testDetector.py

# Code adapted from https://github.com/fizyr/keras-retinanet
# jasperebrown@gmail.com
# 2020

# This script loads a single image, runs inferencing on it
# and saves that image back out with detections overalaid.

# You need to set the model_path and image_path below

# import keras
import keras

# import keras_retinanet
from keras_retinanet import models
from keras_retinanet.utils.image import preprocess_image, resize_image

# import miscellaneous modules
import cv2
import os
import numpy as np
import time
from PIL import Image

# set tf backend to allow memory to grow, instead of claiming everything
import tensorflow as tf

model_path = '/home/jasper/RetinanetTutorial/RetinanetModels/PlumsInference.h5'
image_path = '/home/jasper/RetinanetTutorial/Retinanet-Tutorial/plumsTest.png'
image_output_path = '/home/jasper/RetinanetTutorial/Retinanet-Tutorial/plumsTest_detected.png'
confidence_cutoff = 0.5 #Detections below this confidence will be ignored

def get_session():
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    return tf.Session(config=config)

# use this environment flag to change which GPU to use
#os.environ["CUDA_VISIBLE_DEVICES"] = "1"

# set the modified tf session as backend in keras
keras.backend.tensorflow_backend.set_session(get_session())

# adjust this to point to your downloaded/trained model
# models can be downloaded here: https://github.com/fizyr/keras-retinanet/releases
#model_path = os.path.join('..', 'snapshots', 'resnet50_coco_best_v2.1.0.h5')

print("Loading image from {}".format(image_path))
image = np.asarray(Image.open(image_path).convert('RGB'))
image = image[:, :, ::-1].copy()

# load retinanet model
print("Loading Model: {}".format(model_path))
model = models.load_model(model_path, backbone_name='resnet50')

#Check that it's been converted to an inference model
try:
    model = models.convert_model(model)
except:
    print("Model is likely already an inference model")

# load label to names mapping for visualization purposes
labels_to_names = {0: 'plum', 1: 'green_plum'}

# copy to draw on
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)

# Image formatting specific to Retinanet
image = preprocess_image(image)
image, scale = resize_image(image)

# Run the inference
start = time.time()

boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
print("processing time: ", time.time() - start)

# correct for image scale
boxes /= scale

# visualize detections
for box, score, label in zip(boxes[0], scores[0], labels[0]):
    # scores are sorted so we can break
    if score < confidence_cutoff:
        break

    #Add boxes and captions
    color = (255, 255, 255)
    thickness = 2
    b = np.array(box).astype(int)
    cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), color, thickness, cv2.LINE_AA)

    if(label > len(labels_to_names)):
        print("WARNING: Got unknown label, using 'detection' instead")
        caption = "Detection {:.3f}".format(score)
    else:
        caption = "{} {:.3f}".format(labels_to_names[label], score)

    cv2.putText(draw, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
    cv2.putText(draw, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)

#Write out image
draw = Image.fromarray(draw)
draw.save(image_output_path)