try cv2import only

app.py

@@ -1,73 +1,74 @@
-from icevision.all import *
-import icedata
-import PIL, requests
-import torch
-from torchvision import transforms
-import gradio as gr
+import cv2
+# 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)
+# # 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=data_dir / "odFridgeObjects/annotations", images_dir=data_dir / "odFridgeObjects/images")
+# # Create the parser
+# parser = parsers.VOCBBoxParser(annotations_dir=data_dir / "odFridgeObjects/annotations", images_dir=data_dir / "odFridgeObjects/images")
 
-# Parse annotations to create records
-train_records, valid_records = parser.parse()
+# # Parse annotations to create records
+# train_records, valid_records = parser.parse()
 
-class_map = parser.class_map
+# 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) 
+# 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 = 384
-train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), 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)
+# # Transforms
+# # size is set to 384 because EfficientDet requires its inputs to be divisible by 128
+# image_size = 384
+# train_tfms = tfms.A.Adapter([*tfms.A.aug_tfms(size=image_size, presize=512), 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')
+# learn = learn.load('model')
 
-def show_preds(input_image, display_label, display_bbox, detection_threshold):
+# def show_preds(input_image, display_label, display_bbox, detection_threshold):
 
-    if detection_threshold==0: detection_threshold=0.5
+#     if detection_threshold==0: detection_threshold=0.5
 
-    img = PIL.Image.fromarray(input_image, 'RGB')
+#     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")
+#     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']
+#     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)
+# # 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")
+# 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")
+# 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')
+# # 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')
 
-# #  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 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)
+# # #  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)
+# gr_interface.launch(inline=False, share=True, debug=True)