app.py

import cv2
import numpy as np
import argparse
import base64
import io
import os
import re
import sys
import traceback
import uuid
from typing import List, Optional
from PIL import ImageEnhance
import traceback
import cv2
import numpy as np
import pandas as pd
import pinecone
import pyiqa
import timm
import torch
import uvicorn
from dotenv import load_dotenv
from fastapi import FastAPI, File, Form, HTTPException, UploadFile
from PIL import Image
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer, util
from transformers import (
    AutoFeatureExtractor,
    AutoModel,
    DonutProcessor,
    VisionEncoderDecoderModel,
)
from fashion_clip.fashion_clip import FashionCLIP
load_dotenv()
pinecone.init(api_key=os.getenv("PINECONE_KEY"), environment=os.getenv("PINECONE_ENV"))
DETECTION_URL = "/object-detection/"
CLASSIFICATION_URL = "/object-classification/"
QUALITY_ASSESSMENT_URL = "/quality-assessment/"
FACE_URL = "/face-anonymization/"
LICENCE_URL = "/licenceplate-anonymization/"
DOCUMENT_QA = "/document-qa/"
IMAGE_SIMILARITY_DEMO = "/find-similar-image/"
IMAGE_SIMILARITY_PINECONE_DEMO = "/find-similar-image-pinecone/"
INDEX_NAME = "imagesearch-demo"
INDEX_DIMENSION = 512
TMP_DIR = "tmp"

def enhance_image(pil_image):
    # Convert PIL Image to OpenCV format
    open_cv_image = np.array(pil_image)
    # Convert RGB to BGR
    open_cv_image = open_cv_image[:, :, ::-1].copy()

    # Convert to grayscale
    gray = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2GRAY)

    # Histogram equalization
    equ = cv2.equalizeHist(gray)
    
    # Adaptive Histogram Equalization
    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
    adaptive_hist_eq = clahe.apply(gray)

    # Gaussian Blurring
    gaussian_blurred = cv2.GaussianBlur(adaptive_hist_eq, (5,5), 0)

    # Noise reduction
    denoised = cv2.medianBlur(gaussian_blurred, 3)

    # Brightness & Contrast adjustment
    lab = cv2.cvtColor(open_cv_image, cv2.COLOR_BGR2Lab)
    l, a, b = cv2.split(lab)
    cl = clahe.apply(l)
    limg = cv2.merge((cl, a, b))
    enhanced_image = cv2.cvtColor(limg, cv2.COLOR_Lab2BGR)

    # Convert back to PIL Image
    enhanced_pil_image = Image.fromarray(cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2RGB))

    # IMAGE AUGMENTATION
    # For demonstration purposes, let's do a simple brightness adjustment.
    # In practice, choose the augmentations that suit your task.
    enhancer = ImageEnhance.Brightness(enhanced_pil_image)
    enhanced_pil_image = enhancer.enhance(1.2)  # Brighten the image by 20%
    
    return enhanced_pil_image

    
if INDEX_NAME not in pinecone.list_indexes():
    pinecone.create_index(INDEX_NAME, dimension=512, metric='cosine')
    
print("Connecting to Pinecone Index")
index = pinecone.Index(INDEX_NAME)
    
os.makedirs(TMP_DIR, exist_ok=True)

# licence_model = torch.hub.load(
#     "ultralytics/yolov5", "custom", path="Licenseplate_model.pt", device="cpu", force_reload=True
# )
# licence_model.cpu()

# detector = cv2.dnn.DetectionModel(
#     "res10_300x300_ssd_iter_140000_fp16.caffemodel", "deploy.prototxt"
# )

# processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
# doc_qa_model = VisionEncoderDecoderModel.from_pretrained(
#     "naver-clova-ix/donut-base-finetuned-docvqa"
# )

device = "cuda" if torch.cuda.is_available() else "cpu"
# doc_qa_model.to(device)


os.makedirs(TMP_DIR, exist_ok=True)

# model = torch.hub.load(
#     "ultralytics/yolov5", "custom", path="best.pt", device="cpu", force_reload=True
# )
# model.cpu()

# classes = [
#     "gas-distribution-meter",
#     "gas-distribution-piping",
#     "gas-distribution-regulator",
#     "gas-distribution-valve",
# ]

# class_to_idx = {
#     "gas-distribution-meter": 0,
#     "gas-distribution-piping": 1,
#     "gas-distribution-regulator": 2,
#     "gas-distribution-valve": 3,
# }

# idx_to_classes = {v: k for k, v in class_to_idx.items()}
# modelname = "resnet50d"
# model_weights = "best_classifer_model.pt"
# num_classes = len(classes)

# classifier_model = timm.create_model(
#     "resnet50d", pretrained=True, num_classes=num_classes, drop_path_rate=0.05
# )
# classifier_model.load_state_dict(
#     torch.load(model_weights, map_location=torch.device("cpu"))["model_state_dict"]
# )

# musiq_metric = pyiqa.create_metric("musiq-koniq", device=torch.device("cpu"))
# image_sim_model = SentenceTransformer("patrickjohncyh/fashion-clip")
# from transformers import AutoProcessor, AutoModelForZeroShotImageClassification

# processor = AutoProcessor.from_pretrained("patrickjohncyh/fashion-clip")
# model = AutoModelForZeroShotImageClassification.from_pretrained("patrickjohncyh/fashion-clip")

# model_ckpt = "nateraw/vit-base-beans"
# extractor = AutoFeatureExtractor.from_pretrained(model_ckpt)
# image_sim_model = AutoModel.from_pretrained(model_ckpt)
fclip = FashionCLIP('fashion-clip')

app = FastAPI(title="CV Demos")

# Define the Response
class Prediction(BaseModel):
    filename: str
    contenttype: str
    prediction: List[float] = []


# define response
@app.get("/")
def root_route():
    return {"error": f"Use GET {IMAGE_SIMILARITY_PINECONE_DEMO} instead of the root route!"}


# @app.post(
#     DETECTION_URL,
# )
# async def predict(file: UploadFile = File(...), quality_check: bool = False):
#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents))
#         if quality_check:
#             print("RUNNING QUALITY CEHCK BEFORE OBJEFCT DETECTION!!!")
#             tmp_file = f"{TMP_DIR}/tmp.png"
#             pil_image.save(tmp_file)
#             score = musiq_metric(tmp_file)
#             if score < 50:
#                 return {
#                     "Error": "Image quality is not sufficient enough to be considered for object detection"
#                 }

#         results = model(pil_image, size=640)  # reduce size=320 for faster inference
#         return results.pandas().xyxy[0].to_json(orient="records")
#     except:
#         e = sys.exc_info()[1]
#         raise HTTPException(status_code=500, detail=str(e))


# @app.post(CLASSIFICATION_URL)
# async def classify(file: UploadFile = File(...)):
#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents))
#         data_mean = (0.485, 0.456, 0.406)
#         data_std = (0.229, 0.224, 0.225)
#         image_size = (224, 224)
#         eval_transforms = timm.data.create_transform(
#             input_size=image_size, mean=data_mean, std=data_std
#         )
#         eval_transforms(pil_image).unsqueeze(dim=0).shape
#         classifier_model.eval()
#         print("RUNNING Image Classification!!!")
#         max_class_idx = np.argmax(
#             classifier_model(eval_transforms(pil_image).unsqueeze(dim=0)).detach().numpy()
#         )
#         predicted_class = idx_to_classes[max_class_idx]
#         print(f"Predicted Class idx: {max_class_idx} with name : {predicted_class}")
#         return {"object": predicted_class}

#     except:
#         e = sys.exc_info()[1]
#         raise HTTPException(status_code=500, detail=str(e))


# @app.post(QUALITY_ASSESSMENT_URL)
# async def quality_check(file: UploadFile = File(...)):
#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents))
#         tmp_file = f"{TMP_DIR}/tmp.png"
#         pil_image.save(tmp_file)
#         score = musiq_metric(tmp_file).detach().numpy().tolist()
#         return {"score": score}

#     except:
#         e = sys.exc_info()[1]
#         raise HTTPException(status_code=500, detail=str(e))


# def anonymize_simple(image, factor=3.0):
#     # automatically determine the size of the blurring kernel based
#     # on the spatial dimensions of the input image
#     (h, w) = image.shape[:2]
#     kW = int(w / factor)
#     kH = int(h / factor)
#     # ensure the width of the kernel is odd
#     if kW % 2 == 0:
#         kW -= 1
#     # ensure the height of the kernel is odd
#     if kH % 2 == 0:
#         kH -= 1
#     # apply a Gaussian blur to the input image using our computed
#     # kernel size
#     return cv2.GaussianBlur(image, (kW, kH), 0)


# def anonymize_pixelate(image, blocks=3):
#     # divide the input image into NxN blocks
#     (h, w) = image.shape[:2]
#     xSteps = np.linspace(0, w, blocks + 1, dtype="int")
#     ySteps = np.linspace(0, h, blocks + 1, dtype="int")
#     # loop over the blocks in both the x and y direction
#     for i in range(1, len(ySteps)):
#         for j in range(1, len(xSteps)):
#             # compute the starting and ending (x, y)-coordinates
#             # for the current block
#             startX = xSteps[j - 1]
#             startY = ySteps[i - 1]
#             endX = xSteps[j]
#             endY = ySteps[i]
#             # extract the ROI using NumPy array slicing, compute the
#             # mean of the ROI, and then draw a rectangle with the
#             # mean RGB values over the ROI in the original image
#             roi = image[startY:endY, startX:endX]
#             (B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
#             cv2.rectangle(image, (startX, startY), (endX, endY), (B, G, R), -1)
#     # return the pixelated blurred image
#     return image


# # define response
# @app.get("/")
# def root_route():
#     return {"error": f"Use GET {FACE_URL}  or {LICENCE_URL} instead of the root route!"}


# @app.post(
#     FACE_URL,
# )
# async def face_anonymize(
#     file: UploadFile = File(...), blur_type="simple", quality_check: bool = False
# ):
#     """
#     https://pyimagesearch.com/2020/04/06/blur-and-anonymize-faces-with-opencv-and-python/
#     """
#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents)).convert("RGB")
#         detector = cv2.dnn.DetectionModel(
#             "res10_300x300_ssd_iter_140000_fp16.caffemodel", "deploy.prototxt"
#         )
#         open_cv_image = np.array(pil_image)
#         # Convert RGB to BGR
#         open_cv_image = open_cv_image[:, :, ::-1].copy()
#         (h, w) = open_cv_image.shape[:2]
#         # Getting the detections
#         detections = detector.detect(open_cv_image)
#         if len(detections[2]) > 0:
#             for face in detections[2]:
#                 (x, y, w, h) = face.astype("int")
#                 # extract the face ROI

#                 face = open_cv_image[y : y + h, x : x + w]
#                 if blur_type == "simple":
#                     face = anonymize_simple(face)
#                 else:
#                     face = anonymize_pixelate(face)
#                 open_cv_image[y : y + h, x : x + w] = face

#         _, encoded_img = cv2.imencode(".PNG", open_cv_image)

#         encoded_img = base64.b64encode(encoded_img)
#         return {
#             "filename": file.filename,
#             "dimensions": str(open_cv_image.shape),
#             "encoded_img": encoded_img,
#         }
#     except:
#         e = sys.exc_info()[1]
#         print(traceback.format_exc())
#         raise HTTPException(status_code=500, detail=str(e))


# @app.post(LICENCE_URL)
# async def licence_anonymize(file: UploadFile = File(...), blur_type="simple"):
#     """https://www.kaggle.com/code/gowrishankarp/license-plate-detection-yolov5-pytesseract/notebook#Visualize"""
#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents))
#         results = licence_model(pil_image, size=640)  # reduce size=320 for faster inference
#         pil_image = pil_image.convert("RGB")
#         open_cv_image = np.array(pil_image)
#         open_cv_image = open_cv_image[:, :, ::-1].copy()
#         df = results.pandas().xyxy[0]
#         for i, row in df.iterrows():
#             xmin = int(row["xmin"])
#             ymin = int(row["ymin"])
#             xmax = int(row["xmax"])
#             ymax = int(row["ymax"])
#             licence = open_cv_image[ymin:ymax, xmin:xmax]
#             if blur_type == "simple":
#                 licence = anonymize_simple(licence)
#             else:
#                 licence = anonymize_pixelate(licence)
#             open_cv_image[ymin:ymax, xmin:xmax] = licence

#         _, encoded_img = cv2.imencode(".PNG", open_cv_image)

#         encoded_img = base64.b64encode(encoded_img)
#         return {
#             "filename": file.filename,
#             "dimensions": str(open_cv_image.shape),
#             "encoded_img": encoded_img,
#         }

#     except:
#         e = sys.exc_info()[1]
#         raise HTTPException(status_code=500, detail=str(e))


# def process_document(image, question):
#     # prepare encoder inputs
#     pixel_values = processor(image, return_tensors="pt").pixel_values

#     # prepare decoder inputs
#     task_prompt = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
#     prompt = task_prompt.replace("{user_input}", question)
#     decoder_input_ids = processor.tokenizer(
#         prompt, add_special_tokens=False, return_tensors="pt"
#     ).input_ids

#     # generate answer
#     outputs = doc_qa_model.generate(
#         pixel_values.to(device),
#         decoder_input_ids=decoder_input_ids.to(device),
#         max_length=doc_qa_model.decoder.config.max_position_embeddings,
#         early_stopping=True,
#         pad_token_id=processor.tokenizer.pad_token_id,
#         eos_token_id=processor.tokenizer.eos_token_id,
#         use_cache=True,
#         num_beams=1,
#         bad_words_ids=[[processor.tokenizer.unk_token_id]],
#         return_dict_in_generate=True,
#     )

#     # postprocess
#     sequence = processor.batch_decode(outputs.sequences)[0]
#     sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(
#         processor.tokenizer.pad_token, ""
#     )
#     sequence = re.sub(r"<.*?>", "", sequence, count=1).strip()  # remove first task start token

#     return processor.token2json(sequence)


# @app.post(DOCUMENT_QA)
# async def document_qa(question: str = Form(...), file: UploadFile = File(...)):

#     try:
#         extension = file.filename.split(".")[-1] in ("jpg", "jpeg", "png")
#         if not extension:
#             return "Image must be jpg or png format!"
#         # read image contain
#         contents = await file.read()
#         pil_image = Image.open(io.BytesIO(contents))
#         # tmp_file = f"{TMP_DIR}/tmp.png"
#         # pil_image.save(tmp_file)
#         # answer_git_large = generate_answer_git(git_processor_large, git_model_large, image, question)

#         answer = process_document(pil_image, question)["answer"]

#         return {"answer": answer}

#     except:
#         e = sys.exc_info()[1]
#         raise HTTPException(status_code=500, detail=str(e))


@app.post(IMAGE_SIMILARITY_DEMO)
async def image_search_local(
    images_to_search: List[UploadFile], query_image: UploadFile = File(...), top_k: int = 5
):
    print(
        f"Recived images of length: {len(images_to_search)} needs to retrieve top k  : {top_k} similar images as result"
    )
    try:
        extension = query_image.filename.split(".")[-1] in ("jpg", "jpeg", "png")
        search_images = []
        search_filenames = []
        print("Processing request...")
        for image in images_to_search:
            if image.filename.split(".")[-1] not in ("jpg", "jpeg", "png"):
                return "Image must be jpg or png format!"
            # read image contain
            search_filenames.append(image.filename)
            contents = await image.read()
            search_images.append(Image.open(io.BytesIO(contents)))
        print("Indexing images to search...")

        corpus_embeddings = image_sim_model.encode(
            search_images, convert_to_tensor=True, show_progress_bar=True
        )
        if not extension:
            return "Image must be jpg or png format!"
        # read image contain
        contents = await query_image.read()
        query_image = Image.open(io.BytesIO(contents))
        print("Indexing query image...")

        prompt_embedding = image_sim_model.encode(query_image, convert_to_tensor=True)
        print("Searching query image...")

        hits = util.semantic_search(prompt_embedding, corpus_embeddings, top_k=top_k)
        # hits = pd.DataFrame(hits[0], columns=['corpus_id', 'score'])
        # tmp_file = f"{TMP_DIR}/tmp.png"
        # pil_image.save(tmp_file)
        # answer_git_large = generate_answer_git(git_processor_large, git_model_large, image, question)
        print("Creating the result..")
        similar_images = []
        print("hits ", hits)
        for hit in hits[0]:
            # print("Finding the image ")
            # print("Type of images list ", type(search_images), "similar image id ",  hit['corpus_id'])
            open_cv_image = np.array(search_images[hit["corpus_id"]].convert("RGB"))[:, :, ::-1]
            # print("cv2.imencode the image ")
            _, encoded_img = cv2.imencode(".PNG", open_cv_image)
            # print("base64 the image ")
            encoded_img = base64.b64encode(encoded_img)
            # print("Appending the image ")
            similar_images.append(
                {
                    "filename": search_filenames[hit["corpus_id"]],
                    "dimensions": str(open_cv_image.shape),
                    "score": hit["score"],
                    "encoded_img": encoded_img,
                }
            )
        print("Sending result..")

        return {"similar_images": similar_images}

    except:
        e = sys.exc_info()[1]
        raise HTTPException(status_code=500, detail=str(e))


@app.post(IMAGE_SIMILARITY_PINECONE_DEMO)
async def image_search_pinecone(
    images_to_search: Optional[List[UploadFile]] = File(None),
    query_image: Optional[UploadFile] = File(None),
    top_k: int = 5,
    namespace="av_local",
    action="query",
):
    
    try:
        # Function to delete all files from the database
        print(f"Received request with images_to_search: {images_to_search} and query_image: {query_image} with action: {action}")
        if action == "delete":
            index = pinecone.Index(INDEX_NAME)
            delete_response = index.delete(delete_all=True, namespace=namespace)
            return {f"Deleted the namespace: {namespace}": delete_response}
        
        elif action == "query" and query_image is not None:
            extension = query_image.filename.split(".")[-1] in ("jpg", "jpeg", "png", "JPG", "PNG", "JPEG")
            if not extension:
                return "Image must be jpg or png format!"
            # read image contain
            contents = await query_image.read()
            query_image = Image.open(io.BytesIO(contents))
            print("Indexing query image...")
            query_image = enhance_image(query_image)
            # prompt_embedding = image_sim_model.encode(query_image, convert_to_tensor=True).tolist()
            prompt_embedding = fclip.encode_images([query_image], batch_size=32)[0]
            if INDEX_NAME not in pinecone.list_indexes():
                return {"similar_images": [], "status": "No index found for images"}

            else:
                index = pinecone.Index(INDEX_NAME)
                query_response = index.query(
                    namespace=namespace,
                    top_k=top_k,
                    include_values=True,
                    include_metadata=True,
                    vector=prompt_embedding,
                )
                result_images = [d["metadata"]["file_path"] for d in query_response["matches"]]
                print("Creating the result..")
                similar_images = []
                print("Retrieved matches ", query_response["matches"])
                for file_path in result_images:
                    try:
                        # print("Finding the image ")
                        # print("Type of images list ", type(search_images), "similar image id ",  hit['corpus_id'])
                        open_cv_image = cv2.imread(file_path)
                        # print("cv2.imencode the image ")
                        _, encoded_img = cv2.imencode(".PNG", open_cv_image)
                        # print("base64 the image ")
                        encoded_img = base64.b64encode(encoded_img)
                        # print("Appending the image ")
                        similar_images.append(
                            {
                                "filename": file_path,
                                "dimensions": str(open_cv_image.shape),
                                "score": 0,
                                "encoded_img": encoded_img,
                            }
                        )
                    except:
                         similar_images.append(
                            {
                                "filename": file_path,
                                "dimensions": None,
                                "score": 0,
                                "encoded_img": None,
                            }
                        )
                print("Sending result..")

                return {"similar_images": similar_images}

        elif action == "index" and len(images_to_search) > 0:
            print(
                f"Recived images of length: {len(images_to_search)} needs to retrieve top k  : {top_k} similar images as result"
            )
            print(f"Action indexing is executing for : {len(images_to_search)} images")
            # if the index does not already exist, we create it
            # check if the abstractive-question-answering index exists
            print("checking pinecone Index")
            if INDEX_NAME not in pinecone.list_indexes():
                # delete the current index and create the new index if it does not exist
                for delete_index in pinecone.list_indexes():
                    print(f"Deleting exitsing pinecone Index : {delete_index}")

                    pinecone.delete_index(delete_index)
                print(f"Creating new pinecone Index : {INDEX_NAME}")
                pinecone.create_index(INDEX_NAME, dimension=INDEX_DIMENSION, metric="cosine")
            # instantiate connection to your Pinecone index
            print(f"Connecting to pinecone Index : {INDEX_NAME}")
            index = pinecone.Index(INDEX_NAME)
            search_images = []
            meta_datas = []
            ids = []
            print("Processing request...")
            for image in images_to_search:
                if image.filename.split(".")[-1] not in ("jpg", "jpeg", "png", "JPG", "PNG", "JPEG"):
                    return "Image must be jpg or png format!"
                # read image contain
                contents = await image.read()
                pil_image = Image.open(io.BytesIO(contents))
                tmp_file = f"{TMP_DIR}/{image.filename}"
                pil_image.save(tmp_file)
                meta_datas.append({"file_path": tmp_file})
                search_images.append(pil_image)
                ids.append(str(uuid.uuid1()).replace("-",""))

            print("Encoding images to vectors...")
            # corpus_embeddings = image_sim_model.encode(
            #     search_images, convert_to_tensor=True, show_progress_bar=True
            # ).tolist()
            corpus_embeddings = fclip.encode_images(search_images, batch_size=32)[0]
            print(f"Indexing images to pinecone Index : {INDEX_NAME}")
            index.upsert(
                vectors=list(zip(ids, corpus_embeddings, meta_datas)), namespace=namespace
            )
            

            return {"similar_images": [], "status": "Indexing succesfull for uploaded files"}
        else:
            return {"similar_images": []}
    except Exception as e:
        e = sys.exc_info()[1]
        print(f"exception happened {e} {str(traceback.print_exc())}")
        raise HTTPException(status_code=500, detail=str(e))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Fast API exposing YOLOv5 model")
    parser.add_argument("--port", default=8000, type=int, help="port number")
    # parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s')
    opt = parser.parse_args()
    uvicorn.run(app, port=opt.port)