app.py

import segmentation_models as sm
import numpy as np
import os
import cv2
import keras
import matplotlib.colors as colorsHTML
from PIL import Image
import gradio as gr

import os
os.system('wget https://huggingface.co./Armandoliv/cars-parts-segmentation-unet-resnet18/resolve/main/best_model.h5')
os.system('pip -qq install pycocotools @ git+https://github.com/philferriere/cocoapi.git@2929bd2ef6b451054755dfd7ceb09278f935f7ad#subdirectory=PythonAPI')

c= ['_background_', 'back_bumper', 'back_glass', 'back_left_door','back_left_light',
 'back_right_door', 'back_right_light', 'front_bumper','front_glass',  
 'front_left_door',  'front_left_light', 'front_right_door',  'front_right_light',  'hood',  'left_mirror',
 'right_mirror', 'tailgate',  'trunk', 'wheel']

colors = [ 	(245,255,250), (75,0,130),  (0,255,0), (32,178,170),(0,0,255), (0,255,255), (255,0,255), (128,0,128), (255,140,0),
          (85,107,47), (102,205,170), (0,191,255), (255,0,0), 	(255,228,196), (205,133,63),
          (220,20,60), (255,69,0), 	(143,188,143), (255,255,0)]


sm.set_framework('tf.keras')

sm.framework()

BACKBONE = 'resnet18'
n_classes = 19
activation =  'softmax'

#create model
model = sm.Unet(BACKBONE, classes=n_classes, activation=activation)

# load best weights
model.load_weights('best_model.h5') 

def get_colored_segmentation_image(seg_arr, n_classes, colors=colors):
    output_height = seg_arr.shape[0]
    output_width = seg_arr.shape[1]

    seg_img = np.zeros((output_height, output_width, 3))

    for c in range(n_classes):
        seg_arr_c = seg_arr[:, :] == c
          # print(sum(sum(seg_arr_c)), colors[c] )
        seg_img[:, :, 0] += ((seg_arr_c)*(colors[c][0])).astype('uint8')
        seg_img[:, :, 1] += ((seg_arr_c)*(colors[c][1])).astype('uint8')
        seg_img[:, :, 2] += ((seg_arr_c)*(colors[c][2])).astype('uint8')

    return seg_img/255

def get_legends(class_names, colors, tags):

    n_classes = len(class_names)
    legend = np.zeros(((len(class_names) * 25) + 25, 125, 3),
                      dtype="uint8") + 255

    class_names_colors = enumerate(zip(class_names[:n_classes],
                                       colors[:n_classes]))
    j = 0
    for (i, (class_name, color)) in class_names_colors:
        if i in tags:
          color = [int(c) for c in color]
          cv2.putText(legend, class_name, (5, (j * 25) + 17),
                      cv2.FONT_HERSHEY_COMPLEX, 0.35, (0, 0, 0), 1)
          cv2.rectangle(legend, (100, (j* 25)), (125, (j * 25) + 25),
                        tuple(color), -1)
          j +=1
    return legend



def preprocess_image(path_img):
  img = Image.open(path_img)
  ww = 512
  hh = 512
  img.thumbnail((hh, ww))
  i = np.array(img)
  ht, wd, cc= i.shape

  # create new image of desired size and color (blue) for padding
  color = (0,0,0)
  result = np.full((hh,ww,cc), color, dtype=np.uint8)

  # copy img image into center of result image
  result[:ht, :wd] = img
  return result, ht, wd

def concat_lengends(seg_img, legend_img):

  new_h = np.maximum(seg_img.shape[0], legend_img.shape[0])
  new_w = seg_img.shape[1] + legend_img.shape[1]

  out_img = np.zeros((new_h, new_w, 3)).astype('uint8') + legend_img[0, 0, 0]

  out_img[:legend_img.shape[0], :  legend_img.shape[1]] = np.copy(legend_img)
  out_img[:seg_img.shape[0], legend_img.shape[1]:] = np.copy(seg_img)

  return out_img

def main_convert(filename):

  print(filename)
  #load the image
  img_path = filename
  img = Image.open(img_path).convert("RGB")
  tags = []

  #preprocess the image
  img_scaled_arr = preprocess_image(img_path)
  image = np.expand_dims(img_scaled_arr[0], axis=0)

  #make the predictions
  pr_mask = model.predict(image).squeeze()
  pr_mask_int = np.zeros((pr_mask.shape[0],pr_mask.shape[1]))

  #filter the smallest noisy segments
  kernel = np.ones((5, 5), 'uint8')

  for i in range(1,19):
    array_one = np.round(pr_mask[:,:,i])
    op = cv2.morphologyEx(array_one, cv2.MORPH_OPEN, kernel)
    if sum(sum(op ==1)) > 100:
      tags.append(i)
      pr_mask_int[op ==1] = i 

  img_segmented = np.array(Image.fromarray(pr_mask_int[:img_scaled_arr[1], :img_scaled_arr[2]]).resize(img.size))

  seg = get_colored_segmentation_image(img_segmented,19, colors=colors)

  fused_img = ((np.array(img)/255)/2 + seg/2).astype('float32')

  seg = Image.fromarray((seg*255).astype(np.uint8))
  fused_img  = Image.fromarray((fused_img *255).astype(np.uint8))

  #get the legends
  legend_predicted = get_legends(c, colors, tags)

  final_img = concat_lengends(np.array(fused_img), np.array(legend_predicted))

  return final_img, seg
  
  
  
inputs = [gr.Image(type="filepath", label="Car Image")]
outputs = [gr.Image(type="PIL.Image", label="Detected Segments Image"),gr.Image(type="PIL.Image", label="Segment Image")]


title = "Car Parts Segmentation APP"
description = """This demo uses AI Models to detect 18 parts of cars: \n
1: background, 
2: back bumper, 
3: back glass, 
4: back left door, 
5: back left light, 
6: back right door, 
7: back right light, 
8: front bumper, 
9: front glass, 
10: front left door, 
11: front left light, 
12: front right door, 
13: front right light, 
14: hood, 
15: left mirror, 
16: right mirror, 
17: tailgate, 
18: trunk, 
19: wheel"""

examples = [['test_image.jpeg']]
io = gr.Interface(fn=main_convert, inputs=inputs, outputs=outputs, title=title, description=description, examples=examples,
                  css= """.gr-button-primary { background: -webkit-linear-gradient( 
                    90deg, #355764 0%, #55a8a1 100% ) !important;     background: #355764;
                        background: linear-gradient( 
                    90deg, #355764 0%, #55a8a1 100% ) !important;
                        background: -moz-linear-gradient( 90deg, #355764 0%, #55a8a1 100% ) !important;
                        background: -webkit-linear-gradient( 
                    90deg, #355764 0%, #55a8a1 100% ) !important;
                    color:white !important}"""
                  )
                  
io.launch()