Review1

README.md

@@ -1,6 +1,7 @@
 # Trash AI: Web application for serverless image classification of trash
 
 [![Website](https://img.shields.io/badge/Web-TrashAI.org-blue)](https://www.trashai.org)
+[![status](https://joss.theoj.org/papers/6ffbb0f89e6c928dad6908a02639789b/status.svg)](https://joss.theoj.org/papers/6ffbb0f89e6c928dad6908a02639789b)
 
 ### Project Information
 
@@ -14,7 +15,7 @@ Trash AI is a web application where users can upload photos of litter, which wil
 
 #### Demo
 
-[![image](https://user-images.githubusercontent.com/26821843/188515526-33e1196b-6830-4187-8fe4-e68b2bd4019e.png)](https://youtu.be/HHrjUpQynUM)
+[![image](https://user-images.githubusercontent.com/26821843/188515526-33e1196b-6830-4187-8fe4-e68b2bd4019e.png)](https://youtu.be/u0DxGrbPOC0)
 
 ## Deployment
 
@@ -71,6 +72,9 @@ docker rm -v $id
 
 -   Runs the complex stuff so you don't have to.
 
+### Tests
+Instructions for automated and manual tests [here](https://github.com/code4sac/trash-ai/tree/production/frontend/__tests__). 
+
 ## Contribute
 
 We welcome contributions of all kinds.

docs/localdev.md

@@ -16,12 +16,12 @@ These values can be adjusted by editing the localdev env file [.env](../localdev
 It's suggested you work in branch `local` by creating your own local branch when developing
 Pushing / merging PR's to any branches with a prefix of `aws/` will trigger deployment actions
 For full functionality you will want to get a Google Maps API key and name it VITE_GOOGLE_MAPS_API_KEY, but it is not required
-=======
 
 Pushing / merging PR's to any branches with a prefix of `aws/` will
 trigger deployment actions, when developing locally, create a new branch
 and submit a pull request to `aws/trashai-staging`
 
+
 ---
 # Set up 
 

frontend/src/views/about.vue

@@ -23,6 +23,10 @@
             To get started, visit the Upload Tab or
             <a href="/uploads/0">click here</a>.
         </p>
+        <h2>Tutorial</h2>
+        <p>
+            <iframe width="560" height="315" src="https://www.youtube.com/embed/u0DxGrbPOC0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
+        </p>
 
         <h2>What is it?</h2>
         <p>
@@ -54,9 +58,8 @@
         <h2>Disclaimer about uploaded images</h2>
         <p>
             The current version of Trash AI and the model we are using is just a
-            start! When you upload an image, we are storing the image and the
-            classification in an effort to expand the trash dataset and improve
-            the model over time.
+            start! The tool works best for images of individual pieces of trash imaged less than 1 meter away from the camera. 
+            We are looking for collaborators who can help us improve this project.
         </p>
 
         <h2>Reporting issues and improvements</h2>

notebooks/data_reader/data_reader.R

@@ -0,0 +1,50 @@
+#Working directory ----
+setwd("notebooks/data_reader") #Change this to your working directory
+
+#Libraries ----
+library(rio)
+library(jsonlite)
+library(ggplot2)
+library(data.table)
+
+# Data import ----
+json_list <- import_list("example_data_download2.zip")
+
+# Get path of the summary table. 
+summary_metadata <- names(json_list)[grepl("summary.json", names(json_list))]
+
+# Get path of the image metadata. 
+image_metadata <- names(json_list)[!grepl("(.jpg)|(.png)|(.tif)|(schema)|(summary)", names(json_list))][-1]
+
+# Filter the summary data. 
+summary_json <- json_list[[summary_metadata]]
+
+# Flatten the summary data. 
+flattened_summary <- data.frame(name = summary_json$detected_objects$name,
+                                     count = summary_json$detected_objects$count)
+# Filter the image data. 
+image_json <- json_list[image_metadata] 
+
+# Flatten the image data. 
+flattened_images <- lapply(1:length(image_json), function(i){
+    print(i)
+    data.frame(hash = image_json[[i]]$hash, 
+               filename = image_json[[i]]$filename, 
+               datetime = if(!is.null(image_json[[i]]$exifdata$DateTimeOriginal)){image_json[[i]]$exifdata$DateTimeOriginal} else{NA}, 
+               latitude = if(!is.null(image_json[[i]]$exifdata$GPSLatitude)){image_json[[i]]$exifdata$GPSLatitude} else{NA}, 
+               longitude = if(!is.null(image_json[[i]]$exifdata$GPSLongitude)){image_json[[i]]$exifdata$GPSLongitude} else{NA}, 
+               score = if(!is.null(image_json[[i]]$metadata$score)){image_json[[i]]$metadata$score} else{NA}, 
+               label = if(!is.null(image_json[[i]]$metadata$label)){image_json[[i]]$metadata$label} else{NA})
+}) |>
+    rbindlist()
+
+# Test equivalence in counts. 
+nrow(flattened_images[!is.na(flattened_images$label),]) == sum(flattened_summary$count)
+
+# Figure creation ----
+ggplot(flattened_summary, aes(y = reorder(name, count), x = count, fill = name)) +
+    geom_bar(stat = "identity") +
+    theme_classic(base_size = 15) +
+    theme(legend.position = "none") +
+    labs(x = "Count", y = "Type")
+

paper.bib

@@ -88,7 +88,8 @@ @ARTICLE{Hapich:2022
   number   =  1,
   pages    = "15",
   month    =  jun,
-  year     =  2022
+  year     =  2022,
+  doi      =  "10.1186/s43591-022-00035-1"
 }
 
 @misc{Waterboards:2018,
@@ -108,11 +109,10 @@ @article{vanLieshout:2020
     number = {8},
     pages = {e2019EA000960},
     keywords = {plastic pollution, object detection, automated monitoring, deep learning, artificial intelligence, river plastic},
-    doi = {https://doi.org/10.1029/2019EA000960},
+    doi = {10.1029/2019EA000960},
     url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019EA000960},
     eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019EA000960},
     note = {e2019EA000960 10.1029/2019EA000960},
-    abstract = {Abstract Quantifying plastic pollution on surface water is essential to understand and mitigate the impact of plastic pollution to the environment. Current monitoring methods such as visual counting are labor intensive. This limits the feasibility of scaling to long-term monitoring at multiple locations. We present an automated method for monitoring plastic pollution that overcomes this limitation. Floating macroplastics are detected from images of the water surface using deep learning. We perform an experimental evaluation of our method using images from bridge-mounted cameras at five different river locations across Jakarta, Indonesia. The four main results of the experimental evaluation are as follows. First, we realize a method that obtains a reliable estimate of plastic density (68.7\% precision). Our monitoring method successfully distinguishes plastics from environmental elements, such as water surface reflection and organic waste. Second, when trained on one location, the method generalizes well to new locations with relatively similar conditions without retraining (≈50\% average precision). Third, generalization to new locations with considerably different conditions can be boosted by retraining on only 50 objects of the new location (improving precision from ≈20\% to ≈42\%). Fourth, our method matches visual counting methods and detects ≈35\% more plastics, even more so during periods of plastic transport rates of above 10 items per meter per minute. Taken together, these results demonstrate that our method is a promising way of monitoring plastic pollution. By extending the variety of the data set the monitoring method can be readily applied at a larger scale.},
     year = {2020}
 }
 
@@ -145,7 +145,8 @@ @ARTICLE{Lynch:2018
   number   =  1,
   pages    = "6",
   month    =  jun,
-  year     =  2018
+  year     =  2018,
+  doi      =  "10.1186/s40965-018-0050-y"
 }
 
 
@@ -176,7 +177,7 @@ @article{Majchrowska:2022
 pages = {274-284},
 year = {2022},
 issn = {0956-053X},
-doi = {https://doi.org/10.1016/j.wasman.2021.12.001},
+doi = {10.1016/j.wasman.2021.12.001},
 url = {https://www.sciencedirect.com/science/article/pii/S0956053X21006474},
 author = {Sylwia Majchrowska and Agnieszka Mikołajczyk and Maria Ferlin and Zuzanna Klawikowska and Marta A. Plantykow and Arkadiusz Kwasigroch and Karol Majek},
 keywords = {Object detection, Semi-supervised learning, Waste classification benchmarks, Waste detection benchmarks, Waste localization, Waste recognition},
@@ -193,4 +194,3 @@ @misc{Proença:2020
   year = {2020},
   copyright = {arXiv.org perpetual, non-exclusive license}
 }
-