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https://github.com/invoke-ai/InvokeAI
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cleanup: remove unused code from the DWPose implementation
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@ -9,64 +9,6 @@ import numpy as np
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eps = 0.01
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eps = 0.01
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def smart_resize(x, s):
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Ht, Wt = s
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if x.ndim == 2:
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Ho, Wo = x.shape
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Co = 1
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else:
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Ho, Wo, Co = x.shape
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if Co == 3 or Co == 1:
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k = float(Ht + Wt) / float(Ho + Wo)
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return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
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else:
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return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
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def smart_resize_k(x, fx, fy):
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if x.ndim == 2:
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Ho, Wo = x.shape
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Co = 1
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else:
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Ho, Wo, Co = x.shape
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Ht, Wt = Ho * fy, Wo * fx
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if Co == 3 or Co == 1:
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k = float(Ht + Wt) / float(Ho + Wo)
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return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
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else:
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return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
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def padRightDownCorner(img, stride, padValue):
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h = img.shape[0]
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w = img.shape[1]
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pad = 4 * [None]
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pad[0] = 0 # up
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pad[1] = 0 # left
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pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
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pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
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img_padded = img
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pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
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img_padded = np.concatenate((pad_up, img_padded), axis=0)
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pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
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img_padded = np.concatenate((pad_left, img_padded), axis=1)
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pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
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img_padded = np.concatenate((img_padded, pad_down), axis=0)
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pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
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img_padded = np.concatenate((img_padded, pad_right), axis=1)
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return img_padded, pad
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def transfer(model, model_weights):
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transfered_model_weights = {}
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for weights_name in model.state_dict().keys():
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transfered_model_weights[weights_name] = model_weights[".".join(weights_name.split(".")[1:])]
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return transfered_model_weights
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def draw_bodypose(canvas, candidate, subset):
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def draw_bodypose(canvas, candidate, subset):
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H, W, C = canvas.shape
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H, W, C = canvas.shape
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candidate = np.array(candidate)
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candidate = np.array(candidate)
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