Initial commit - BraceIQMed platform with frontend, API, and brace generator

scoliovis-api/main.py

@@ -0,0 +1,104 @@
+import dotenv
+dotenv.load_dotenv()
+from fastapi import FastAPI, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+
+# -- 1. Create FastAPI app --
+app = FastAPI()
+
+# -- 2. Enable CORS All Origin --
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+# -- 3. Load models if not yet loaded --
+# from scoliovis.get_model import get_detection_model # YoloV5
+# detection_model = get_detection_model() # YoloV5
+from scoliovis.kprcnn import predict, kprcnn_to_scoliovis_api_format # KPRCNN
+
+# 4. -- Routes --
+@app.get("/")
+async def read_root():
+    print("Read Root started")
+    return {"Hello": "World", "Message": "Welcome to ScolioVis-API! Send a POST request these APIs to get started!",
+    "ModelPredict": "/v2/getprediction"
+    }
+
+# Keypoint RCNN Model
+from io import BytesIO
+from PIL import Image
+import cv2 as cv
+import numpy as np
+
+@app.post('/v2/getprediction')
+async def get_prediction_v2(image: UploadFile):
+    # - Preprocess 'Image'
+    image = Image.open(BytesIO(await image.read())).convert('RGB') # Decode UploadFile -> PIL
+    image = cv.cvtColor(np.array(image), cv.COLOR_RGB2BGR) # PIL RGB -> Mat(OpenCV) RGB -> BGR
+
+    # - Keypoint RCNN Predict
+    bboxes, keypoints, scores = predict(image)[0]
+    api_object = kprcnn_to_scoliovis_api_format(bboxes, keypoints, scores, image.shape)
+    return api_object
+
+# YOLO V5
+# import base64
+# import pandas as pd
+# import json
+
+# def detect(imgs):
+#   detection_model.conf = 0.50
+#   results = detection_model(imgs, size=640) # batch of images
+  
+#   # Sort by confidence all and get top 17
+#   results_df = [pred_df.sort_values('confidence', ascending = False).head(17) for pred_df in results.pandas().xyxy]
+#   results_df_n = [pred_df.sort_values('confidence', ascending = False).head(17) for pred_df in results.pandas().xyxyn]
+
+#   # Sort by min y so they're ordered from top to bottom
+#   results_df = [pred_df.sort_values('ymin', ascending = True) for pred_df in results_df]
+#   results_df_n = [pred_df.sort_values('ymin', ascending = True) for pred_df in results_df_n]
+
+#   return results_df, results_df_n
+
+# @app.post("/getprediction")
+# async def get_prediction(image: UploadFile):
+#     # - Preprocess 'image'
+#     image = Image.open(BytesIO(await image.read())).convert('RGB') # Decode UploadFile -> PIL
+#     image = cv.cvtColor(np.array(image), cv.COLOR_RGB2BGR) # PIL RGB -> Mat(OpenCV) RGB -> BGR
+
+#     # - Object Detection
+#     results_df, results_df_n = detect(image)
+#     detections = json.loads(results_df[0].to_json(orient="records"))
+#     detections_n = json.loads(results_df_n[0].to_json(orient="records"))
+
+#     # - Create Base64 from Detection
+#     jpeg_string = base64.b64encode(cv.imencode('.jpg', image)[1]).decode()
+
+#     # - Landmark Detection
+#     # df = pd.read_csv('data/C AP test landmarks.csv', header=None)
+#     height, width = image.shape
+#     landmarks = [0.43909,0.57581,0.44172,0.56968,0.43646,0.56617,0.43032,0.55653,0.42857,0.5539,0.42244,0.53812,0.41718,0.52498,0.40578,0.50482,0.38738,0.48904,0.34882,0.45223,0.33041,0.44172,0.28396,0.3979,0.28396,0.38826,0.25592,0.37511,0.25416,0.37248,0.25855,0.37862,0.26643,0.38563,0.28571,0.41455,0.30237,0.4312,0.34531,0.48203,0.37423,0.50394,0.4128,0.57055,0.43909,0.5837,0.47415,0.62138,0.47765,0.63453,0.52585,0.66959,0.52498,0.68098,0.52147,0.68449,0.52235,0.68536,0.51183,0.66258,0.48203,0.64417,0.42682,0.6021,0.38913,0.58808,0.38124,0.57493,0.071538,0.072303,0.091048,0.096787,0.10214,0.1075,0.12663,0.13007,0.13657,0.14614,0.15647,0.17024,0.17024,0.18592,0.20161,0.215,0.21385,0.22992,0.24981,0.26473,0.26358,0.27621,0.29763,0.31331,0.30987,0.32249,0.35004,0.35501,0.36266,0.36496,0.40742,0.39977,0.42043,0.41086,0.46174,0.4407,0.47666,0.45409,0.51836,0.48852,0.52946,0.5,0.56618,0.54093,0.58569,0.55394,0.61515,0.5899,0.62969,0.61132,0.66641,0.65302,0.68133,0.6733,0.71385,0.72073,0.728,0.73718,0.76243,0.77735,0.785,0.80796,0.83818,0.85845,0.86113,0.87643,0.90207,0.90551]
+    
+#     return {
+#         "landmarks": landmarks,
+#         "detections": detections,
+#         "normalized_detections": detections_n,
+#         "base64_image": jpeg_string,
+#     }
+
+# Useful Stackoverflow/GitHub Solutions:
+# - Convert PIL -> Mat(OpenCV) & Vice Versa
+# https://stackoverflow.com/questions/14134892/convert-image-from-pil-to-opencv-format
+
+# - How to Receive Image -> Process with Cv2 -> Return Image in FastAPI
+# https://stackoverflow.com/questions/61333907/receiving-an-image-with-fast-api-processing-it-with-cv2-then-returning-it
+
+# - Receiving UploadFile -> PIL
+# https://github.com/tiangolo/fastapi/discussions/4308
+
+# Run the application with:
+# uvicorn main:app --reload