Create app.py

app.py

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+from PIL import Image
+import numpy as np
+import torch
+from torchvision import transforms, models
+from onnx import numpy_helper
+import os
+import onnxruntime as rt
+from matplotlib.colors import hsv_to_rgb
+import cv2
+import gradio as gr
+
+preprocess = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Start from ORT 1.10, ORT requires explicitly setting the providers parameter if you want to use execution providers
+# other than the default CPU provider (as opposed to the previous behavior of providers getting set/registered by default
+# based on the build flags) when instantiating InferenceSession.
+# For example, if NVIDIA GPU is available and ORT Python package is built with CUDA, then call API as following:
+# onnxruntime.InferenceSession(path/to/model, providers=['CUDAExecutionProvider'])
+os.system("wget https://github.com/AK391/models/raw/main/vision/object_detection_segmentation/mask-rcnn/model/MaskRCNN-10.onnx")
+sess = rt.InferenceSession("MaskRCNN-10.onnx")
+
+outputs = sess.get_outputs()
+
+
+classes = [line.rstrip('\n') for line in open('coco_classes.txt')]
+num_classes = len(classes)
+
+def get_palette():
+    # prepare and return palette
+    palette = [0] * num_classes * 3
+    
+    for hue in range(num_classes):
+        if hue == 0: # Background color
+            colors = (0, 0, 0)
+        else:
+            colors = hsv_to_rgb((hue / num_classes, 0.75, 0.75))
+            
+        for i in range(3):
+            palette[hue * 3 + i] = int(colors[i] * 255)
+            
+    return palette
+
+def colorize(labels):
+    # generate colorized image from output labels and color palette
+    result_img = Image.fromarray(labels).convert('P', colors=num_classes)
+    result_img.putpalette(get_palette())
+    return np.array(result_img.convert('RGB'))
+
+def visualize_output(image, output):
+    assert(image.shape[0] == output.shape[1] and \
+           image.shape[1] == output.shape[2]) # Same height and width
+    assert(output.shape[0] == num_classes)
+    
+    # get classification labels
+    raw_labels = np.argmax(output, axis=0).astype(np.uint8)
+
+    # comput confidence score
+    confidence = float(np.max(output, axis=0).mean())
+
+    # generate segmented image
+    result_img = colorize(raw_labels)
+    
+    # generate blended image
+    blended_img = cv2.addWeighted(image[:, :, ::-1], 0.5, result_img, 0.5, 0)
+    
+    result_img = Image.fromarray(result_img)
+    blended_img = Image.fromarray(blended_img)
+
+    return confidence, result_img, blended_img, raw_labels
+
+def inference(img):
+  input_image = Image.open(img)
+  orig_tensor = np.asarray(input_image)
+  input_tensor = preprocess(input_image)
+  input_tensor = input_tensor.unsqueeze(0)
+  input_tensor = input_tensor.detach().cpu().numpy()
+  output_names = list(map(lambda output: output.name, outputs))
+  input_name = sess.get_inputs()[0].name
+  detections = sess.run(output_names, {input_name: input_tensor})
+  output, aux = detections
+  conf, result_img, blended_img, _ = visualize_output(orig_tensor, output[0])
+  return blended_img
+  
+title="Mask R-CNN"
+description="This model is a real-time neural network for object instance segmentation that detects 80 different classes."
+examples=[["examplefcn.png"]]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),gr.outputs.Image(type="pil"),title=title,description=description,examples=examples).launch(enable_queue=True)