model_wh(args.resize)
    if w > 0 and h > 0:
        e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
    else:
        e = TfPoseEstimator(get_graph_path(args.model), target_size=(1280, 720))



    while(True):

        # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
        # i.e. a single-column array, where each item in the column has the pixel RGB value
        ret, frame = video.read()
        frame_expanded = np.expand_dims(frame, axis=0)

        # Perform the actual detection by running the model with the image as input
        (boxes, scores, classes, num) = sess.run(
            [detection_boxes, detection_scores, detection_classes, num_detections],
            feed_dict={image_tensor: frame_expanded})

        # Draw the results of the detection (aka 'visulaize the results')
        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,
            min_score_thresh=0.60)
        #print(np.squeeze(classes),np.squeeze(boxes))


        #pose
        humans = e.inference(frame, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)
        image = TfPoseEstimator.draw_humans(frame, humans, imgcopy=False)
        cv2.putText(frame,"FPS: %f" % (1.0 / (time.time() - fps_time)),
                    (10, 10),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                    (0, 255, 0), 2)

        if (time.time() -  start_time) > 30:
            break

        # All the results have been drawn on the frame, so it's time to display it.
        cv2.imshow('ProjectInt', frame)
        fps_time = time.time()

        # Press 'q' to quit
        if cv2.waitKey(1) == ord('q'):
            break

# Clean up
video.release()
cv2.destroyAllWindows()