Upload app.py

app.py

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+from icevision.all import *
+import icedata
+import PIL, requests
+import torch
+from torchvision import transforms
+import gradio as gr
+
+# Download the dataset
+url = "https://cvbp-secondary.z19.web.core.windows.net/datasets/object_detection/odFridgeObjects.zip"
+dest_dir = "fridge"
+data_dir = icedata.load_data(url, dest_dir)
+
+# Create the parser
+
+
+parser = parsers.VOCBBoxParser(annotations_dir="Images/Annotated/augmented", images_dir="Images/Annotated/augmented")
+
+
+# Parse annotations to create records
+train_records, valid_records = parser.parse()
+
+class_map = parser.class_map
+
+extra_args = {}
+model_type = models.torchvision.retinanet
+backbone = model_type.backbones.resnet50_fpn
+# Instantiate the model
+model = model_type.model(backbone=backbone(pretrained=True), num_classes=len(parser.class_map), **extra_args) 
+
+# Transforms
+# size is set to 384 because EfficientDet requires its inputs to be divisible by 128
+image_size = 640
+train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=768), tfms.A.Normalize()])
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(image_size), tfms.A.Normalize()])
+# Datasets
+train_ds = Dataset(train_records, train_tfms)
+valid_ds = Dataset(valid_records, valid_tfms)
+# Data Loaders
+train_dl = model_type.train_dl(train_ds, batch_size=8, num_workers=4, shuffle=True)
+valid_dl = model_type.valid_dl(valid_ds, batch_size=8, num_workers=4, shuffle=False)
+metrics = [COCOMetric(metric_type=COCOMetricType.bbox)]
+learn = model_type.fastai.learner(dls=[train_dl, valid_dl], model=model, metrics=metrics)
+
+learn = learn.load('model')
+
+import os
+for root, dirs, files in os.walk(r'sample_images/'):
+    for filename in files:
+        print(filename)
+
+examples = ["sample_images/"+file for file in files] 
+article="<p style='text-align: center'><a href='https://dicksonneoh.com/fridge-detector/' target='_blank'>Blog post</a></p>"
+enable_queue=True
+
+
+#examples = [['sample_images/3.jpg']]
+examples = [["sample_images/"+file] for file in files] 
+
+def show_preds(input_image, display_label, display_bbox, detection_threshold):
+
+    if detection_threshold==0: detection_threshold=0.5
+
+    img = PIL.Image.fromarray(input_image, 'RGB')
+
+    pred_dict  = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=detection_threshold,
+                                           display_label=display_label, display_bbox=display_bbox, return_img=True, 
+                                           font_size=16, label_color="#FF59D6")
+
+    return pred_dict['img']
+
+# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
+display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True)
+display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
+
+detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold")
+
+outputs = gr.outputs.Image(type="pil")
+
+# Option 1: Get an image from local drive
+gr_interface = gr.Interface(fn=show_preds, inputs=["image", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - Fridge Object', article=article, examples=examples)
+
+# #  Option 2: Grab an image from a webcam
+# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
+
+# #  Option 3: Continuous image stream from the webcam
+# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
+
+
+gr_interface.launch(inline=False, share=True, debug=True)