| import gradio as gr | |
| from matplotlib import gridspec | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from PIL import Image | |
| import tensorflow as tf | |
| from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation | |
| feature_extractor = SegformerFeatureExtractor.from_pretrained( | |
| "mattmdjaga/segformer_b2_clothes" | |
| ) | |
| model = TFSegformerForSemanticSegmentation.from_pretrained( | |
| "mattmdjaga/segformer_b2_clothes" | |
| ) | |
| def ade_palette(): | |
| """ADE20K palette that maps each class to RGB values.""" | |
| return [ | |
| [204, 87, 92], | |
| [112, 185, 212], | |
| [45, 189, 106], | |
| [234, 123, 67], | |
| [78, 56, 123], | |
| [210, 32, 89], | |
| [90, 180, 56], | |
| [155, 102, 200], | |
| [33, 147, 176], | |
| [255, 183, 76], | |
| [67, 123, 89], | |
| [190, 60, 45], | |
| [134, 112, 200], | |
| [56, 45, 189], | |
| [200, 56, 123], | |
| [87, 92, 204], | |
| [120, 56, 123], | |
| [45, 78, 123] | |
| ] | |
| labels_list = [] | |
| with open(r'labels.txt', 'r') as fp: | |
| for line in fp: | |
| labels_list.append(line[:-1]) | |
| colormap = np.asarray(ade_palette()) | |
| def label_to_color_image(label): | |
| if label.ndim != 2: | |
| raise ValueError("Expect 2-D input label") | |
| if np.max(label) >= len(colormap): | |
| raise ValueError("label value too large.") | |
| return colormap[label] | |
| def draw_plot(pred_img, seg): | |
| fig = plt.figure(figsize=(20, 15)) | |
| grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1]) | |
| plt.subplot(grid_spec[0]) | |
| plt.imshow(pred_img) | |
| plt.axis('off') | |
| LABEL_NAMES = np.asarray(labels_list) | |
| FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1) | |
| FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP) | |
| unique_labels = np.unique(seg.numpy().astype("uint8")) | |
| ax = plt.subplot(grid_spec[1]) | |
| plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest") | |
| ax.yaxis.tick_right() | |
| plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels]) | |
| plt.xticks([], []) | |
| ax.tick_params(width=0.0, labelsize=25) | |
| return fig | |
| def sepia(input_img): | |
| input_img = Image.fromarray(input_img) | |
| inputs = feature_extractor(images=input_img, return_tensors="tf") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| logits = tf.transpose(logits, [0, 2, 3, 1]) | |
| logits = tf.image.resize( | |
| logits, input_img.size[::-1] | |
| ) # We reverse the shape of `image` because `image.size` returns width and height. | |
| seg = tf.math.argmax(logits, axis=-1)[0] | |
| color_seg = np.zeros( | |
| (seg.shape[0], seg.shape[1], 3), dtype=np.uint8 | |
| ) # height, width, 3 | |
| for label, color in enumerate(colormap): | |
| color_seg[seg.numpy() == label, :] = color | |
| # Show image + mask | |
| pred_img = np.array(input_img) * 0.5 + color_seg * 0.5 | |
| pred_img = pred_img.astype(np.uint8) | |
| fig = draw_plot(pred_img, seg) | |
| return fig | |
| # demo = gr.Interface(fn=sepia, | |
| # inputs=gr.Image(shape=(400, 600)), | |
| # outputs=['plot'], | |
| # examples=["person-1", "person-2", "person-3", "person-4", "person-5"], | |
| # allow_flagging='never') | |
| demo = gr.Interface(fn=sepia, | |
| inputs=gr.Image(), # Remove the 'shape' argument here | |
| outputs=['plot'], | |
| examples=[ | |
| "person-1.jpg", | |
| "person-2.jpg", | |
| "person-3.jpg", | |
| "person-4.jpg", | |
| "person-5.jpg" | |
| ], | |
| allow_flagging='never') | |
| demo.launch() | |
| # | |
| # import gradio as gr | |
| # from matplotlib import gridspec | |
| # import matplotlib.pyplot as plt | |
| # import numpy as np | |
| # from PIL import Image | |
| # import tensorflow as tf | |
| # from transformers import SegformerImageProcessor, TFSegformerForSemanticSegmentation | |
| # | |
| # # SegformerImageProcessor ๋ฐ ๋ชจ๋ธ์ ๋ก๋ํฉ๋๋ค. | |
| # processor = SegformerImageProcessor.from_pretrained("mattmdjaga/segformer_b2_clothes") | |
| # model = TFSegformerForSemanticSegmentation.from_pretrained("mattmdjaga/segformer_b2_clothes") | |
| # | |
| # def ade_palette(): | |
| # """ADE20K palette that maps each class to RGB values.""" | |
| # return [ | |
| # [204, 87, 92], | |
| # [112, 185, 212], | |
| # [45, 189, 106], | |
| # [234, 123, 67], | |
| # [78, 56, 123], | |
| # [210, 32, 89], | |
| # [90, 180, 56], | |
| # [155, 102, 200], | |
| # [33, 147, 176], | |
| # [255, 183, 76], | |
| # [67, 123, 89], | |
| # [190, 60, 45], | |
| # [134, 112, 200], | |
| # [56, 45, 189], | |
| # [200, 56, 123], | |
| # [87, 92, 204], | |
| # [120, 56, 123], | |
| # [45, 78, 123] | |
| # ] | |
| # | |
| # labels_list = [] | |
| # | |
| # with open(r'labels.txt', 'r') as fp: | |
| # for line in fp: | |
| # labels_list.append(line[:-1]) | |
| # | |
| # colormap = np.asarray(ade_palette()) | |
| # | |
| # def label_to_color_image(label): | |
| # if label.ndim != 2: | |
| # raise ValueError("Expect 2-D input label") | |
| # | |
| # if np.max(label) >= len(colormap): | |
| # raise ValueError("label value too large.") | |
| # return colormap[label] | |
| # | |
| # def draw_plot(pred_img, seg): | |
| # fig = plt.figure(figsize=(20, 15)) | |
| # | |
| # grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1]) | |
| # | |
| # plt.subplot(grid_spec[0]) | |
| # plt.imshow(pred_img) | |
| # plt.axis('off') | |
| # LABEL_NAMES = np.asarray(labels_list) | |
| # FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1) | |
| # FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP) | |
| # | |
| # unique_labels = np.unique(seg.numpy().astype("uint8")) | |
| # ax = plt.subplot(grid_spec[1]) | |
| # plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest") | |
| # ax.yaxis.tick_right() | |
| # plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels]) | |
| # plt.xticks([], []) | |
| # ax.tick_params(width=0.0, labelsize=25) | |
| # return fig | |
| # | |
| # def sepia(input_img): | |
| # input_img = Image.fromarray(input_img) | |
| # | |
| # inputs = processor(images=input_img, return_tensors="tf") | |
| # outputs = model(**inputs) | |
| # logits = outputs.logits | |
| # | |
| # logits = tf.transpose(logits, [0, 2, 3, 1]) | |
| # logits = tf.image.resize( | |
| # logits, input_img.size[::-1] | |
| # ) # We reverse the shape of `image` because `image.size` returns width and height. | |
| # seg = tf.math.argmax(logits, axis=-1)[0] | |
| # | |
| # color_seg = np.zeros( | |
| # (seg.shape[0], seg.shape[1], 3), dtype=np.uint8 | |
| # ) # height, width, 3 | |
| # for label, color in enumerate(colormap): | |
| # color_seg[seg.numpy() == label, :] = color | |
| # | |
| # # Show image + mask | |
| # pred_img = np.array(input_img) * 0.5 + color_seg * 0.5 | |
| # pred_img = pred_img.astype(np.uint8) | |
| # | |
| # fig = draw_plot(pred_img, seg) | |
| # return fig | |
| # | |
| # demo = gr.Interface(fn=sepia, | |
| # inputs=gr.inputs.Image(shape=(400, 600)), | |
| # outputs='plot', | |
| # examples=["person-1", "person-2", "person-3", "person-4", "person-5"], | |
| # allow_flagging='never') | |
| # | |
| # demo.launch() | |