Update app.py

app.py

@@ -1,514 +1,3 @@
-# import torch
-# import numpy as np
-# import cv2
-# import json
-# import os
-# import gradio as gr
-# from detectron2.detectron2.engine import DefaultPredictor
-# from detectron2.detectron2.config import get_cfg
-# from detectron2 import model_zoo
-# import torch_utils
-# import dnnlib
-# # Create output directory if it doesn't exist
-# output_dir = "key/"
-# os.makedirs(output_dir, exist_ok=True)
-# output_file = os.path.join(output_dir, "keypoints.json")
-
-# # Load pre-trained Keypoint R-CNN model
-# cfg = get_cfg()
-# cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
-# cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
-# cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# # Load the predictor
-# predictor = DefaultPredictor(cfg)
-
-# def process_image(image, user_height_cm):
-#     # Convert Gradio image input to OpenCV format
-#     image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-
-#     # Run keypoint detection
-#     outputs = predictor(image)
-
-#     # Extract keypoints
-#     instances = outputs["instances"]
-#     keypoints = instances.pred_keypoints.cpu().numpy().tolist() if instances.has("pred_keypoints") else None
-
-#     if not keypoints:
-#         return "No keypoints detected.", None
-
-#     # Save keypoints to JSON
-#     with open(output_file, "w") as f:
-#         json.dump({"keypoints": keypoints}, f, indent=4)
-
-#     keypoints = np.array(keypoints[0])[:, :2]  # Extract (x, y) coordinates
-
-#     # COCO format indices
-#     NOSE, L_SHOULDER, R_SHOULDER = 0, 5, 6
-#     L_ELBOW, R_ELBOW = 7, 8
-#     L_WRIST, R_WRIST = 9, 10
-#     L_HIP, R_HIP = 11, 12
-#     L_ANKLE, R_ANKLE = 15, 16
-
-#     # Define Keypoint Pairs for Drawing Lines (COCO Format)
-#     skeleton = [(5, 6), (5, 11), (6, 12), (11, 12)]
-
-#     # Draw Keypoints
-#     for x, y in keypoints:
-#         cv2.circle(image, (int(x), int(y)), 5, (0, 255, 0), -1)
-
-#     # Draw Skeleton
-#     for pt1, pt2 in skeleton:
-#         x1, y1 = map(int, keypoints[pt1])
-#         x2, y2 = map(int, keypoints[pt2])
-#         cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-
-#     # Function to calculate Euclidean distance
-#     def get_distance(p1, p2):
-#         return np.linalg.norm(np.array(p1) - np.array(p2))
-
-#     # Calculate full height (consider head length)
-#     ankle_mid = ((keypoints[L_ANKLE] + keypoints[R_ANKLE]) / 2).tolist()
-#     pixel_height = get_distance(keypoints[NOSE], ankle_mid)
-
-#     # Estimated full body height (add approx head length)
-#     estimated_full_pixel_height = pixel_height / 0.87  # Since 87% = nose to ankle
-#     pixels_per_cm = estimated_full_pixel_height / user_height_cm
-
-#     # Waist and shoulder measurements
-#     shoulder_width_px = get_distance(keypoints[L_SHOULDER], keypoints[R_SHOULDER])
-#     waist_width_px = get_distance(keypoints[L_HIP], keypoints[R_HIP])
-
-#     # Convert to cm
-#     shoulder_width_cm = shoulder_width_px / pixels_per_cm
-#     waist_width_cm = waist_width_px / pixels_per_cm
-
-#     # Torso Length (Neck to Pelvis)
-#     pelvis = ((keypoints[L_HIP] + keypoints[R_HIP]) / 2).tolist()
-#     neck = ((keypoints[L_SHOULDER] + keypoints[R_SHOULDER]) / 2).tolist()
-#     torso_length_px = get_distance(neck, pelvis)
-#     torso_length_cm = torso_length_px / pixels_per_cm
-
-#     # Arm Length (Shoulder to Wrist)
-#     arm_length_px = get_distance(keypoints[L_SHOULDER], keypoints[L_WRIST])
-#     arm_length_cm = arm_length_px / pixels_per_cm
-
-#     # Calculate waist and hip circumference (Ellipse approximation)
-#     # Waist circumference ≈ π × (waist_width / 2) × 2
-#     waist_circumference = np.pi * waist_width_cm
-#     hip_circumference = waist_circumference / 0.75  # Assuming hip is slightly bigger than waist
-
-#     # Improved body measurement calculation
-#     def calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm):
-#         return {
-#             "Waist Circumference (cm)": round(waist_circumference, 2),
-#             "Hip Circumference (cm)": round(hip_circumference, 2),
-#             "Shoulder Width (cm)": round(shoulder_width_cm, 2),
-#             "Torso Length (Neck to Pelvis, cm)": round(torso_length_cm, 2),
-#             "Full Arm Length (Shoulder to Wrist, cm)": round(arm_length_cm, 2),
-#         }
-
-#     measurements = calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm)
-
-#     return measurements, cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-# # Gradio Interface
-# demo = gr.Interface(
-#     fn=process_image,
-#     inputs=[gr.Image(type="pil"), gr.Number(label="User Height (cm)")],
-#     outputs=[gr.JSON(label="Measurements"), gr.Image(type="pil", label="Keypoint Overlay")],
-#     title="Keypoint Measurement Extractor",
-#     description="Upload an image, enter your height, and get body measurements based on keypoints.",
-# )
-
-# demo.launch()
-
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-# from PIL import Image  # Importing Image from PIL (Pillow)
-# import json
-# import base64
-# import requests
-# from io import BytesIO
-# import torch
-# import numpy as np
-# import cv2
-# import json
-# import os
-# import gradio as gr
-# from detectron2.detectron2.engine import DefaultPredictor
-# from detectron2.detectron2.config import get_cfg
-# from detectron2 import model_zoo
-# import torch_utils
-# import dnnlib
-# # Create output directory if it doesn't exist
-# output_dir = "key/"
-# os.makedirs(output_dir, exist_ok=True)
-# output_file = os.path.join(output_dir, "keypoints.json")
-
-
-
-# # Load pre-trained Keypoint R-CNN model
-# cfg = get_cfg()
-# cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
-# cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
-# cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-# # Load the predictor
-# predictor = DefaultPredictor(cfg)
-
-# def process_image(image, user_height_cm):
-#     # Convert Gradio image input to OpenCV format
-#     image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-
-#     # Run keypoint detection
-#     outputs = predictor(image)
-
-#     # Extract keypoints
-#     instances = outputs["instances"]
-#     keypoints = instances.pred_keypoints.cpu().numpy().tolist() if instances.has("pred_keypoints") else None
-
-#     if not keypoints:
-#         return "No keypoints detected.", None
-
-#     # Save keypoints to JSON
-#     with open(output_file, "w") as f:
-#         json.dump({"keypoints": keypoints}, f, indent=4)
-
-#     keypoints = np.array(keypoints[0])[:, :2]  # Extract (x, y) coordinates
-
-#     # COCO format indices
-#     NOSE, L_SHOULDER, R_SHOULDER = 0, 5, 6
-#     L_ELBOW, R_ELBOW = 7, 8
-#     L_WRIST, R_WRIST = 9, 10
-#     L_HIP, R_HIP = 11, 12
-#     L_ANKLE, R_ANKLE = 15, 16
-
-#     # Define Keypoint Pairs for Drawing Lines (COCO Format)
-#     skeleton = [(5, 6), (5, 11), (6, 12), (11, 12)]
-
-#     # Draw Keypoints
-#     for x, y in keypoints:
-#         cv2.circle(image, (int(x), int(y)), 5, (0, 255, 0), -1)
-
-#     # Draw Skeleton
-#     for pt1, pt2 in skeleton:
-#         x1, y1 = map(int, keypoints[pt1])
-#         x2, y2 = map(int, keypoints[pt2])
-#         cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-
-#     # Function to calculate Euclidean distance
-#     def get_distance(p1, p2):
-#         return np.linalg.norm(np.array(p1) - np.array(p2))
-
-#     # Calculate full height (consider head length)
-#     ankle_mid = ((keypoints[L_ANKLE] + keypoints[R_ANKLE]) / 2).tolist()
-#     pixel_height = get_distance(keypoints[NOSE], ankle_mid)
-
-#     # Estimated full body height (add approx head length)
-#     estimated_full_pixel_height = pixel_height / 0.87  # Since 87% = nose to ankle
-#     pixels_per_cm = estimated_full_pixel_height / user_height_cm
-
-#     # Waist and shoulder measurements
-#     shoulder_width_px = get_distance(keypoints[L_SHOULDER], keypoints[R_SHOULDER])
-#     waist_width_px = get_distance(keypoints[L_HIP], keypoints[R_HIP])
-
-#     # Convert to cm
-#     shoulder_width_cm = shoulder_width_px / pixels_per_cm
-#     waist_width_cm = waist_width_px / pixels_per_cm
-
-#     # Torso Length (Neck to Pelvis)
-#     pelvis = ((keypoints[L_HIP] + keypoints[R_HIP]) / 2).tolist()
-#     neck = ((keypoints[L_SHOULDER] + keypoints[R_SHOULDER]) / 2).tolist()
-#     torso_length_px = get_distance(neck, pelvis)
-#     torso_length_cm = torso_length_px / pixels_per_cm
-
-#     # Arm Length (Shoulder to Wrist)
-#     arm_length_px = get_distance(keypoints[L_SHOULDER], keypoints[L_WRIST])
-#     arm_length_cm = arm_length_px / pixels_per_cm
-
-#     # Calculate waist and hip circumference (Ellipse approximation)
-#     # Waist circumference ≈ π × (waist_width / 2) × 2
-#     waist_circumference = np.pi * waist_width_cm
-#     hip_circumference = waist_circumference / 0.75  # Assuming hip is slightly bigger than waist
-
-#     # Improved body measurement calculation
-#     def calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm):
-#         return {
-#             "Waist Circumference (cm)": round(waist_circumference, 2),
-#             "Hip Circumference (cm)": round(hip_circumference, 2),
-#             "Shoulder Width (cm)": round(shoulder_width_cm, 2),
-#             "Torso Length (Neck to Pelvis, cm)": round(torso_length_cm, 2),
-#             "Full Arm Length (Shoulder to Wrist, cm)": round(arm_length_cm, 2),
-#         }
-
-#     measurements = calculate_body_measurements(waist_circumference, hip_circumference, shoulder_width_cm, torso_length_cm, arm_length_cm)
-
-#     return measurements, cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-   
-
-# # Save to database function
-# def save_to_database(measurements, image, user_height_cm):
-#     if measurements is None or image is None:
-#         return "No data to save."
-
-#     # Convert image to base64
-#     buffered = BytesIO()
-#     pil_image = Image.fromarray(image)  # Convert to PIL Image from NumPy array
-#     pil_image.save(buffered, format="JPEG")
-#     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-#     # Send POST request to save measurements and image to the database
-#     response = requests.post(
-#         "https://9dbc-210-212-162-140.ngrok-free.app/upload",  # Replace with your actual URL
-#         json={
-#             "imageBase64": img_str,
-#             "heightCm": user_height_cm,
-#             "waistCircumferenceCm": measurements["Waist Circumference (cm)"],
-#             "shoulderwidth": measurements["Shoulder Width (cm)"],
-#             "hipcircumference": measurements["Hip Circumference (cm)"],
-#             "torsolength": measurements["Torso Length (Neck to Pelvis, cm)"],
-#             "fullarmlength": measurements["Full Arm Length (Shoulder to Wrist, cm)"]
-#         }
-#     )
-
-#     return f"Status: {response.status_code}, Message: {response.text}"
-
-# # Example usage (integrating with your Gradio app)
-# import gradio as gr
-
-# # Gradio interface setup
-# def process_and_save(image, user_height_cm):
-#     measurements, processed_image = process_image(image, user_height_cm)
-#     if measurements:
-#         save_message = save_to_database(measurements, processed_image, user_height_cm)
-#         return processed_image, measurements, save_message
-#     else:
-#         return None, None, "Error in processing image."
-
-# # Gradio interface setup
-# iface = gr.Interface(
-#     fn=process_and_save,
-#     inputs=[gr.Image(type="numpy"), gr.Number(label="User Height (cm)")],
-#     outputs=[gr.Image(type="numpy"), gr.JSON(), gr.Textbox()],
-#     live=True
-# )
-
-# iface.launch(share=True)
-
-
-
-# import gradio as gr
-# import json
-# import base64
-# import requests
-# from io import BytesIO
-# import torch
-# import numpy as np
-# import cv2
-# import os
-# from PIL import Image
-# from detectron2.engine import DefaultPredictor
-# from detectron2.config import get_cfg
-# from detectron2 import model_zoo
-
-# # === Set up Detectron2 model ===
-# cfg = get_cfg()
-# cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
-# cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
-# cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-# predictor = DefaultPredictor(cfg)
-
-# # === Utility ===
-# def get_distance(p1, p2):
-#     return np.linalg.norm(np.array(p1) - np.array(p2))
-
-# # === Keypoint and Measurement Logic ===
-# def process_image(image, user_height_cm):
-#     image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-#     outputs = predictor(image_cv)
-#     instances = outputs["instances"]
-#     keypoints = instances.pred_keypoints.cpu().numpy().tolist() if instances.has("pred_keypoints") else None
-
-#     if not keypoints:
-#         return "No keypoints detected.", None, None
-
-#     keypoints = np.array(keypoints[0])[:, :2]
-
-#     # Draw keypoints and skeleton
-#     skeleton = [(5, 6), (5, 11), (6, 12), (11, 12)]
-#     for x, y in keypoints:
-#         cv2.circle(image_cv, (int(x), int(y)), 5, (0, 255, 0), -1)
-#     for pt1, pt2 in skeleton:
-#         x1, y1 = map(int, keypoints[pt1])
-#         x2, y2 = map(int, keypoints[pt2])
-#         cv2.line(image_cv, (x1, y1), (x2, y2), (255, 0, 0), 2)
-
-#     # Body part indices
-#     NOSE, L_SHOULDER, R_SHOULDER = 0, 5, 6
-#     L_WRIST, L_HIP, R_HIP, L_ANKLE, R_ANKLE = 9, 11, 12, 15, 16
-
-#     ankle_mid = ((keypoints[L_ANKLE] + keypoints[R_ANKLE]) / 2).tolist()
-#     pixel_height = get_distance(keypoints[NOSE], ankle_mid)
-#     estimated_full_pixel_height = pixel_height / 0.87
-#     pixels_per_cm = estimated_full_pixel_height / user_height_cm
-
-#     shoulder_width_cm = get_distance(keypoints[L_SHOULDER], keypoints[R_SHOULDER]) / pixels_per_cm
-#     waist_width_cm = get_distance(keypoints[L_HIP], keypoints[R_HIP]) / pixels_per_cm
-#     pelvis = ((keypoints[L_HIP] + keypoints[R_HIP]) / 2).tolist()
-#     neck = ((keypoints[L_SHOULDER] + keypoints[R_SHOULDER]) / 2).tolist()
-#     torso_length_cm = get_distance(neck, pelvis) / pixels_per_cm
-#     arm_length_cm = get_distance(keypoints[L_SHOULDER], keypoints[L_WRIST]) / pixels_per_cm
-
-#     waist_circumference = np.pi * waist_width_cm
-#     hip_circumference = waist_circumference / 0.75
-
-#     measurements = {
-#         "Waist Circumference (cm)": round(waist_circumference, 2),
-#         "Hip Circumference (cm)": round(hip_circumference, 2),
-#         "Shoulder Width (cm)": round(shoulder_width_cm, 2),
-#         "Torso Length (Neck to Pelvis, cm)": round(torso_length_cm, 2),
-#         "Full Arm Length (Shoulder to Wrist, cm)": round(arm_length_cm, 2),
-#     }
-
-#     return measurements, cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB), keypoints.tolist()
-
-# # === Save to DB ===
-# # def save_to_database(measurements, image, user_height_cm, user_id):
-# #     if not user_id:
-# #         return "❌ user_id missing from URL."
-
-# #     if measurements is None or image is None:
-# #         return "⚠️ No data to save."
-
-# #     buffered = BytesIO()
-# #     pil_image = Image.fromarray(image)
-# #     pil_image.save(buffered, format="JPEG")
-# #     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-# #     payload = {
-# #         "imageBase64": img_str,
-# #         "heightCm": user_height_cm,
-# #         "measurements": measurements
-# #     }
-
-#     # try:
-#     #     response = requests.post(
-#     #         f"https://9d68-210-212-162-140.ngrok-free.app/upload/{user_id}",
-#     #         json=payload
-#     #     )
-#     #     if response.status_code == 201:
-#     #         return "✅ Measurements and image saved to database!"
-#     #     else:
-#     #         return f"❌ Failed: {response.status_code} - {response.text}"
-#     # except Exception as e:
-#     #     return f"⚠️ Error during save: {str(e)}"
-
-
-
-# def save_to_database(measurements, image, user_height_cm, user_id):
-#     if not user_id:
-#         return "❌ user_id missing from URL."
-#     if measurements is None or image is None:
-#         return "⚠️ No data to save."
-
-#     buffered = BytesIO()
-#     pil_image = Image.fromarray(image)
-#     pil_image.save(buffered, format="JPEG")
-#     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-
-#     payload = {
-#         "imageBase64": img_str,
-#         "heightCm": user_height_cm,
-#         "waistCircumferenceCm": measurements.get("Waist Circumference (cm)"),
-#         "hipcircumference": measurements.get("Hip Circumference (cm)"),
-#         "shoulderwidth": measurements.get("Shoulder Width (cm)"),
-#         "torsolength": measurements.get("Torso Length (Neck to Pelvis, cm)"),
-#         "fullarmlength": measurements.get("Full Arm Length (Shoulder to Wrist, cm)"),
-#     }
-
-#     try:
-#         response = requests.post(
-#             f"https://7da2-2409-4042-6e81-1806-de6-b8e5-836c-6b95.ngrok-free.app/upload/{user_id}",
-#             json=payload
-#         )
-#         if response.status_code == 201:
-#             return "✅ Measurements and image saved to database!"
-#         else:
-#             return f"❌ Failed: {response.status_code} - {response.text}"
-#     except Exception as e:
-#         return f"⚠️ Error during save: {str(e)}"
-
-
-# # === Gradio App ===
-# with gr.Blocks() as demo:
-#     gr.Markdown("# 📏 AI-Powered Body Measurement Tool")
-#     user_id_state = gr.State()
-
-#     @demo.load(inputs=None, outputs=[user_id_state])
-#     def load_user_id(request: gr.Request):
-#         return request.query_params.get("user_id", "")
-
-#     with gr.Row():
-#         with gr.Column():
-#             image_input = gr.Image(label="Upload Your Full Body Image", type="pil")
-#             height_input = gr.Number(label="Your Real Height (in cm)")
-#             process_button = gr.Button("📐 Extract Measurements")
-
-#         with gr.Column():
-#             output_image = gr.Image(label="Detected Keypoints")
-#             measurement_output = gr.JSON(label="Body Measurements")
-
-#     with gr.Row():
-#         save_button = gr.Button("💾 Save to Backend")
-#         save_status = gr.Textbox(label="Status", interactive=False)
-
-#     # Store results for save
-#     processed_img = gr.State()
-#     processed_data = gr.State()
-
-#     process_button.click(
-#         fn=process_image,
-#         inputs=[image_input, height_input],
-#         outputs=[measurement_output, output_image, processed_data]
-#     ).then(
-#         fn=lambda img: img,
-#         inputs=[output_image],
-#         outputs=processed_img
-#     )
-
-#     save_button.click(
-#         fn=save_to_database,
-#         inputs=[measurement_output, processed_img, height_input, user_id_state],
-#         outputs=save_status
-#     )
-
-# if __name__ == "__main__":
-#     demo.launch()
-
 import torch
 import numpy as np
 import cv2
@@ -539,7 +28,7 @@ def process_image(image, user_height_cm):
     keypoints = instances.pred_keypoints.cpu().numpy().tolist() if instances.has("pred_keypoints") else None
 
     if not keypoints:
-        return "No keypoints detected.", None
+        return "No keypoints detected."
 
     with open(output_file, "w") as f:
         json.dump({"keypoints": keypoints}, f, indent=4)
@@ -553,15 +42,6 @@ def process_image(image, user_height_cm):
     L_KNEE, R_KNEE = 13, 14
     L_ANKLE, R_ANKLE = 15, 16
 
-    skeleton = [(5, 6), (5, 11), (6, 12), (11, 12)]
-
-    for x, y in keypoints:
-        cv2.circle(image, (int(x), int(y)), 5, (0, 255, 0), -1)
-    for pt1, pt2 in skeleton:
-        x1, y1 = map(int, keypoints[pt1])
-        x2, y2 = map(int, keypoints[pt2])
-        cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-
     def get_distance(p1, p2):
         return np.linalg.norm(np.array(p1) - np.array(p2))
 
@@ -594,21 +74,26 @@ def process_image(image, user_height_cm):
         "Neck to Knee Length (cm)": round(neck_to_knee_cm, 2)
     }
 
-    return measurements, cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return measurements
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("## 🧍 Keypoint-Based Body Measurement Tool")
-    gr.Markdown("Upload a **full-body image** and enter your **height (in cm)** to estimate body measurements using AI-powered keypoint detection.")
+    gr.Markdown("## 🧍 AI-Powered Body Measurement Estimator")
+    gr.Markdown("Upload a clear **full-body image** and enter your **height (in cm)** to estimate key body dimensions using computer vision.")
 
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="pil", label="📸 Upload Image")
-            submit_btn = gr.Button("🔍 Generate Measurements")
-        with gr.Column():
             height_input = gr.Number(label="📏 Your Height (cm)", value=170)
+            submit_btn = gr.Button("🔍 Estimate Measurements")
+
+        with gr.Column():
             measurement_output = gr.JSON(label="📐 Estimated Measurements")
 
-    submit_btn.click(fn=process_image, inputs=[image_input, height_input], outputs=[measurement_output])
+    submit_btn.click(
+        fn=process_image,
+        inputs=[image_input, height_input],
+        outputs=[measurement_output]
+    )
 
 demo.launch()