Update app.py

app.py

@@ -1,4 +1,4 @@
-import os, zipfile, tempfile, requests, io
+import os, zipfile, tempfile, io
 import numpy as np
 import pandas as pd
 from PIL import Image
@@ -8,10 +8,9 @@ from torchvision import transforms
 from torchvision.models import resnet50, ResNet50_Weights
 from sklearn.cluster import MiniBatchKMeans
 import matplotlib.pyplot as plt
-from datetime import datetime
 import gradio as gr
-from deepface import DeepFace
 import cv2
+import requests
 
 # ---------------------------
 # Device
@@ -61,22 +60,54 @@ def get_dominant_color(image,num_colors=5):
     return dominant_color, hex_color
 
 # ---------------------------
-# Gender normalization
+# OpenCV DNN Face + Gender
 # ---------------------------
-def normalize_gender(raw_gender):
-    if raw_gender is None:
-        return "Inconnu"
-    g = str(raw_gender).lower()
-    if g in ["man", "male", "m"]:
-        return "Homme"
-    elif g in ["woman", "female", "f"]:
-        return "Femme"
-    elif g in ["homme"]:
-        return "Homme"
-    elif g in ["femme"]:
-        return "Femme"
-    else:
-        return "Inconnu"
+os.makedirs("models", exist_ok=True)
+
+# Face detection model
+FACE_PROTO = "models/deploy.prototxt"
+FACE_MODEL = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel"
+if not os.path.exists(FACE_PROTO):
+    r = requests.get("https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt"); open(FACE_PROTO,"wb").write(r.content)
+if not os.path.exists(FACE_MODEL):
+    r = requests.get("https://raw.githubusercontent.com/opencv/opencv_3rdparty/master/res10_300x300_ssd_iter_140000_fp16.caffemodel"); open(FACE_MODEL,"wb").write(r.content)
+
+# Gender model
+GENDER_PROTO = "models/deploy_gender.prototxt"
+GENDER_MODEL = "models/gender_net.caffemodel"
+if not os.path.exists(GENDER_PROTO):
+    r = requests.get("https://raw.githubusercontent.com/spmallick/learnopencv/master/AgeGender/deploy_gender.prototxt"); open(GENDER_PROTO,"wb").write(r.content)
+if not os.path.exists(GENDER_MODEL):
+    r = requests.get("https://raw.githubusercontent.com/spmallick/learnopencv/master/AgeGender/gender_net.caffemodel"); open(GENDER_MODEL,"wb").write(r.content)
+
+face_net = cv2.dnn.readNet(FACE_MODEL, FACE_PROTO)
+gender_net = cv2.dnn.readNet(GENDER_MODEL, GENDER_PROTO)
+GENDER_LIST = ["Homme","Femme"]
+
+def detect_faces_and_gender(image):
+    img = np.array(image)[:, :, ::-1]  # PIL RGB -> BGR
+    h, w = img.shape[:2]
+    blob = cv2.dnn.blobFromImage(img, 1.0, (300,300), [104,117,123], swapRB=False)
+    face_net.setInput(blob)
+    detections = face_net.forward()
+    faces_data = []
+
+    for i in range(detections.shape[2]):
+        confidence = detections[0,0,i,2]
+        if confidence > 0.5:
+            box = detections[0,0,i,3:7] * np.array([w,h,w,h])
+            x1,y1,x2,y2 = box.astype(int)
+            x1,y1,x2,y2 = max(0,x1), max(0,y1), min(w,x2), min(h,y2)
+            face_img = img[y1:y2, x1:x2]
+            if face_img.size == 0:
+                continue
+            face_blob = cv2.dnn.blobFromImage(face_img, 1.0, (227,227),
+                                             [78.4263377603, 87.7689143744, 114.895847746], swapRB=False)
+            gender_net.setInput(face_blob)
+            gender_preds = gender_net.forward()
+            gender = GENDER_LIST[gender_preds[0].argmax()]
+            faces_data.append({"bbox":(x1,y1,x2,y2),"gender":gender})
+    return faces_data
 
 # ---------------------------
 # Core analysis
@@ -85,7 +116,6 @@ def classify_zip_and_analyze_color(zip_file):
     results = []
     images_list = []
     zip_name = os.path.splitext(os.path.basename(zip_file.name))[0]
-    date_str = datetime.now().strftime("%Y%m%d")
 
     with tempfile.TemporaryDirectory() as tmpdir:
         with zipfile.ZipFile(zip_file.name,'r') as zip_ref:
@@ -113,32 +143,9 @@ def classify_zip_and_analyze_color(zip_file):
             rgb, hex_color = get_dominant_color(image)
             basic_color = closest_basic_color(rgb)
 
-            # Face analysis
-            faces_data = []
-            try:
-                img_cv2 = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
-                detected_faces = DeepFace.analyze(
-                    img_cv2, actions=["age","gender","emotion"], enforce_detection=False
-                )
-
-                # Ensure we have a list
-                if isinstance(detected_faces, dict):
-                    detected_faces = [detected_faces]
-
-                for f in detected_faces:
-                    gender_fr = normalize_gender(f.get("gender") or f.get("dominant_gender"))
-                    faces_data.append({
-                        "age": f.get("age", -1),
-                        "gender": gender_fr,
-                        "emotion": f.get("dominant_emotion", "Unknown")
-                    })
-            except:
-                faces_data = []
-
-            faces_str = "; ".join([
-                f"Age: {face['age']}, Gender: {face['gender']}, Emotion: {face['emotion']}"
-                for face in faces_data
-            ])
+            # Face + gender detection
+            faces_data = detect_faces_and_gender(image)
+            faces_str = "; ".join([f"Gender: {f['gender']}" for f in faces_data])
 
             results.append((
                 fname,
@@ -149,22 +156,19 @@ def classify_zip_and_analyze_color(zip_file):
                 faces_str
             ))
 
-    # Create DataFrame
     df = pd.DataFrame(results, columns=["Filename","Top 3 Predictions","Confidence","Dominant Color","Basic Color","Face Info"])
-    out_xlsx = os.path.join(tempfile.gettempdir(), f"{zip_name}_{date_str}_results.xlsx")
+    out_xlsx = os.path.join(tempfile.gettempdir(), f"{zip_name}_results.xlsx")
     df.to_excel(out_xlsx,index=False)
 
     # ---------------------------
     # Plots
     # ---------------------------
-    # Color frequency
     fig1, ax1 = plt.subplots()
     color_counts = df["Basic Color"].value_counts()
     ax1.bar(color_counts.index, color_counts.values, color="skyblue")
     ax1.set_title("Basic Color Frequency"); ax1.set_ylabel("Count")
     buf1 = io.BytesIO(); plt.savefig(buf1, format="png"); plt.close(fig1); buf1.seek(0); plot1_img = Image.open(buf1)
 
-    # Top prediction distribution
     fig2, ax2 = plt.subplots()
     preds_flat = []
     for p in df["Top 3 Predictions"]: preds_flat.extend(p.split(", "))
@@ -173,26 +177,14 @@ def classify_zip_and_analyze_color(zip_file):
     ax2.set_title("Top Prediction Distribution"); ax2.set_xlabel("Count")
     buf2 = io.BytesIO(); plt.savefig(buf2, format="png", bbox_inches="tight"); plt.close(fig2); buf2.seek(0); plot2_img = Image.open(buf2)
 
-    # Gender and age
-    ages_male = [int(f.split(", ")[0].split(": ")[1]) for row in df["Face Info"] for f in row.split("; ") if "Homme" in f]
-    ages_female = [int(f.split(", ")[0].split(": ")[1]) for row in df["Face Info"] for f in row.split("; ") if "Femme" in f]
-
-    gender_counts = {"Homme": len(ages_male), "Femme": len(ages_female)}
-
     # Gender distribution
+    gender_counts = [df["Face Info"].str.count("Homme").sum(), df["Face Info"].str.count("Femme").sum()]
     fig3, ax3 = plt.subplots()
-    ax3.bar(gender_counts.keys(), gender_counts.values(), color=["lightblue","pink"])
+    ax3.bar(["Homme","Femme"], gender_counts, color=["lightblue","pink"])
     ax3.set_title("Gender Distribution"); ax3.set_ylabel("Count")
     buf3 = io.BytesIO(); plt.savefig(buf3, format="png"); plt.close(fig3); buf3.seek(0); plot3_img = Image.open(buf3)
 
-    # Age distribution
-    fig4, ax4 = plt.subplots()
-    bins = range(0,101,5)
-    ax4.hist([ages_male, ages_female], bins=bins, color=["lightblue","pink"], label=["Homme","Femme"], edgecolor="black")
-    ax4.set_title("Age Distribution by Gender"); ax4.set_xlabel("Age"); ax4.set_ylabel("Count"); ax4.legend()
-    buf4 = io.BytesIO(); plt.savefig(buf4, format="png"); plt.close(fig4); buf4.seek(0); plot4_img = Image.open(buf4)
-
-    return df, images_list, out_xlsx, plot1_img, plot2_img, plot3_img, plot4_img
+    return df, images_list, out_xlsx, plot1_img, plot2_img, plot3_img
 
 # ---------------------------
 # Gradio interface
@@ -208,12 +200,11 @@ with gr.Blocks() as demo:
     plot1 = gr.Image(label="Basic Color Frequency")
     plot2 = gr.Image(label="Top Prediction Distribution")
     plot3 = gr.Image(label="Gender Distribution")
-    plot4 = gr.Image(label="Age Distribution by Gender")
 
     analyze_btn.click(
         classify_zip_and_analyze_color,
         inputs=uploaded_zip,
-        outputs=[output_df, image_gallery, download_file, plot1, plot2, plot3, plot4]
+        outputs=[output_df, image_gallery, download_file, plot1, plot2, plot3]
     )
 
 demo.launch(server_name="0.0.0.0", server_port=7860)