added hockey breeds v2 page and try it functionality

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/images/samples/v2/v2-sample1-090124.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -17,7 +17,15 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
 ```source .venv/bin/activate```
 
 ## Update requirements.txt
-```pip freeze > requirements.txt```
+There are two requirements.txt files, the requirements-cpu.txt can be used for smaller installations but will only use CPU based 
+pytorch computations.
+
+Keeping them in sync is important, *to do so on a CPU installed dev system* do the following:
+
+1.```pip freeze > requirements-cpu.txt```
+1.```diff requirements-cpu.txt requirements.txt > req-patch.diff```
+1. Edit the req-patch.diff to remove the torch* related diffs (the requirements.txt torch entries should not have +cpu)
+1. ```patch -R requirements.txt req-patch.diff```
 
 ## Manual Testing
 To run in google cloud shell: 

requirements-cpu.txt

@@ -41,11 +41,14 @@ murmurhash==1.0.10
 narwhals==1.6.0
 networkx==3.2.1
 numpy==1.26.4
+opencv-python==4.10.0.84
 packaging==24.1
 pandas==2.2.2
 pillow==10.4.0
 preshed==3.0.9
 protobuf==5.28.0
+psutil==6.0.0
+py-cpuinfo==9.0.0
 pyarrow==17.0.0
 pydantic==2.9.0
 pydantic_core==2.23.2
@@ -61,6 +64,7 @@ rich==13.8.0
 rpds-py==0.20.0
 scikit-learn==1.5.1
 scipy==1.14.1
+seaborn==0.13.2
 shellingham==1.5.4
 six==1.16.0
 smart-open==7.0.4
@@ -84,6 +88,8 @@ tqdm==4.66.5
 typer==0.12.5
 typing_extensions==4.12.2
 tzdata==2024.1
+ultralytics==8.2.90
+ultralytics-thop==2.0.6
 urllib3==2.2.2
 wasabi==1.1.3
 watchdog==4.0.2

requirements.txt

@@ -41,11 +41,14 @@ murmurhash==1.0.10
 narwhals==1.6.0
 networkx==3.2.1
 numpy==1.26.4
+opencv-python==4.10.0.84
 packaging==24.1
 pandas==2.2.2
 pillow==10.4.0
 preshed==3.0.9
 protobuf==5.28.0
+psutil==6.0.0
+py-cpuinfo==9.0.0
 pyarrow==17.0.0
 pydantic==2.9.0
 pydantic_core==2.23.2
@@ -61,6 +64,7 @@ rich==13.8.0
 rpds-py==0.20.0
 scikit-learn==1.5.1
 scipy==1.14.1
+seaborn==0.13.2
 shellingham==1.5.4
 six==1.16.0
 smart-open==7.0.4
@@ -84,6 +88,8 @@ tqdm==4.66.5
 typer==0.12.5
 typing_extensions==4.12.2
 tzdata==2024.1
+ultralytics==8.2.90
+ultralytics-thop==2.0.6
 urllib3==2.2.2
 wasabi==1.1.3
 watchdog==4.0.2

src/Hockey_Breeds.py

@@ -53,7 +53,7 @@ st.markdown(desc)
 st.image("src/images/samples/sampl_batch.png")
 st.subheader("Validation Results")
 st.markdown('Validation of the model\'s performance was done using 26 images not included in the training set.  The model performed fairly well against the validation dataset, with only 1 misclassified image.')
-st.image("src/images/artifacts/confusion_matrix.png", caption="Confusion Matrix for Hockey Breeds ")
+st.image("src/images/artifacts/confusion_matrix_v1.png", caption="Confusion Matrix for Hockey Breeds ")
 
 st.subheader("Try It Out")
 

src/Home.py

@@ -10,7 +10,7 @@ st.title('Welcome To Top Shelf :goal_net:',
 st.subheader('Artificial Intelligence for Hockey Coaches and Players',
              help='Proof of concept application')
 
-overview = '''**Top Shelf** helps coaches and players analyze their gameplay, providing helpful suggestions on areas for improvement.  
+overview = '''**Top Shelf** helps coaches and players analyze their gameplay, providing helpful suggestions & recommendations on areas for improvement.  
 
 We're starting with a focus on ice hockey, however this same technology could apply to other "invasion" games and sports, for example lacrosse, basketball, soccer, etc.
 

src/hockey_object_detection.py

@@ -1,15 +1,100 @@
 import streamlit as st
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_download
+import cv2
+import numpy as np
+
+@st.cache_resource
+def get_model():
+    repo_id = "danbiagini/hockey_breeds_v2"
+    return hf_hub_download(repo_id=repo_id, filename="hockey_breeds-v2-101623.pt")
+
+def run_inference(img, model, thresh=0.5):
+    model = YOLO(model_f)
+    st.session_state.results = model(img)
+    return draw_hockey_boxes(img, st.session_state.results, thresh)
+
+def draw_hockey_boxes(frame, results, thresh=0.5):
+    colors = {0: (0, 255, 0), 1: (255, 0, 0), 2: (0, 0, 255), 3: (128, 0, 0), 4: (0, 128, 0), 5: (0, 0, 128), 6: (0, 64, 0), 7: (64, 0, 0), 8: (0, 0, 64)}
+    font_scale = frame.shape[0] / 500
+    objects = []
+    
+    for name in results:
+        for box in name.boxes.data.tolist():
+            x1, y1, x2, y2, score, class_id = box
+            objects.append((name.names[int(class_id)], score))
+
+            if score > thresh:
+                cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), colors[(class_id % 9)], 3)
+                cv2.putText(frame, f'{name.names[int(class_id)].upper()}: {score:.2f}', (int(x1), int(y1 - 10)),
+                  cv2.FONT_HERSHEY_SIMPLEX, font_scale, colors[(class_id % 9)], 3, cv2.LINE_AA)
+            else:
+                print(f'Found an object under confidence threshold {thresh} type: {name.names[class_id]}, score:{score}, x1, y2:{x1}, {y2}')
+    return objects
+
+
+if 'results' not in st.session_state:
+    st.session_state.results = []
 
 st.set_page_config(page_title='Hockey Breeds v2 - Objects', layout="wide",
                    page_icon=":frame_with_picture:")
 
 st.title('Hockey Breeds v2 - Objects')
-st.markdown('The first version of Hockey Breeds was fun and educational, but not useful for analyzing hockey videos.  The second version needs to be able to recognize individual "objects" within an image and ultimately track those objects through game play')
+intro = '''The first version of Hockey Breeds was fun and educational, but not useful for analyzing hockey videos.  The second version is to a proof of concept 
+with the ability to recognize individual "objects" within an image, which paves the way to ultimately tracking those objects through game play.'''
+
+st.markdown(intro)
 st.subheader('Object Detection')
 
 desc = '''Hockey Breed detector v2 uses a state of the art (circa 2023) computer vision approach.
 
 I used the same training images as the first version of the Hockey Breeds model, but change the ML algorithm to use YOLO object detection (YOLO v8). 
-The output will be a set of hockey objects (defined by "bounding boxes") with labels for any hockey image uploaded.'''
+The output will be a set of hockey objects (defined by "bounding boxes") with labels for any hockey image uploaded.
+
+**Object List**:
+1. net
+1. stick
+1. puck
+1. skater
+1. goalie
+1. referee
+'''
 
 st.markdown(desc)
+
+st.subheader("Sample")
+st.image('src/images/samples/v2/v2-sample1-090124.png', caption='Sample image with hockey objects detected')
+
+st.subheader("Validation Results")
+
+st.markdown('''Validation of the model\'s performance was done using 15 images not included in the training set.  The model had many issues; it did poorly with detecting *pucks* and *sticks* vs backgrounds and even goalies and skaters.  It did very well on detecting referees.''')
+st.image("src/images/artifacts/confusion_matrix_v2.png", caption="Confusion Matrix for Hockey Breeds v2", )
+
+st.subheader("Try It Out")
+
+img = st.file_uploader("Upload an image for object detection", type=["jpg", "jpeg", "png"])
+
+if img is not None:
+    thresh =st.slider('Set the object confidence threshold', min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+    with st.status("Detecting hockey objects..."):
+        st.write("Loading model...")
+        model_f = get_model()
+
+        st.write("Processing image...")
+        frame = cv2.imdecode(np.frombuffer(img.read(), np.uint8), 1)
+        
+        st.write("Running inference on image...")
+        objects = run_inference(frame, model_f, thresh)
+        st.dataframe(objects, column_config={
+            "0": "Object",
+            "1": "Confidence"
+        })
+
+    # check if the results list is empty
+    if len(st.session_state.results) == 0:
+        st.image(img, caption='Uploaded Image')
+    else:
+        st.image(frame, caption='Uploaded Image')
+
+else:
+    st.session_state.results = []