Initial commit

Divers/tutoriel36/Detector.py

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+import numpy as np
+import cv2
+
+lo=np.array([80, 50, 50])
+hi=np.array([100, 255, 255])
+
+def detect_inrange(image, surface):
+    points=[]
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+    image=cv2.blur(image, (5, 5))
+    mask=cv2.inRange(image, lo, hi)
+    mask=cv2.erode(mask, None, iterations=2)
+    mask=cv2.dilate(mask, None, iterations=2)
+    elements=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
+    elements=sorted(elements, key=lambda x:cv2.contourArea(x), reverse=True)
+    for element in elements:
+        if cv2.contourArea(element)>surface:
+            ((x, y), rayon)=cv2.minEnclosingCircle(element)
+            points.append(np.array([int(x), int(y)]))
+        else:
+            break
+
+    return points, mask
+
+def detect_visage(image):
+    face_cascade=cv2.CascadeClassifier("./haarcascade_frontalface_alt2.xml")
+    points=[]
+    gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    face=face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=3)
+    for x, y, w, h in face:
+        points.append(np.array([int(x+w/2), int(y+h/2)]))
+
+    return points, None