TFLite_detection_image_modified.py

######## Webcam Object Detection Using Tensorflow-trained Classifier #########
#
# Author: Evan Juras (>97%), some modifications by Christian Baun (<3%)
# License: Apache-2.0 License
# Date: January 10th 2022
# Description: 
# This program uses a TensorFlow Lite object detection model to perform object 
# detection on an image or a folder full of images. It draws boxes and scores 
# around the objects of interest in each image.
#
# This code is mostly from Evan Juras who did a great work in providing helpful
# tutorials about object detection with singe board computers.
# https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10
#
# Evan Juras says this code is based off the TensorFlow Lite image classification example at:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/python/label_image.py
#
# Evan Juras added his own method of drawing boxes and labels using OpenCV.
# Christian Baun modified some path and filename stuff in order to make it work for this prototype

# Import packages
import os
import argparse
import cv2
import numpy as np
import sys
import glob
import importlib.util
import time

from datetime import datetime
# datetime object containing current date and time
now = datetime.now()
# Get UNIX timestamp containing current date and time
timestamp = datetime.timestamp(now)
print('1) UNIX-Timestamp [s] at start: ' + str(timestamp))


# Define and parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--modeldir', help='Folder the .tflite file is located in',
                    required=True)
parser.add_argument('--graph', help='Name of the .tflite file, if different than detect.tflite',
                    default='detect.tflite')
parser.add_argument('--labels', help='Name of the labelmap file, if different than labelmap.txt',
                    default='labelmap.txt')
parser.add_argument('--threshold', help='Minimum confidence threshold for displaying detected objects',
                    default=0.5)
parser.add_argument('--image', help='Name of the single image to perform detection on. To run detection on multiple images, use --imagedir',
                    default=None)
parser.add_argument('--imagedir', help='Name of the folder containing images to perform detection on. Folder must contain only images.',
                    default=None)
parser.add_argument('--edgetpu', help='Use Coral Edge TPU Accelerator to speed up detection',
                    action='store_true')

args = parser.parse_args()

MODEL_NAME = args.modeldir
GRAPH_NAME = args.graph
LABELMAP_NAME = args.labels
min_conf_threshold = float(args.threshold)
use_TPU = args.edgetpu

# Parse input image name and directory. 
IM_NAME = args.image
IM_DIR = args.imagedir

# If both an image AND a folder are specified, throw an error
if (IM_NAME and IM_DIR):
    print('Error! Please only use the --image argument or the --imagedir argument, not both. Issue "python TFLite_detection_image.py -h" for help.')
    sys.exit()

# If neither an image or a folder are specified, default to using 'test1.jpg' for image name
if (not IM_NAME and not IM_DIR):
    IM_NAME = 'test1.jpg'

# Import TensorFlow libraries
# If tflite_runtime is installed, import interpreter from tflite_runtime, else import from regular tensorflow
# If using Coral Edge TPU, import the load_delegate library
pkg = importlib.util.find_spec('tflite_runtime')
if pkg:
    from tflite_runtime.interpreter import Interpreter
    if use_TPU:
        from tflite_runtime.interpreter import load_delegate
else:
    from tensorflow.lite.python.interpreter import Interpreter
    if use_TPU:
        from tensorflow.lite.python.interpreter import load_delegate

# If using Edge TPU, assign filename for Edge TPU model
if use_TPU:
    # If user has specified the name of the .tflite file, use that name, otherwise use default 'edgetpu.tflite'
    if (GRAPH_NAME == 'detect.tflite'):
        GRAPH_NAME = 'edgetpu.tflite'



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

# Define path to images and grab all image filenames
if IM_DIR:
    PATH_TO_IMAGES = os.path.join(CWD_PATH,IM_DIR)
    # Add image formats here
    ext = ['jpeg', 'jpg'] 
    images = []
    [images.extend(glob.glob(PATH_TO_IMAGES + '/*.' + e)) for e in ext]
    print(images)

elif IM_NAME:
    PATH_TO_IMAGES = os.path.join(CWD_PATH,IM_NAME)
    images = glob.glob(PATH_TO_IMAGES)
    print(images)

# Path to .tflite file, which contains the model that is used for object detection
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,GRAPH_NAME)

# Path to label map file
PATH_TO_LABELS = os.path.join(CWD_PATH,MODEL_NAME,LABELMAP_NAME)

# Load the label map
with open(PATH_TO_LABELS, 'r') as f:
    labels = [line.strip() for line in f.readlines()]

# Have to do a weird fix for label map if using the COCO "starter model" from
# https://www.tensorflow.org/lite/models/object_detection/overview
# First label is '???', which has to be removed.
if labels[0] == '???':
    del(labels[0])

most_recent_timestamp = datetime.timestamp(datetime.now()) - timestamp
print('2) Seconds passed since start: ' + str(most_recent_timestamp))

# Load the Tensorflow Lite model.
# If using Edge TPU, use special load_delegate argument
if use_TPU:
    interpreter = Interpreter(model_path=PATH_TO_CKPT,
                              experimental_delegates=[load_delegate('libedgetpu.so.1.0')])
    print(PATH_TO_CKPT)
else:
    interpreter = Interpreter(model_path=PATH_TO_CKPT)
    print(PATH_TO_CKPT)


most_recent_timestamp = datetime.timestamp(datetime.now()) - timestamp
print('3) Seconds passed since start: ' + str(most_recent_timestamp))

interpreter.allocate_tensors()

# Get model details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
height = input_details[0]['shape'][1]
width = input_details[0]['shape'][2]

floating_model = (input_details[0]['dtype'] == np.float32)

input_mean = 127.5
input_std = 127.5

# Loop over every image and perform detection
for image_path in images:

    # Load image and resize to expected shape [1xHxWx3]
    image = cv2.imread(image_path)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    imH, imW, _ = image.shape 
    image_resized = cv2.resize(image_rgb, (width, height))
    input_data = np.expand_dims(image_resized, axis=0)

    # Normalize pixel values if using a floating model (i.e. if model is non-quantized)
    if floating_model:
        input_data = (np.float32(input_data) - input_mean) / input_std

    # Perform the actual detection by running the model with the image as input
    interpreter.set_tensor(input_details[0]['index'],input_data)

    interpreter.invoke()

    # Retrieve detection results
    boxes = interpreter.get_tensor(output_details[0]['index'])[0] # Bounding box coordinates of detected objects
    classes = interpreter.get_tensor(output_details[1]['index'])[0] # Class index of detected objects
    scores = interpreter.get_tensor(output_details[2]['index'])[0] # Confidence of detected objects
    #num = interpreter.get_tensor(output_details[3]['index'])[0]  # Total number of detected objects (inaccurate and not needed)

    # We need these values initially before the loop just in case we do not detect anything.
    date_string = now.strftime("%Y-%m-%d")
    time_string = now.strftime("%H-%M-%S")

    # Initially we have no objects detected...
    dateandtimeprintedout = 0

    # Loop over all detections and draw detection box if confidence is above minimum threshold
    for i in range(len(scores)):
        if ((scores[i] > min_conf_threshold) and (scores[i] <= 1.0)):

            # If it is the first object in the image detected... (first iteration)...
            if dateandtimeprintedout == 0:
                # Print the date and time information one time because we deteced an object
                # dd/mm/YY H:M:S
                date_string = now.strftime("%Y-%m-%d")
                time_string = now.strftime("%H-%M-%S")
                # Integer is sufficient... we dont't need the floating point number here...
                timestamp_integer = int(timestamp)
                print("===", timestamp_integer, date_string, time_string, "===")	
                # We have at least a single object detected.
                # We will not print out the time and date information twice
                dateandtimeprintedout = 1

            # Get bounding box coordinates and draw box
            # Interpreter can return coordinates that are outside of image dimensions, need to force them to be within image using max() and min()
            ymin = int(max(1,(boxes[i][0] * imH)))
            xmin = int(max(1,(boxes[i][1] * imW)))
            ymax = int(min(imH,(boxes[i][2] * imH)))
            xmax = int(min(imW,(boxes[i][3] * imW)))
            
            cv2.rectangle(image, (xmin,ymin), (xmax,ymax), (10, 255, 0), 2)

            # Draw label
            object_name = labels[int(classes[i])] # Look up object name from "labels" array using class index

            # Return some information about the detected object in command line
            print('Detected Object: '+ str(object_name) + ' with ' + str(int(scores[i]*100)) + ' % ' );

            label = '%s: %d%%' % (object_name, int(scores[i]*100)) # Example: 'person: 72%'
            labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.7, 2) # Get font size
            label_ymin = max(ymin, labelSize[1] + 10) # Make sure not to draw label too close to top of window
            cv2.rectangle(image, (xmin, label_ymin-labelSize[1]-10), (xmin+labelSize[0], label_ymin+baseLine-10), (255, 255, 255), cv2.FILLED) # Draw white box to put label text in
            cv2.putText(image, label, (xmin, label_ymin-7), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0), 2) # Draw label text

    # All the results have been drawn on the image, now display the image
    # cv2.imshow('Object detector', image)
  
    if IM_DIR:
        print(image_path)

        # Using cv2.imwrite() method
        # Saving the image
        cv2.imwrite(image_path, image)

        # Print out some information
        print('File '+ repr(image_path) + ' stored\n');


    elif IM_NAME:
        # Create filename
        filename = str(IM_NAME)
        print(filename);
        # filename = date_string+'_'+time_string+'_detected_'+str(IM_NAME)

        # Using cv2.imwrite() method
        # Saving the image
        cv2.imwrite(filename, image)

        # Print out some information
        print('File '+ repr(filename) + ' stored\n');



    most_recent_timestamp = datetime.timestamp(datetime.now()) - timestamp
    print('4) Seconds passed since start: ' + str(most_recent_timestamp))

    # Press any key to continue to next image, or press 'q' to quit
    #if cv2.waitKey(0) == ord('q'):
    #    break

# Clean up
# cv2.destroyAllWindows()