diff --git a/README.md b/README.md index 5178ee42..658caa1f 100644 --- a/README.md +++ b/README.md @@ -1,32 +1,31 @@ -# oneAPI-GenAI-Hackathon-2023 - Hack2Skill +#### Team Name - TechnoTouch +#### Problem Statement - Revolutionary AI-Infused Retail Platform +#### Team Leader Email - laxmi17sarki@gmail.com -Welcome to the official repository for the oneAPI-GenAI-Hackathon-2023 organized by Hack2Skill! +### A Brief of the Prototype: Image_Se_Image_Search + Problem statement: The built models should be able to find database images that correspond to a given query image (i.e., the model should retrieve database images containing the same object as the query). -## Getting Started +Example: + + + + Screen 1 : Where user Uploads Image as a seach query and hit `Submit`. -To get started with the oneAPI-GenAI-Hackathon-2023 repository, follow these steps: + + + Screen 2 : Where response of similar images are presented. -### Submission Instruction: - 1. Fork this repository - 2. Create a folder with your Team Name - 3. Upload all the code and necessary files in the created folder - 4. Upload a **README.md** file in your folder with the below mentioned informations. - 5. Generate a Pull Request with your Team Name. (Example: submission-XYZ_team) - -### README.md must consist of the following information: - -#### Team Name - -#### Problem Statement - -#### Team Leader Email - - -### A Brief of the Prototype: - This section must include UML Diagrams and prototype description - -### Tech Stack: - List Down all technologies used to Build the prototype - -### Step-by-Step Code Execution Instructions: - This Section must contain a set of instructions required to clone and run the prototype so that it can be tested and deeply analyzed - -### Future Scope: - Write about the scalability and futuristic aspects of the prototype developed + +`Note` : images are displayed in their most to less similar images . +
+
+ Steps to Run the Project : + diff --git a/TechnoTouch/app.py b/TechnoTouch/app.py new file mode 100644 index 00000000..e0f0d4d9 --- /dev/null +++ b/TechnoTouch/app.py @@ -0,0 +1,47 @@ +import os +from PIL import Image +from flask import Flask, request, render_template +from datetime import datetime +from image_feature_extraction import FeatureExtractor +from pathlib import Path +import numpy as np + +app = Flask(__name__) + +# Read image features +fe = FeatureExtractor() +features = [] +img_paths = [] +for feature_path in Path("./static/flipkart_feature").glob("*.npy"): + features.append(np.load(feature_path)) + img_paths.append(Path("./static/flipkart_dataset") / (feature_path.stem + ".jpg")) +features = np.array(features) + + +@app.route('/', methods=['GET', 'POST']) +def index(): + if request.method == 'POST': + file = request.files['query_img'] + # Save query image + img = Image.open(file.stream) # PIL image + uploaded_img_path = "static/uploaded/" + datetime.now().isoformat().replace(":", ".") + "_" + file.filename + img.save(uploaded_img_path) + + # Run search + fe = FeatureExtractor() + query = fe.extract(img) + dists = np.linalg.norm(features - query, axis=1) # L2 distances to features + ids = np.argsort(dists)[:20] # Top 10 results + scores = [(dists[id], img_paths[id]) for id in ids] + return render_template('index.html', + query_path=uploaded_img_path, + context=uploaded_img_path, + scores=scores + ) + if request.method == 'GET': + return render_template('index.html') + + +if __name__ == "__main__": + port = int(os.environ.get('PORT', 5000)) + app.run(host="0.0.0.0", port=port, debug=False) \ No newline at end of file diff --git a/TechnoTouch/db_dataset_feature_preparation.py b/TechnoTouch/db_dataset_feature_preparation.py new file mode 100644 index 00000000..a61cb6e9 --- /dev/null +++ b/TechnoTouch/db_dataset_feature_preparation.py @@ -0,0 +1,13 @@ +from PIL import Image +from Image_feature_extraction import FeatureExtractor +from pathlib import Path +import numpy as np + +if __name__ == '__main__': + fe = FeatureExtractor() + + for img_path in sorted(Path("./static/dataset").glob("*.jpg")): + print(img_path) # e.g., ./static/img/xxx.jpg + feature = fe.extract(img=Image.open(img_path)) + feature_path = Path("./static/feature") / (img_path.stem + ".npy") # e.g., ./static/feature/xxx.npy + np.save(feature_path, feature) \ No newline at end of file diff --git a/TechnoTouch/image_feature_extraction.py b/TechnoTouch/image_feature_extraction.py new file mode 100644 index 00000000..0000acdd --- /dev/null +++ b/TechnoTouch/image_feature_extraction.py @@ -0,0 +1,27 @@ +# Import the libraries +from tensorflow.keras.preprocessing import image +from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input +from tensorflow.keras.models import Model +import numpy as np + + +class FeatureExtractor: + def __init__(self): + # Use VGG-16 as the architecture and ImageNet for the weight + base_model = VGG16(weights='imagenet') + # Customize the model to return features from fully-connected layer + self.model = Model(inputs=base_model.input, outputs=base_model.get_layer('fc1').output) + + def extract(self, img): + # Resize the image + img = img.resize((224, 224)) + # Convert the image color space + img = img.convert('RGB') + # Reformat the image + x = image.img_to_array(img) + x = np.expand_dims(x, axis=0) + x = preprocess_input(x) + # Extract Features + feature = self.model.predict(x)[0] + return feature / np.linalg.norm(feature) + diff --git a/TechnoTouch/requirements.txt b/TechnoTouch/requirements.txt new file mode 100644 index 00000000..733b3343 Binary files /dev/null and b/TechnoTouch/requirements.txt differ diff --git a/TechnoTouch/static/flipkart_dataset/-original-imagf255fpcfzfnt.jpeg b/TechnoTouch/static/flipkart_dataset/-original-imagf255fpcfzfnt.jpeg new file mode 100644 index 00000000..37a0d426 Binary files /dev/null and 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100644 index 00000000..50f611db --- /dev/null +++ b/TechnoTouch/templates/index.html @@ -0,0 +1,68 @@ + + + + + + + +
+
+ + +
+
+ +
+
+ + + + + +
+ {% for score in scores %} + +
+
+
+
{{ score[0] }}
+

Similarity Between query and DB match is considered.

+
+

JPG

+
Matching
+
+
+ +
+ +
+ {% endfor %} + + +
+
Show me more
+
+
+ +