In the last blog we have trained the model and saved the inference graph. In this blog we will learn how to use this inference graph for object detection and how to run our snake game using this trained object detection model.
To play snake game using this trained model, you first need to develop a snake game. But don’t worry you need not to develop it from scratch, you can clone this repository. And if you want to know algorithm behind this code you can follow this blog.
Now we have our snake game next thing is to use this object detection model to play the snake game. To do this we need to run both snake game file and following script from models/research folder simultaneously.
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import os import sys from multiprocessing import Value import cv2 import numpy as np import pyautogui import tensorflow as tf cap = cv2.VideoCapture(0) sys.path.append("..") from object_detection.utils import label_map_util from object_detection.utils import visualization_utils as vis_util # # Model preparation # Path to frozen detection graph. This is the actual model that is used for the object detection. PATH_TO_CKPT = 'snake/frozen_inference_graph.pb' # List of the strings that is used to add correct label for each box. PATH_TO_LABELS = os.path.join('images/data', 'object-detection.pbtxt') NUM_CLASSES = 4 # ## Load a (frozen) 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='') # ## Loading label map 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) with detection_graph.as_default(): # from directkeys import PressKey, ReleaseKey, W # enter your monitor's resolution or use a library to fetch this - I had to hard code due to issues with # dual monitor setup x, y = 288, 512 # init process safe variables for workers objectX, objectY = Value('d', 0.0), Value('d', 0.0) objectX_previous = None objectY_previous = None with tf.Session(graph=detection_graph) as sess: # Definite input and output Tensors for detection_graph 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. detection_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. detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') num_detections = detection_graph.get_tensor_by_name('num_detections:0') while True: ret, image_np = cap.read() # Expand dimensions since the model expects images to have shape: [1, None, None, 3] image_np_expanded = np.expand_dims(image_np, axis=0) # Actual detection. (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) # Visualization of the results of a detection. 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) cv2.imshow('controls detection', image_np) if cv2.waitKey(50) & 0xFF == ord('q'): cv2.destroyAllWindows() break '''MOVE''' # press 'w' if bounding box of finger detected objects = np.where(classes[0] == 1)[0] # calculate center of box if detection exceeds threshold if len(objects) > 0 and scores[0][objects][0] > 0.15: pyautogui.press('up') objects = np.where(classes[0] == 2)[0] # calculate center of box if detection exceeds threshold if len(objects) > 0 and scores[0][objects][0] > 0.15: pyautogui.press('down') objects = np.where(classes[0] == 3)[0] # calculate center of box if detection exceeds threshold if len(objects) > 0 and scores[0][objects][0] > 0.15: pyautogui.press('left') objects = np.where(classes[0] == 4)[0] # calculate center of box if detection exceeds threshold if len(objects) > 0 and scores[0][objects][0] > 0.15: pyautogui.press('right') cap.release() |
In the above code we need to specify path to our inference graph using ” PATH_TO_CKPT ” variable. Also we need to specify ” PATH_TO_LABELS ” variable with path of object-detection.pbtxt file. Then specify number of classes i.e. 4 in our case.
In the above script we have used ” pyautogui ” to press the button when particular hand gesture for a particular direction is detected.
Finally you can play snake game using your hand gestures. Let see some of the results.
Pretty well yeah. This is all for playing snake game using tensorflow object detection API. Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.
which algorithm is used in this project