latest updates

app.py

@@ -11,7 +11,9 @@ def classify_face_shape(image):
     pipe = pipeline("image-classification", model="metadome/face_shape_classification")
     
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("face_region.jpg") # use the face_region image instead
+
     # Log the output for debugging
     print("Pipeline output for shape:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -22,7 +24,8 @@ def classify_face_shape(image):
 def classify_age(image):
     pipe = pipeline("image-classification", model="nateraw/vit-age-classifier")
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("face_region.jpg") # use the face_region image instead
     
     print("Pipeline output for age:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -34,7 +37,8 @@ def classify_skin_type(image):
     pipe = pipeline("image-classification", model="dima806/skin_types_image_detection")
     
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("face_region.jpg") # use the face_region image instead
     
     print("Pipeline output for skin_type:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -46,7 +50,8 @@ def classify_acne_type(image):
     pipe = pipeline("image-classification", model="imfarzanansari/skintelligent-acne")
     
     # Run the pipeline on the uploaded image
-    output = pipe(image)
+    #output = pipe(image)
+    output = pipe("face_region.jpg") # use the face_region image instead
     
     print("Pipeline output for acne:", output)
     # Format the output to be compatible with gr.outputs.Label
@@ -88,11 +93,14 @@ def classify_eye_color(image):
     
     # Run the pipeline on the uploaded image
     #output = pipe(image)
+    print("WEARE USING THIS CODE TO GET THE RESULT FOR EYE COLOR!!!!!!")
     output = pipe("eye_regions.jpg") #use the eye_regions image instead
     
     print("Pipeline output for eye color:", output)
     # Format the output to be compatible with gr.outputs.Label
     formatted_output = {item['label']: item['score'] for item in output}
+
+    print("THIS IS FORMATTED OUTPUT!" + str(formatted_output))
     
     return formatted_output
     
@@ -103,21 +111,14 @@ def process_gradio_image(pil_image):
     return image
 
 def classify_race(image):
-    '''
-    "0": "East Asian",
-    "1": "Indian",
-    "2": "Black",
-    "3": "White",
-    "4": "Middle Eastern",
-    "5": "Latino_Hispanic",
-    "6": "Southeast Asian"
-    '''
-    pipe = pipeline("image-classification", model="crangana/trained-race")
+    pipe = pipeline("image-classification", model="cledoux42/Ethnicity_Test_v003")
     # Run the pipeline on the uploaded image
     output = pipe("face_region.jpg")
 
     # Format the output to be compatible with gr.outputs.Label
     formatted_output = {item['label']: item['score'] for item in output}
+
+    print(formatted_output)
     
     return formatted_output
 
@@ -128,6 +129,12 @@ def classify_gender(image):
     formatted_output = {item['label']: item['score'] for item in output}
     return formatted_output
 
+def classify_wrinkles(image):
+    pipe = pipeline("image-classification", model="imfarzanansari/skintelligent-wrinkles")
+    output = pipe("face_region.jpg")
+    # Format the output to be compatible with gr.outputs.Label
+    formatted_output = {item['label']: item['score'] for item in output}
+    return formatted_output
 
 def classify_image_with_multiple_models(image):
     create_eye_region(image)
@@ -140,8 +147,9 @@ def classify_image_with_multiple_models(image):
     eye_color = classify_eye_color(image)
     race = classify_race(image)
     gender = classify_gender(image)
+    wrinkles = classify_wrinkles(image)
 
-    return face_shape_result, age_result, skin_type_result, acne_results, hair_color_results, eye_shape, eye_color, race, gender, Image.open("segmented_face.jpg")
+    return face_shape_result, age_result, skin_type_result, acne_results, hair_color_results, eye_shape, eye_color, race, gender, wrinkles, Image.open("segmented_face.jpg")
 
 def create_eye_region(image):
     # Load the pre-trained face detector
@@ -171,12 +179,14 @@ def create_eye_region(image):
         eyes = eye_cascade.detectMultiScale(roi_gray, scaleFactor=1.1, minNeighbors=10, minSize=(20, 20))
 
         eye_positions = []
-        for (ex, ey, ew, eh) in eyes:
+        #for (ex, ey, ew, eh) in eyes:
+        for (ex, ey, ew, eh) in eyes[:1]:
             # Ensure eyes are within the upper half of the face region
             if ey + eh < h // 2:
                 eye_positions.append((ex, ey, ew, eh))
 
-        for (ex, ey, ew, eh) in eyes:
+        #for (ex, ey, ew, eh) in eyes:
+        for (ex, ey, ew, eh) in eyes[:1]:
             # Draw a rectangle around the eyes
             cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
 
@@ -187,6 +197,10 @@ def create_eye_region(image):
             # Calculate the average color of the eye region
             avg_color = np.mean(eye_roi, axis=(0, 1))
 
+            print("Average color:", avg_color)
+            #color = "NULL"
+
+            color = classify_eye_color_opencv(avg_color)
             # Classify eye color based on average color
             #if avg_color[0] > avg_color[1] and avg_color[0] > avg_color[2]:
             #    color = "Brown"
@@ -196,11 +210,63 @@ def create_eye_region(image):
             #    color = "Blue"
 
             # Display the eye color
-            #cv2.putText(image, color, (ex, ey - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+            cv2.putText(image, color, (ex, ey - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
 
             cv2.imwrite('segmented_face.jpg', image)
 
 
+
+def classify_eye_color_opencv(avg_color):
+    """
+    Classify eye color based on average BGR values from cv2 image.
+    
+    Args:
+        avg_color: numpy array containing [B, G, R] values
+        
+    Returns:
+        str: classified eye color
+    """
+    # Convert BGR to RGB (since OpenCV uses BGR)
+
+    #avg_color = np.mean(avg_color, axis=(0, 1))
+    b, g, r = avg_color
+    
+    # Define color ranges for each eye color (in BGR)
+    # These thresholds may need adjustment based on your specific lighting conditions
+    
+    # Check brown eyes (darker, red-dominant)
+    if r > g and r > b and r > 100:
+        if g < 90 and b < 90:
+            return "brown"
+            
+    # Check amber eyes (golden-brown)
+    if r > 150 and g > 100 and b < 100:
+        if r > g > b:
+            return "amber"
+            
+    # Check hazel eyes (mix of brown and green)
+    if g > 100 and r > 100 and b < 100:
+        if abs(r - g) < 40:
+            return "hazel"
+            
+    # Check green eyes (green-dominant)
+    if g > r and g > b:
+        if g > 100:
+            return "green"
+            
+    # Check blue eyes (blue-dominant)
+    if b > r and b > g:
+        if b > 100:
+            return "blue"
+            
+    # Check gray eyes (all values similar)
+    if abs(r - g) < 20 and abs(g - b) < 20 and abs(r - b) < 20:
+        if r > 100 and g > 100 and b > 100:
+            return "gray"
+    
+    return "undefined"
+
+
 # Create the Gradio interface
 demo = gr.Interface(
     fn=classify_image_with_multiple_models,  # The function to run
@@ -215,6 +281,7 @@ demo = gr.Interface(
         gr.Label(num_top_classes=5, label="Eye Color"),
         gr.Label(num_top_classes=7, label="Race"),
         gr.Label(num_top_classes=2, label="Gender"),
+        gr.Label(num_top_classes=2, label="Wrinkles"),
         gr.Image(type="pil", label="Segmented Face", value="segmented_face.jpg")  # Provide the path to the image
     ],
     title="Multiple Model Classification",