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yolov7.py
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import math
import os
import sys
import time
import ailia
import cv2
import numpy as np
from yolov7_utils import postprocess, predictions_to_object, check_img_size, letterbox
# import original modules
sys.path.append('../../util')
# logger
from logging import getLogger
import webcamera_utils
from detector_utils import (load_image, plot_results, reverse_letterbox,
write_predictions)
from image_utils import imread # noqa: E402
from model_utils import check_and_download_models
from utils import get_base_parser, get_savepath, update_parser
logger = getLogger(__name__)
# os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
# ======================
# Parameters
# ======================
MODEL_PARAMS = {'yolov7': {'input_shape': [640, 640], 'max_stride': 32, 'anchors':[
[12,16, 19,36, 40,28], [36,75, 76,55, 72,146], [142,110, 192,243, 459,401]
]},
'yolov7x': {'input_shape': [640, 640], 'max_stride': 32, 'anchors':[
[12,16, 19,36, 40,28], [36,75, 76,55, 72,146], [142,110, 192,243, 459,401]
]},
'yolov7_w6': {'input_shape': [1280, 1280], 'max_stride': 64, 'anchors':[
[19,27, 44,40, 38,94 ], [96,68, 86,152, 180,137 ], [140,301, 303,264, 238,542], [436,615, 739,380, 925,792]
]},
'yolov7_e6': {'input_shape': [1280, 1280], 'max_stride': 64, 'anchors':[
[19,27, 44,40, 38,94 ], [96,68, 86,152, 180,137 ], [140,301, 303,264, 238,542], [436,615, 739,380, 925,792]
]},
'yolov7_d6': {'input_shape': [1280, 1280], 'max_stride': 64, 'anchors':[
[19,27, 44,40, 38,94 ], [96,68, 86,152, 180,137 ], [140,301, 303,264, 238,542], [436,615, 739,380, 925,792]
]},
'yolov7_e6e': {'input_shape': [1280, 1280], 'max_stride': 64, 'anchors':[
[19,27, 44,40, 38,94 ], [96,68, 86,152, 180,137 ], [140,301, 303,264, 238,542], [436,615, 739,380, 925,792]
]},
'yolov7_tiny': {'input_shape': [512, 512], 'max_stride': 32, 'anchors':[
[12,16, 19,36, 40,28], [36,75, 76,55, 72,146], [142,110, 192,243, 459,401]
]}
}
REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/yolov7/'
IMAGE_PATH = 'input.jpg'
SAVE_IMAGE_PATH = 'output.jpg'
COCO_CATEGORY = [
"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train",
"truck", "boat", "traffic light", "fire hydrant", "stop sign",
"parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
"handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard",
"surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork",
"knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange",
"broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair",
"couch", "potted plant", "bed", "dining table", "toilet", "tv",
"laptop", "mouse", "remote", "keyboard", "cell phone", "microwave",
"oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush"
]
SCORE_THR = 0.25#0.4
NMS_THR = 0.45
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser('yolox model', IMAGE_PATH, SAVE_IMAGE_PATH)
parser.add_argument(
'-m', '--model_name',
default='yolov7',
help='[yolov7, yolov7x, yolov7_w6, yolov7_e6, yolov7_d6,'
'yolov7_e6e, yolov7_tiny]'
)
parser.add_argument(
'-w', '--write_prediction',
action='store_true',
help='Flag to output the prediction file.'
)
parser.add_argument(
'-th', '--threshold',
default=SCORE_THR, type=float,
help='The detection threshold for yolo. (default: '+str(SCORE_THR)+')'
)
parser.add_argument(
'-iou', '--iou',
default=NMS_THR, type=float,
help='The detection iou for yolo. (default: '+str(NMS_THR)+')'
)
parser.add_argument(
'-dw', '--detection_width',
default=-1, type=int,
help='The detection width and height for yolo. (default: 640)'
)
parser.add_argument(
'-dh', '--detection_height',
default=-1, type=int,
help='The detection height and height for yolo. (default: 640)'
)
parser.add_argument(
'--classes', nargs='+',
type=int, help='filter by class: --class 0, or --class 0 2 3'
)
parser.add_argument(
'--agnostic-nms',
action='store_true', help='class-agnostic NMS'
)
args = update_parser(parser)
MODEL_NAME = args.model_name
WEIGHT_PATH = MODEL_NAME + ".onnx"
MODEL_PATH = MODEL_NAME + ".onnx.prototxt"
HEIGHT = MODEL_PARAMS[MODEL_NAME]['input_shape'][0]
WIDTH = MODEL_PARAMS[MODEL_NAME]['input_shape'][1]
STRIDE = MODEL_PARAMS[MODEL_NAME]['max_stride']
ANCHORS = MODEL_PARAMS[MODEL_NAME]['anchors']
# ======================
# Main functions
# ======================
def recognize_from_image(detector):
# input image loop
for image_path in args.input:
# prepare input data
logger.debug(f'input image: {image_path}')
raw_img = cv2.imread(image_path) # BGR
# Padded resize
img = letterbox(raw_img, (HEIGHT, WIDTH), stride=STRIDE)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img)
img = img/255.0 # 0 - 255 to 0.0 - 1.0
logger.debug(f'input image shape: {raw_img.shape}')
# inference
logger.info('Start inference...')
if args.benchmark:
logger.info('BENCHMARK mode')
total_time = 0
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
output = detector.run(img[None, :, :, :])
end = int(round(time.time() * 1000))
if i != 0:
total_time = total_time + (end - start)
logger.info(f'\tailia processing time {end - start} ms')
logger.info(f'\taverage time {total_time / (args.benchmark_count-1)} ms')
else:
output = detector.run(img[None, :, :, :])
predictions = postprocess(output, ANCHORS, p6=STRIDE==64)[0]
detect_object = predictions_to_object(predictions, raw_img, args.iou, args.threshold, img.shape)
res_img = plot_results(detect_object, raw_img, COCO_CATEGORY)
# plot result
savepath = get_savepath(args.savepath, image_path)
logger.info(f'saved at : {savepath}')
cv2.imwrite(savepath, res_img)
# write prediction
if args.write_prediction:
pred_file = '%s.txt' % savepath.rsplit('.', 1)[0]
write_predictions(pred_file, detect_object, raw_img, COCO_CATEGORY)
logger.info('Script finished successfully.')
def recognize_from_video(detector):
capture = webcamera_utils.get_capture(args.video)
# create video writer if savepath is specified as video format
if args.savepath != SAVE_IMAGE_PATH:
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
save_h, save_w = f_h, f_w
writer = webcamera_utils.get_writer(args.savepath, save_h, save_w)
else:
writer = None
if args.write_prediction:
frame_count = 0
frame_digit = int(math.log10(capture.get(cv2.CAP_PROP_FRAME_COUNT)) + 1)
video_name = os.path.splitext(os.path.basename(args.video))[0]
frame_shown = False
while (True):
ret, frame = capture.read()
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0:
break
raw_img = frame
# Padded resize
img = letterbox(raw_img, (HEIGHT, WIDTH), stride=STRIDE)[0]
# Convert
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img)
img = img/255.0 # 0 - 255 to 0.0 - 1.0
output = detector.run(img[None, :, :, :])
predictions = postprocess(output, ANCHORS, p6=STRIDE==64)[0]
detect_object = predictions_to_object(predictions, raw_img, args.iou, args.threshold, img.shape)
res_img = plot_results(detect_object, raw_img, COCO_CATEGORY)
cv2.imshow('frame', res_img)
frame_shown = True
# save results
if writer is not None:
writer.write(res_img)
# write prediction
if args.write_prediction:
savepath = get_savepath(args.savepath, video_name, post_fix = '_%s' % (str(frame_count).zfill(frame_digit) + '_res'), ext='.png')
pred_file = '%s.txt' % savepath.rsplit('.', 1)[0]
write_predictions(pred_file, detect_object, frame, COCO_CATEGORY)
frame_count += 1
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def main():
# model files check and download
check_and_download_models(WEIGHT_PATH, MODEL_PATH, REMOTE_PATH)
env_id = args.env_id
detector = ailia.Net(MODEL_PATH, WEIGHT_PATH, env_id=env_id)
if args.detection_width!=-1 or args.detection_height!=-1:
global WIDTH,HEIGHT
imgsz = max(args.detection_width, args.detection_height)
imgsz = check_img_size(imgsz, s=STRIDE) # check img_size
WIDTH=imgsz
HEIGHT=imgsz
detector.set_input_shape((1,3,WIDTH,HEIGHT))
if args.video is not None:
# video mode
recognize_from_video(detector)
else:
# image mode
recognize_from_image(detector)
if __name__ == '__main__':
main()