96 lines
3.0 KiB
Python
96 lines
3.0 KiB
Python
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import os
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import cv2
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import numpy as np
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import torch
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from PIL import Image
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from ..utils import HWC3, resize_image
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from .leres.depthmap import estimateleres
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from .leres.multi_depth_model_woauxi import RelDepthModel
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from .leres.net_tools import strip_prefix_if_present
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class LeresDetector:
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def __init__(self, model):
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self.model = model
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@classmethod
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def from_pretrained(cls, pretrained_model_or_path, filename=None):
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filename = filename or "res101.pth"
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if os.path.isdir(pretrained_model_or_path):
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model_path = os.path.join(pretrained_model_or_path, filename)
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else:
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model_path = pretrained_model_or_path
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checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
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model = RelDepthModel(backbone='resnext101')
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model.load_state_dict(strip_prefix_if_present(checkpoint['depth_model'], "module."), strict=True)
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del checkpoint
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return cls(model)
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def to(self, device):
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self.model.to(device)
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return self
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def __call__(self, input_image, thr_a=0, thr_b=0, detect_resolution=512, image_resolution=512, output_type="np"):
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if not isinstance(input_image, np.ndarray):
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input_image = np.array(input_image, dtype=np.uint8)
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, detect_resolution)
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assert input_image.ndim == 3
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height, width, dim = input_image.shape
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with torch.no_grad():
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depth = estimateleres(input_image, self.model, width, height)
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numbytes=2
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depth_min = depth.min()
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depth_max = depth.max()
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max_val = (2**(8*numbytes))-1
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# check output before normalizing and mapping to 16 bit
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if depth_max - depth_min > np.finfo("float").eps:
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out = max_val * (depth - depth_min) / (depth_max - depth_min)
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else:
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out = np.zeros(depth.shape)
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# single channel, 16 bit image
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depth_image = out.astype("uint16")
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# convert to uint8
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depth_image = cv2.convertScaleAbs(depth_image, alpha=(255.0/65535.0))
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# remove near
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if thr_a != 0:
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thr_a = ((thr_a/100)*255)
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depth_image = cv2.threshold(depth_image, thr_a, 255, cv2.THRESH_TOZERO)[1]
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# invert image
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depth_image = cv2.bitwise_not(depth_image)
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# remove bg
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if thr_b != 0:
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thr_b = ((thr_b/100)*255)
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depth_image = cv2.threshold(depth_image, thr_b, 255, cv2.THRESH_TOZERO)[1]
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detected_map = depth_image
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detected_map = HWC3(detected_map)
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img = resize_image(input_image, image_resolution)
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H, W, C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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return detected_map
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