Update app.py

app.py

@@ -1,3 +1,131 @@
+# 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()
+
+
+
+
 import torch
 import numpy as np
 import cv2
@@ -9,6 +137,10 @@ from detectron2.detectron2.config import get_cfg
 from detectron2 import model_zoo
 import torch_utils
 import dnnlib
+import requests
+import base64
+from io import BytesIO
+
 # Create output directory if it doesn't exist
 output_dir = "key/"
 os.makedirs(output_dir, exist_ok=True)
@@ -23,6 +155,9 @@ cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 # Load the predictor
 predictor = DefaultPredictor(cfg)
 
+# Replace with your actual Ngrok or backend URL
+NGROK_URL = " https://9dbc-210-212-162-140.ngrok-free.app/upload"  # Change this
+
 def process_image(image, user_height_cm):
     # Convert Gradio image input to OpenCV format
     image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
@@ -112,6 +247,33 @@ def process_image(image, user_height_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)
+    pil_image.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+    # Send POST request
+    response = requests.post(
+        "https://your-ngrok-url.ngrok.io/measurements",  # Change this
+        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}"
+
 # Gradio Interface
 demo = gr.Interface(
     fn=process_image,
@@ -121,4 +283,14 @@ demo = gr.Interface(
     description="Upload an image, enter your height, and get body measurements based on keypoints.",
 )
 
+# Add Save to Database button functionality
+save_btn = gr.Button("Save to Database")
+save_status = gr.Textbox(label="Save Status", interactive=False)
+
+save_btn.click(
+    fn=save_to_database,
+    inputs=[demo.outputs[0], demo.outputs[1], gr.Number(label="User Height (cm)")],
+    outputs=save_status
+)
+
 demo.launch()