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import numpy as np
import PIL
import torch
############################################
# Predict detections with MegaDetector v5a model
def predict_md(
im,
megadetector_model, # Megadet_Models[mega_model_input]
size=640,
):
# resize image
g = size / max(im.size) # multipl factor to make max size of the image equal to input size
im = im.resize((int(x * g) for x in im.size), PIL.Image.Resampling.LANCZOS) # resize
# device: yolov5's select_device expects a CUDA index ('0') or 'cpu', not 'cuda'
md_device = "0" if torch.cuda.is_available() else "cpu"
# megadetector
MD_model = torch.hub.load(
"ultralytics/yolov5", # repo_or_dir
"custom", # model
megadetector_model, # args for callable model
skip_validation=True, # avoid GitHub API rate limit (403)
device=md_device,
trust_repo=True,
)
## detect objects
# vars(results).keys(): imgs, pred, names, files, times, xyxy, xywh, xyxyn, xywhn, n, t, s
results = MD_model(im)
return results
##########################################
def crop_animal_detections(img_in, yolo_results, likelihood_th):
## Extract animal crops
list_labels_as_str = [i for i in yolo_results.names.values()] # ['animal', 'person', 'vehicle']
list_np_animal_crops = []
list_animal_bboxes = [] # detection rows [x1,y1,x2,y2,conf,label] matching each crop
# image to crop (scale as input for megadetector)
img_in = img_in.resize((yolo_results.ims[0].shape[1], yolo_results.ims[0].shape[0]))
# for every detection in the img
for det_array in yolo_results.xyxy:
# for every detection
for j in range(det_array.shape[0]):
# compute coords around bbox rounded to the nearest integer (for pasting later)
xmin_rd = int(math.floor(det_array[j, 0])) # int() should suffice?
ymin_rd = int(math.floor(det_array[j, 1]))
xmax_rd = int(math.ceil(det_array[j, 2]))
ymax_rd = int(math.ceil(det_array[j, 3]))
pred_llk = det_array[j, 4]
pred_label = det_array[j, 5]
# keep animal crops above threshold
if (pred_label == list_labels_as_str.index("animal")) and (pred_llk >= likelihood_th):
area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)
# pdb.set_trace()
crop = img_in.crop(area) # Image.fromarray(img_in).crop(area)
crop_np = np.asarray(crop)
# add to list
list_np_animal_crops.append(crop_np)
list_animal_bboxes.append(det_array[j, :].tolist())
return list_np_animal_crops, list_animal_bboxes
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