darknet_images.py batch detection is not working?

[mkleong96]

batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width,
OSError: exception: access violation reading 0x0000018D331F1040

I got the error above, when using the darknet_images.py to do the batch prediction, anyone having the same issue using the batch detection in the darknet_images.py?

Hope anyone can help me out on this. Thank you very much!

[stephanecharette]

What do you mean "batch prediction"? Darknet doesn't support batch prediction. #7541 (comment)

[mkleong96]

def batch_detection(network, images, class_names, class_colors,
thresh=0.25, hier_thresh=.5, nms=.45, batch_size=4):
image_height, image_width, _ = check_batch_shape(images, batch_size)
darknet_images = prepare_batch(images, network)
batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width,
image_height, thresh, hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names, num)
images[idx] = darknet.draw_boxes(predictions, images[idx], class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
return images, batch_predictions

I think this is the function that's written by AlexeyAB in the darknet_images.py file, supposing it can do batch detection right? but when i run the scripts it gets error as below:
batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width,
OSError: exception: access violation reading 0x0000018D331F1040

[mkleong96]

import argparse
import os
import glob
import random
import darknet
import time
import cv2
import numpy as np
import darknet

def parser():
parser = argparse.ArgumentParser(description="YOLO Object Detection")
parser.add_argument("--input", type=str, default="",
help="image source. It can be a single image, a"
"txt with paths to them, or a folder. Image valid"
" formats are jpg, jpeg or png."
"If no input is given, ")
parser.add_argument("--batch_size", default=1, type=int,
help="number of images to be processed at the same time")
parser.add_argument("--weights", default="yolov4.weights",
help="yolo weights path")
parser.add_argument("--dont_show", action='store_true',
help="windown inference display. For headless systems")
parser.add_argument("--ext_output", action='store_true',
help="display bbox coordinates of detected objects")
parser.add_argument("--save_labels", action='store_true',
help="save detections bbox for each image in yolo format")
parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
help="path to config file")
parser.add_argument("--data_file", default="./cfg/coco.data",
help="path to data file")
parser.add_argument("--thresh", type=float, default=.25,
help="remove detections with lower confidence")
return parser.parse_args()

def check_arguments_errors(args):
assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(args.config_file):
raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
if not os.path.exists(args.weights):
raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
if not os.path.exists(args.data_file):
raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
if args.input and not os.path.exists(args.input):
raise(ValueError("Invalid image path {}".format(os.path.abspath(args.input))))

def check_batch_shape(images, batch_size):
"""
Image sizes should be the same width and height
"""
shapes = [image.shape for image in images]
if len(set(shapes)) > 1:
raise ValueError("Images don't have same shape")
if len(shapes) > batch_size:
raise ValueError("Batch size higher than number of images")
return shapes[0]

def load_images(images_path):
"""
If image path is given, return it directly
For txt file, read it and return each line as image path
In other case, it's a folder, return a list with names of each
jpg, jpeg and png file
"""
input_path_extension = images_path.split('.')[-1]
if input_path_extension in ['jpg', 'jpeg', 'png']:
return [images_path]
elif input_path_extension == "txt":
with open(images_path, "r") as f:
return f.read().splitlines()
else:
return glob.glob(
os.path.join(images_path, ".jpg")) +
glob.glob(os.path.join(images_path, ".png")) +
glob.glob(os.path.join(images_path, "*.jpeg"))

def prepare_batch(images, network, channels=3):
width = darknet.network_width(network)
height = darknet.network_height(network)

darknet_images = []
for image in images:
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_resized = cv2.resize(image_rgb, (width, height),
                               interpolation=cv2.INTER_LINEAR)
    custom_image = image_resized.transpose(2, 0, 1)
    darknet_images.append(custom_image)

batch_array = np.concatenate(darknet_images, axis=0)
batch_array = np.ascontiguousarray(batch_array.flat, dtype=np.float32)/255.0
darknet_images = batch_array.ctypes.data_as(darknet.POINTER(darknet.c_float))
return darknet.IMAGE(width, height, channels, darknet_images)

def image_detection(image_path, network, class_names, class_colors, thresh):
# Darknet doesn't accept numpy images.
# Create one with image we reuse for each detect
width = darknet.network_width(network)
height = darknet.network_height(network)
darknet_image = darknet.make_image(width, height, 3)

image = cv2.imread(image_path)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
                           interpolation=cv2.INTER_LINEAR)

darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.detect_image(network, class_names, darknet_image, thresh=thresh)
darknet.free_image(darknet_image)
image = darknet.draw_boxes(detections, image_resized, class_colors)
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB), detections

def batch_detection(network, images, class_names, class_colors,
thresh=0.25, hier_thresh=.5, nms=.45, batch_size=4):
image_height, image_width, _ = check_batch_shape(images, batch_size)
darknet_images = prepare_batch(images, network)
batch_detections = darknet.network_predict_batch(network, darknet_images, batch_size, image_width,
image_height, thresh, hier_thresh, None, 0, 0)
batch_predictions = []
for idx in range(batch_size):
num = batch_detections[idx].num
detections = batch_detections[idx].dets
if nms:
darknet.do_nms_obj(detections, num, len(class_names), nms)
predictions = darknet.remove_negatives(detections, class_names, num)
images[idx] = darknet.draw_boxes(predictions, images[idx], class_colors)
batch_predictions.append(predictions)
darknet.free_batch_detections(batch_detections, batch_size)
return images, batch_predictions

def image_classification(image, network, class_names):
width = darknet.network_width(network)
height = darknet.network_height(network)
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image_resized = cv2.resize(image_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
darknet_image = darknet.make_image(width, height, 3)
darknet.copy_image_from_bytes(darknet_image, image_resized.tobytes())
detections = darknet.predict_image(network, darknet_image)
predictions = [(name, detections[idx]) for idx, name in enumerate(class_names)]
darknet.free_image(darknet_image)
return sorted(predictions, key=lambda x: -x[1])

def convert2relative(image, bbox):
"""
YOLO format use relative coordinates for annotation
"""
x, y, w, h = bbox
height, width, _ = image.shape
return x/width, y/height, w/width, h/height

def save_annotations(name, image, detections, class_names):
"""
Files saved with image_name.txt and relative coordinates
"""
file_name = os.path.splitext(name)[0] + ".txt"
with open(file_name, "w") as f:
for label, confidence, bbox in detections:
x, y, w, h = convert2relative(image, bbox)
label = class_names.index(label)
f.write("{} {:.4f} {:.4f} {:.4f} {:.4f} {:.4f}\n".format(label, x, y, w, h, float(confidence)))

def batch_detection_example():
args = parser()
check_arguments_errors(args)
batch_size = 3
random.seed(3) # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
args.config_file,
args.data_file,
args.weights,
batch_size=batch_size
)
image_names = ['data/horses.jpg', 'data/horses.jpg', 'data/eagle.jpg']
images = [cv2.imread(image) for image in image_names]
images, detections, = batch_detection(network, images, class_names,
class_colors, batch_size=batch_size)
for name, image in zip(image_names, images):
cv2.imwrite(name.replace("data/", ""), image)
print(detections)

def main():
args = parser()
check_arguments_errors(args)

random.seed(3)  # deterministic bbox colors
network, class_names, class_colors = darknet.load_network(
    args.config_file,
    args.data_file,
    args.weights,
    batch_size=args.batch_size
)

images = load_images(args.input)

index = 0
while True:
    # loop asking for new image paths if no list is given
    if args.input:
        if index >= len(images):
            break
        image_name = images[index]
    else:
        image_name = input("Enter Image Path: ")
    prev_time = time.time()
    image, detections = image_detection(
        image_name, network, class_names, class_colors, args.thresh
        )
    if args.save_labels:
        save_annotations(image_name, image, detections, class_names)
    darknet.print_detections(detections, args.ext_output)
    fps = int(1/(time.time() - prev_time))
    print("FPS: {}".format(fps))
    if not args.dont_show:
        cv2.imshow('Inference', image)
        if cv2.waitKey() & 0xFF == ord('q'):
            break
    index += 1

if name == "main":
# unconmment next line for an example of batch processing
# batch_detection_example()
main()

this is the darknet_images.py that is in the darknet-master folder
if i run main(), it has no issue to detect single images, but when it comes to batch detection, it gets OSError

[stephanecharette]

Please see what I wrote in my first comment:

Darknet doesn't support batch prediction. #7541 (comment)

[mkleong96]

Thanks! Seems like there is some issues in there, have u figured out anything that we can edit, in the darknet.py so that we can do the batch inference?

[stephanecharette]

I don't know how else to tell you... It is not supported. See the link I provided twice now.