feat: detecting humans

pyproject.toml

@@ -1,7 +1,10 @@
 [project]
 name = "secure_pixelation"
 version = "0.0.0"
-dependencies = []
+dependencies = [
+    "opencv_python~=4.11.0.86",
+    "imutils~=0.5.4",
+]
 authors = []
 description = "Hiding faces with Mosaic has proven incredibly unsafe especially with videos, because the algorythm isn't destructive. However, if you black out the selected area, repopulate it with generative ai, and then pixelate it, it should look authentic, but be 100% destructive, thus safe."
 readme = "README.md"

secure_pixelation/__main__.py

@@ -1,2 +1,7 @@
+from .detect_humans import detect_humans
+
+
 def cli():
     print(f"Running secure_pixelation")
+
+    detect_humans("assets/humans.png")

secure_pixelation/detect_humans.py

@@ -0,0 +1,101 @@
+from pathlib import Path
+import urllib.request
+from typing import Dict, List
+
+import cv2
+import imutils
+import numpy as np
+
+
+
+MODEL_PATH = Path("assets", "models")
+MODEL_DEPENDENCIES: Dict[str, List[str]] = {
+    "yolov3": [
+        "https://github.com/pjreddie/darknet/raw/refs/heads/master/cfg/yolov3.cfg",
+        "https://github.com/patrick013/Object-Detection---Yolov3/raw/refs/heads/master/model/yolov3.weights"
+    ]
+}
+
+
+def require_net(name: str):
+    if name not in MODEL_DEPENDENCIES:
+        print(f"model {name} not found")
+        exit(1)
+
+    print(f"preparing {name}")
+    MODEL_PATH.mkdir(exist_ok=True, parents=True)
+
+    for dep_url in MODEL_DEPENDENCIES[name]:
+        dep_path = MODEL_PATH / dep_url.split("/")[-1]
+        
+        if dep_path.exists():
+            continue
+        
+        print(f"downloading {dep_url}")
+        urllib.request.urlretrieve(
+            url=dep_url,
+            filename=str(dep_path)
+        )
+
+
+
+# print(f"\tfound human at {x}/{y} with the size of {w} x {h}")
+
+
+def detect_humans(to_detect: str):
+    _p = Path(to_detect)
+    detected = str(_p.with_name(_p.stem + ".detected" + _p.suffix))
+    print(f"detecting humans: {to_detect} => {detected}")
+
+    require_net("yolov3")
+
+    # Load YOLO
+    net = cv2.dnn.readNet(str(MODEL_PATH / 'yolov3.weights'), str(MODEL_PATH / 'yolov3.cfg'))
+    layer_names = net.getLayerNames()
+    indices = net.getUnconnectedOutLayers()
+    output_layers = [layer_names[int(i) - 1] for i in indices]
+
+
+    # Load image
+    image = cv2.imread(to_detect)
+    height, width, channels = image.shape
+
+    # Create blob and do forward pass
+    blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(output_layers)
+
+    boxes = []
+    confidences = []
+
+    # Information for each object detected
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.5 and class_id == 0:  # Class ID 0 is human
+                center_x = int(detection[0] * width)
+                center_y = int(detection[1] * height)
+                w = int(detection[2] * width)
+                h = int(detection[3] * height)
+                x = int(center_x - w / 2)
+                y = int(center_y - h / 2)
+
+                boxes.append([x, y, w, h])
+                confidences.append(float(confidence))
+
+    # Apply Non-Maximum Suppression
+    indices = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold=0.5, nms_threshold=0.4)
+
+    for i in indices:
+        i = i[0] if isinstance(i, (list, np.ndarray)) else i  # Flatten index if needed
+        x, y, w, h = boxes[i]
+
+        print(f"\tfound human at {x}/{y} with the size of {w} x {h}")
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 255), 2)
+
+
+    # Save the result
+    cv2.imwrite(detected, image)
+