| import gradio as gr |
|
|
| import sys |
| import csv |
| import numpy as np |
| import cv2 |
| from matplotlib import gridspec |
| import matplotlib.pyplot as plt |
| import onnxruntime as ort |
|
|
| import wget |
|
|
| csv.field_size_limit(sys.maxsize) |
|
|
| def ade_palette(): |
| """ADE20K palette that maps each class to RGB values.""" |
| return [ |
| [120, 120, 120], |
| [180, 120, 120], |
| [6, 230, 230], |
| [80, 50, 50], |
| [4, 200, 3], |
| [120, 120, 80], |
| [140, 140, 140], |
| [204, 5, 255], |
| [230, 230, 230], |
| [4, 250, 7], |
| [224, 5, 255], |
| [235, 255, 7], |
| [150, 5, 61], |
| [120, 120, 70], |
| [8, 255, 51], |
| [255, 6, 82], |
| [143, 255, 140], |
| [204, 255, 4], |
| [255, 51, 7], |
| [204, 70, 3], |
| [0, 102, 200], |
| [61, 230, 250], |
| [255, 6, 51], |
| [11, 102, 255], |
| [255, 7, 71], |
| [255, 9, 224], |
| [9, 7, 230], |
| [220, 220, 220], |
| [255, 9, 92], |
| [112, 9, 255], |
| [8, 255, 214], |
| [7, 255, 224], |
| [255, 184, 6], |
| [10, 255, 71], |
| [255, 41, 10], |
| [7, 255, 255], |
| [224, 255, 8], |
| [102, 8, 255], |
| [255, 61, 6], |
| [255, 194, 7], |
| [255, 122, 8], |
| [0, 255, 20], |
| [255, 8, 41], |
| [255, 5, 153], |
| [6, 51, 255], |
| [235, 12, 255], |
| [160, 150, 20], |
| [0, 163, 255], |
| [140, 140, 140], |
| [250, 10, 15], |
| [20, 255, 0], |
| [31, 255, 0], |
| [255, 31, 0], |
| [255, 224, 0], |
| [153, 255, 0], |
| [0, 0, 255], |
| [255, 71, 0], |
| [0, 235, 255], |
| [0, 173, 255], |
| [31, 0, 255], |
| [11, 200, 200], |
| [255, 82, 0], |
| [0, 255, 245], |
| [0, 61, 255], |
| [0, 255, 112], |
| [0, 255, 133], |
| [255, 0, 0], |
| [255, 163, 0], |
| [255, 102, 0], |
| [194, 255, 0], |
| [0, 143, 255], |
| [51, 255, 0], |
| [0, 82, 255], |
| [0, 255, 41], |
| [0, 255, 173], |
| [10, 0, 255], |
| [173, 255, 0], |
| [0, 255, 153], |
| [255, 92, 0], |
| [255, 0, 255], |
| [255, 0, 245], |
| [255, 0, 102], |
| [255, 173, 0], |
| [255, 0, 20], |
| [255, 184, 184], |
| [0, 31, 255], |
| [0, 255, 61], |
| [0, 71, 255], |
| [255, 0, 204], |
| [0, 255, 194], |
| [0, 255, 82], |
| [0, 10, 255], |
| [0, 112, 255], |
| [51, 0, 255], |
| [0, 194, 255], |
| [0, 122, 255], |
| [0, 255, 163], |
| [255, 153, 0], |
| [0, 255, 10], |
| [255, 112, 0], |
| [143, 255, 0], |
| [82, 0, 255], |
| [163, 255, 0], |
| [255, 235, 0], |
| [8, 184, 170], |
| [133, 0, 255], |
| [0, 255, 92], |
| [184, 0, 255], |
| [255, 0, 31], |
| [0, 184, 255], |
| [0, 214, 255], |
| [255, 0, 112], |
| [92, 255, 0], |
| [0, 224, 255], |
| [112, 224, 255], |
| [70, 184, 160], |
| [163, 0, 255], |
| [153, 0, 255], |
| [71, 255, 0], |
| [255, 0, 163], |
| [255, 204, 0], |
| [255, 0, 143], |
| [0, 255, 235], |
| [133, 255, 0], |
| [255, 0, 235], |
| [245, 0, 255], |
| [255, 0, 122], |
| [255, 245, 0], |
| [10, 190, 212], |
| [214, 255, 0], |
| [0, 204, 255], |
| [20, 0, 255], |
| [255, 255, 0], |
| [0, 153, 255], |
| [0, 41, 255], |
| [0, 255, 204], |
| [41, 0, 255], |
| [41, 255, 0], |
| [173, 0, 255], |
| [0, 245, 255], |
| [71, 0, 255], |
| [122, 0, 255], |
| [0, 255, 184], |
| [0, 92, 255], |
| [184, 255, 0], |
| [0, 133, 255], |
| [255, 214, 0], |
| [25, 194, 194], |
| [102, 255, 0], |
| [92, 0, 255], |
| ] |
|
|
| url='https://github.com/deep-diver/segformer-tf-transformers/releases/download/1.0/segformer-b5-finetuned-ade-640-640.onnx' |
| labels_list = [] |
| colormap = np.asarray(ade_palette()) |
|
|
| model_path = wget.download(url) |
| sess = ort.InferenceSession(model_path) |
|
|
| with open(r'labels.txt', 'r') as fp: |
| for line in fp: |
| labels_list.append(line[:-1]) |
|
|
| 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) |
| 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): |
| img = cv2.imread(input_img).astype(np.float32) |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| img_batch = np.expand_dims(img, axis=0) |
| img_batch = np.transpose(img_batch, (0, 3, 1, 2)) |
| |
| logits = sess.run(None, {"pixel_values": img_batch})[0] |
|
|
| logits = np.transpose(logits, (0, 2, 3, 1)) |
| seg = np.argmax(logits, axis=-1)[0].astype('float32') |
| seg = cv2.resize(seg, (640, 640)).astype('uint8') |
| |
| color_seg = np.zeros( |
| (seg.shape[0], seg.shape[1], 3), dtype=np.uint8 |
| ) |
|
|
| for label, color in enumerate(colormap): |
| color_seg[seg == label, :] = color |
|
|
| |
| color_seg = color_seg[..., ::-1] |
|
|
| |
| pred_img = 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(sepia, |
| gr.inputs.Image(type="filepath", shape=(640, 640)), |
| outputs=['plot'], |
| examples=["ADE_val_00000001.jpeg"], |
| allow_flagging='never') |
|
|
| demo.launch() |
