|
|
|
|
|
|
|
|
|
|
|
|
|
|
import wget |
|
|
import zipfile |
|
|
import json |
|
|
import glob |
|
|
import os |
|
|
import tensorflow as tf |
|
|
import numpy as np |
|
|
from tqdm import tqdm |
|
|
from pycocotools.cocoeval import COCOeval |
|
|
from pycocotools.coco import COCO |
|
|
from example import load_image |
|
|
from example import decode_output, run_inference, gen_box_colors |
|
|
|
|
|
OBJECT_DETECTOR_TFLITE = "yolov4-tiny_416_quant.tflite" |
|
|
SCORE_THRESHOLD = 0.0 |
|
|
NMS_IOU_THRESHOLD = 0.5 |
|
|
INFERENCE_IMG_SIZE = 416 |
|
|
|
|
|
LABEL_MAP = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, |
|
|
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, |
|
|
40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, |
|
|
57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, |
|
|
77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] |
|
|
|
|
|
COCO_WEBSITE = "http://images.cocodataset.org" |
|
|
VAL_IMG_URL = COCO_WEBSITE + "/zips/val2017.zip" |
|
|
VAL_ANNO_URL = COCO_WEBSITE + "/annotations/annotations_trainval2017.zip" |
|
|
|
|
|
BOX_COLORS = gen_box_colors() |
|
|
|
|
|
print("Downloading COCO validation dataset...") |
|
|
response = wget.download(VAL_IMG_URL, "val2017.zip") |
|
|
response = wget.download(VAL_ANNO_URL, "annotations_trainval2017.zip") |
|
|
|
|
|
with zipfile.ZipFile("val2017.zip", 'r') as zip_ref: |
|
|
zip_ref.extractall("coco") |
|
|
|
|
|
with zipfile.ZipFile("annotations_trainval2017.zip", 'r') as zip_ref: |
|
|
zip_ref.extractall("coco") |
|
|
|
|
|
|
|
|
interpreter = tf.lite.Interpreter(OBJECT_DETECTOR_TFLITE) |
|
|
interpreter.allocate_tensors() |
|
|
|
|
|
annotations = COCO(annotation_file="coco/annotations/instances_val2017.json") |
|
|
|
|
|
|
|
|
def evaluate(interpreter): |
|
|
image_filenames = glob.glob("coco/val2017/*") |
|
|
|
|
|
results = [] |
|
|
|
|
|
for image_fn in tqdm(image_filenames, desc="Evaluating"): |
|
|
|
|
|
image_id = int(os.path.splitext(os.path.basename(image_fn))[0]) |
|
|
|
|
|
orig_image, img = load_image(image_fn) |
|
|
yolo_output = run_inference(interpreter, img) |
|
|
scores, boxes, classes = decode_output(yolo_output, |
|
|
SCORE_THRESHOLD, |
|
|
NMS_IOU_THRESHOLD) |
|
|
|
|
|
shp = orig_image.shape |
|
|
boxes = boxes.numpy() |
|
|
boxes /= INFERENCE_IMG_SIZE |
|
|
boxes *= np.array([shp[1], shp[0], shp[1], shp[0]]) |
|
|
|
|
|
boxes = boxes.astype(np.int32) |
|
|
|
|
|
boxes[..., 2] = boxes[..., 2] - boxes[..., 0] |
|
|
boxes[..., 3] = boxes[..., 3] - boxes[..., 1] |
|
|
|
|
|
for score, box, clas in zip(scores.numpy(), boxes, classes.numpy()): |
|
|
results.append({"image_id": image_id, |
|
|
"category_id": int(LABEL_MAP[clas]), |
|
|
"bbox": [float(x) for x in list(box)], |
|
|
"score": float(score)}) |
|
|
|
|
|
return results |
|
|
|
|
|
|
|
|
predictions = evaluate(interpreter) |
|
|
|
|
|
with open("predictions.json", "w") as f: |
|
|
json.dump(predictions, f, indent=4) |
|
|
|
|
|
predictions = annotations.loadRes("predictions.json") |
|
|
|
|
|
cocoeval = COCOeval(annotations, predictions, "bbox") |
|
|
|
|
|
cocoeval.evaluate() |
|
|
cocoeval.accumulate() |
|
|
cocoeval.summarize() |
|
|
|
