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import gradio as gr
#
from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation
import matplotlib.pyplot as plt
from matplotlib import gridspec
from PIL import Image
import numpy as np
import tensorflow as tf
import requests
#
feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-cityscapes-640-1280")
urls = ["http://farm3.staticflickr.com/2523/3705549787_79049b1b6d_z.jpg",
"http://farm8.staticflickr.com/7012/6476201279_52db36af64_z.jpg",
"http://farm8.staticflickr.com/7180/6967423255_a3d65d5f6b_z.jpg",
"http://farm4.staticflickr.com/3563/3470840644_3378804bea_z.jpg",
"http://farm9.staticflickr.com/8388/8516454091_0ebdc1130a_z.jpg"]
images = []
for i in urls:
images.append(Image.open(requests.get(i, stream=True).raw))
# inputs = feature_extractor(images=image, return_tensors="pt")
# outputs = model(**inputs)
# logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4)
def my_palette():
return [
[131, 162, 255],
[180, 189, 255],
[255, 227, 187],
[255, 210, 143],
[248, 117, 170],
[255, 223, 223],
[255, 246, 246],
[174, 222, 252],
[150, 194, 145],
[255, 219, 170],
[244, 238, 238],
[50, 38, 83],
[128, 98, 214],
[146, 136, 248],
[255, 210, 215],
[255, 152, 152],
[162, 103, 138],
[63, 29, 56]
]
labels_list = []
with open(r"labels.txt", "r") as fp:
for line in fp:
labels_list.append(line[:-1])
colormap = np.asarray(my_palette())
def greet(input_img):
inputs = feature_extractor(images=input_img, return_tensors="pt")
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
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 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]
iface = gr.Interface(
fn=greet,
inputs=gr.Image(shape=(640, 1280)),
outputs=["plot"],
examples=[images],
allow_flagging="never")
iface.launch(share=True)
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