Using SAM2 detects few trees

Hi everyone

I recently started evaluating SAM2 for tree detection.
I´d like to clarify that I am new to this whole topic and I´m trying to learn how to use SAM2 to detect trees.

I have tried the following code:
import os
# if using Apple MPS, fall back to CPU for unsupported ops
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
# select the device for computation
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
print(f"using device: {device}")

if device.type == "cuda":
# use bfloat16 for the entire notebook
torch.autocast("cuda", dtype=torch.bfloat16).__enter__()
# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
if torch.cuda.get_device_properties(0).major >= 8:
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
elif device.type == "mps":
print(
"\nSupport for MPS devices is preliminary. SAM 2 is trained with CUDA and might "
"give numerically different outputs and sometimes degraded performance on MPS. "
"See e.g. https://github.com/pytorch/pytorch/issues/84936 for a discussion."
)
np.random.seed(3)

def show_anns(anns, borders=True):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
ax = plt.gca()
ax.set_autoscale_on(False)

img = np.ones((sorted_anns[0]['segmentation'].shape[0], sorted_anns[0]['segmentation'].shape[1], 4))
img[:, :, 3] = 0
for ann in sorted_anns:
m = ann['segmentation']
color_mask = np.concatenate([np.random.random(3), [0.5]])
img[m] = color_mask
if borders:
import cv2
contours, _ = cv2.findContours(m.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Try to smooth contours
contours = [cv2.approxPolyDP(contour, epsilon=0.01, closed=True) for contour in contours]
cv2.drawContours(img, contours, -1, (0, 0, 1, 0.4), thickness=1)

ax.imshow(img)
image = Image.open(r"C:\Users\Lenovo\Desktop\Daniel\AIconteo\cerro2_corte.tif")
image = np.array(image.convert("RGB"))
plt.figure(figsize=(20, 20))
plt.imshow(image)
plt.axis('off')
plt.show()
from sam2.build_sam import build_sam2
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator

sam2_checkpoint = r"C:\Users\Lenovo\segment-anything-2\checkpoints\sam2_hiera_base_plus.pt"
model_cfg = "sam2_hiera_b+.yaml"

sam2 = build_sam2(model_cfg, sam2_checkpoint, device=device, apply_postprocessing=False)

mask_generator = SAM2AutomaticMaskGenerator(sam2)
masks = mask_generator.generate(image)
print(len(masks))
print(masks[0].keys())
121
dict_keys(['segmentation', 'area', 'bbox', 'predicted_iou', 'point_coords', 'stability_score', 'crop_box'])
plt.figure(figsize=(20, 20))
plt.imshow(image)
show_anns(masks)
plt.axis('off')
plt.show()

I add some examples of images. I am already very grateful if can anyone help me? :)