app.py

# import gradio as gr
# import cv2
# import numpy as np
# import onnxruntime as ort

# # Load the ONNX model using onnxruntime
# onnx_model_path = "Model_IV.onnx"  # Update with your ONNX model path
# session = ort.InferenceSession(onnx_model_path)

# # Function to perform object detection with the ONNX model
# def detect_objects(frame, confidence_threshold=0.5):
#     # Convert the frame from BGR (OpenCV) to RGB
#     image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    
#     # Preprocessing: Resize and normalize the image
#     # Assuming YOLO model input is 640x640, update according to your model's input size
#     input_size = (640, 640)
#     image_resized = cv2.resize(image, input_size)
#     image_normalized = image_resized / 255.0  # Normalize to [0, 1]
#     image_input = np.transpose(image_normalized, (2, 0, 1))  # Change to CHW format
#     image_input = np.expand_dims(image_input, axis=0).astype(np.float32)  # Add batch dimension

#     # Perform inference
#     inputs = {session.get_inputs()[0].name: image_input}
#     outputs = session.run(None, inputs)
    
#     # # Assuming YOLO model outputs are in the form of [boxes, confidences, class_probs]
#     # boxes, confidences, class_probs = outputs

#     # # Post-processing: Filter boxes by confidence threshold
#     # detections = []
#     # for i, confidence in enumerate(confidences[0]):
#     #     if confidence >= confidence_threshold:
#     #         x1, y1, x2, y2 = boxes[0][i]
#     #         class_id = np.argmax(class_probs[0][i])  # Get class with highest probability
#     #         detections.append((x1, y1, x2, y2, confidence, class_id))
    
#     # # Draw bounding boxes and labels on the image
#     # for (x1, y1, x2, y2, confidence, class_id) in detections:
#     #     color = (0, 255, 0)  # Green color for bounding boxes
#     #     cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
#     #     label = f"Class {class_id}: {confidence:.2f}"
#     #     cv2.putText(image, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
#     # # Convert the image back to BGR for displaying in Gradio
#     # image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    
#     return outputs

# # Gradio interface to use the webcam for real-time object detection
# # Added a slider for the confidence threshold
# iface = gr.Interface(fn=detect_objects, 
#                      #inputs=[
#                          # gr.Video(sources="webcam", type="numpy"),  # Webcam input
#                          inputs = gr.Image(sources=["webcam"], type="numpy"),
#                          # gr.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Confidence Threshold")  # Confidence slider
#                      # ],  
#                      outputs="image")  # Show output image with bounding boxes

# iface.launch()
###
# import gradio as gr
# import cv2
# from huggingface_hub import hf_hub_download
# from gradio_webrtc import WebRTC
# from twilio.rest import Client
# import os
# from inference import YOLOv8

# model_file = hf_hub_download(
#     repo_id="aje6/ASL-Fingerspelling-Detection", filename="onnx/Model_IV.onnx"
# )

# model = YOLOv8(model_file)

# account_sid = os.environ.get("TWILIO_ACCOUNT_SID")
# auth_token = os.environ.get("TWILIO_AUTH_TOKEN")

# if account_sid and auth_token:
#     client = Client(account_sid, auth_token)

#     token = client.tokens.create()

#     rtc_configuration = {
#         "iceServers": token.ice_servers,
#         "iceTransportPolicy": "relay",
#     }
# else:
#     rtc_configuration = None


# def detection(image, conf_threshold=0.3):
#     image = cv2.resize(image, (model.input_width, model.input_height))
#     new_image = model.detect_objects(image, conf_threshold)
#     return cv2.resize(new_image, (500, 500))


# css = """.my-group {max-width: 600px !important; max-height: 600 !important;}
#                       .my-column {display: flex !important; justify-content: center !important; align-items: center !important};"""


# with gr.Blocks(css=css) as demo:
#     gr.HTML(
#         """
#     <h1 style='text-align: center'>
#     YOLOv10 Webcam Stream (Powered by WebRTC ⚡️)
#     </h1>
#     """
#     )
#     gr.HTML(
#         """
#         <h3 style='text-align: center'>
#         <a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a>
#         </h3>
#         """
#     )
#     with gr.Column(elem_classes=["my-column"]):
#         with gr.Group(elem_classes=["my-group"]):
#             image = WebRTC(label="Stream", rtc_configuration=rtc_configuration)
#             conf_threshold = gr.Slider(
#                 label="Confidence Threshold",
#                 minimum=0.0,
#                 maximum=1.0,
#                 step=0.05,
#                 value=0.30,
#             )

#         image.stream(
#             fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10
#         )

# if __name__ == "__main__":
#     demo.launch()

# import gradio as gr
# import numpy as np
# import cv2
# from ultralytics import YOLO

# model = YOLO('Model_IV.pt')

# def transform_cv2(frame, transform):
#     if transform == "cartoon":
#         # prepare color
#         img_color = cv2.pyrDown(cv2.pyrDown(frame))
#         for _ in range(6):
#             img_color = cv2.bilateralFilter(img_color, 9, 9, 7)
#         img_color = cv2.pyrUp(cv2.pyrUp(img_color))

#         # prepare edges
#         img_edges = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
#         img_edges = cv2.adaptiveThreshold(
#             cv2.medianBlur(img_edges, 7),
#             255,
#             cv2.ADAPTIVE_THRESH_MEAN_C,
#             cv2.THRESH_BINARY,
#             9,
#             2,
#         )
#         img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB)
#         # combine color and edges
#         img = cv2.bitwise_and(img_color, img_edges)
#         return img
#     elif transform == "edges":
#         # perform edge detection
#         img = cv2.cvtColor(cv2.Canny(frame, 100, 200), cv2.COLOR_GRAY2BGR)
#         return img
#     else:
#         return np.flipud(frame)

# with gr.Blocks() as demo:
#     with gr.Row():
#         with gr.Column():
#             transform = gr.Dropdown(choices=["cartoon", "edges", "flip"],
#                                     value="flip", label="Transformation")
#             input_img = gr.Image(sources=["webcam"], type="numpy")
#         with gr.Column():
#             output_img = gr.Image(streaming=True)
#         dep = input_img.stream(transform_cv2, [input_img, transform], [output_img],
#                                 time_limit=30, stream_every=0.1, concurrency_limit=30)

# if __name__ == "__main__":
#     demo.launch()

###

# import gradio as gr
# import torch
# import cv2

# # Load the YOLOv8 model
# model = torch.hub.load('ultralytics/yolov8', 'yolov8s', trust_repo=True)
# model.load_state_dict(torch.load('Model_IV'))

# def inference(img):
#     results = model(img)
#     annotated_img = results.render()[0]
#     return annotated_img

# iface = gr.Interface(fn=inference, inputs="webcam", outputs="image")
# iface.launch()

import gradio as gr
import torch
from PIL import Image
import torchvision.transforms as T
from ultralytics import YOLO
import onnxruntime as ort
import cv2
import numpy as np

# Load your model

# model = YOLO("Model_IV.pt")
# model = torch.load("Model_IV.pt")
# model.eval()
# checkpoint = torch.load("Model_IV.pt")
# model.load_state_dict(checkpoint)  # Load the saved weights
# model.eval()  # Set the model to evaluation mode

# Load the onnx model
model = ort.InferenceSession("Model_IV.onnx")

# Define preprocessing
# transform = T.Compose([
#     T.Resize((224, 224)),  # Adjust to your model's input size
#     T.ToTensor(),
# ])

def predict(image):
    # # Preprocess the image
    # img_tensor = transform(image).unsqueeze(0)  # Add batch dimension
    
    # # # Make prediction
    # # with torch.no_grad():
    # #     output = model(img_tensor)
    
    # # Process output (adjust based on your model's format)
    # results = model(image)
    # annotated_img = results[0].plot()
    # return annotated_img

    # Preprocess the image

    # Get name and shape of the model's inputs
    input_name = model.get_inputs()[0].name
    input_shape = model.get_inputs()[0].shape

    # Resize the image to the model's input shape
    image = cv2.resize(image, (input_shape[2], input_shape[3]))

    original_image_shape = image.shape
    print("Original image shape:", original_image_shape)

    # Reshape the image to match the model's input shape
    image = image.reshape(3, 640, 640)

    # Normalize output image using ImageNet-style normalization
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    mean = np.expand_dims(mean, axis=(1,2))
    std = np.expand_dims(std, axis=(1,2))
    image = (image / 255.0 - mean)/std
    
    # Convert the image to a numpy array and add a batch dimension
    if len(input_shape) == 4 and input_shape[0] == 1:
        image = np.expand_dims(image, axis=0)
    image = image.astype(np.float32)

    print("Input image shape:", image.shape)
    
    # Make prediction
    output = model.run(None, {input_name: image})

    # print("Output shape:", output.shape)
    
    # print("type output:", type(output))
    # print(output)

    # Postprocess output image
    
    annotated_img = output[0]

    
    
    # annotated_img = (output[0] / 255.0 - mean)/std
    # annotated_img = classes[output[0][0].argmax(0)]
    
    print("Annotated image type before normalization:", type(annotated_img))
    # print("Annotated image before normalization:", annotated_img)
    print("Min value of image before normalization:", np.min(annotated_img))
    print("Max value of image before normalization:", np.max(annotated_img))

    # # Normalize output image using ImageNet-style normalization (again)
    # annotated_img = (annotated_img / 255.0 - mean)/std

    # Normalize output image using Min-Max normalization
    min_val = np.min(annotated_img)
    max_val = np.max(annotated_img)
    annotated_img = (annotated_img - min_val) / (max_val - min_val)

    print("Min value of image after normalization:", np.min(annotated_img))
    print("Max value of image after normalization:", np.max(annotated_img))
    print("annotated_img type after normalization:", type(annotated_img))
    # print("annotated_img shape after normalization:", annotated_img.shape)

    # Reshape the image to match the PIL Image input shape
    print("annotated_img shape before reshape:", annotated_img.shape)
    annotated_img = annotated_img.reshape(original_image_shape)
    print("annotated_img shape after reshape:", annotated_img.shape)

    # Convert to PIL Image
    annotated_img = Image.fromarray(annotated_img)

    print("PIL Image type:", type(annotated_img))
    # print("PIL Image shape:", annotated_img.shape)

    return annotated_img
    
# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(sources=["webcam"], type="numpy"),  # Accepts image input
    # outputs="image"  # Customize based on your output format
    outputs=gr.Image(type="pil"),  # Accepts image input
)

if __name__ == "__main__":
    demo.launch()