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252 changes: 126 additions & 126 deletions sekilrenk4.py
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Original file line number Diff line number Diff line change
@@ -1,167 +1,167 @@
import cv2
import numpy as np

def dynamic_color_threshold(frame, target_color_lower, target_color_upper):
"""Apply dynamic color threshold based on the target color."""
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv_frame, target_color_lower, target_color_upper)
return mask

def filter_contours(contours, min_area):
"""Filter contours based on minimum area."""
filtered_contours = [contour for contour in contours if cv2.contourArea(contour) > min_area]
return filtered_contours

def detect_color(hsv_color):
"""Determine color based on HSV values."""
# Define color ranges in HSV
blue_range = (np.array([100, 80, 50]), np.array([140, 255, 255]))
red_range = (np.array([0, 100, 100]), np.array([10, 255, 255]))
yellow_range = (np.array([20, 100, 100]), np.array([30, 255, 255]))
green_range = (np.array([40, 100, 100]), np.array([80, 255, 255]))
orange_range = (np.array([10, 100, 100]), np.array([20, 255, 255]))
purple_range = (np.array([120, 100, 100]), np.array([160, 255, 255]))
pink_range = (np.array([160, 100, 100]), np.array([180, 255, 255]))

if blue_range[0][0] <= hsv_color[0] <= blue_range[1][0] and \
blue_range[0][1] <= hsv_color[1] <= blue_range[1][1] and \
blue_range[0][2] <= hsv_color[2] <= blue_range[1][2]:
return "Blue"
elif red_range[0][0] <= hsv_color[0] <= red_range[1][0] and \
red_range[0][1] <= hsv_color[1] <= red_range[1][1] and \
red_range[0][2] <= hsv_color[2] <= red_range[1][2]:
return "Red"
elif yellow_range[0][0] <= hsv_color[0] <= yellow_range[1][0] and \
yellow_range[0][1] <= hsv_color[1] <= yellow_range[1][1] and \
yellow_range[0][2] <= hsv_color[2] <= yellow_range[1][2]:
return "Yellow"
elif green_range[0][0] <= hsv_color[0] <= green_range[1][0] and \
green_range[0][1] <= hsv_color[1] <= green_range[1][1] and \
green_range[0][2] <= hsv_color[2] <= green_range[1][2]:
return "Green"
elif orange_range[0][0] <= hsv_color[0] <= orange_range[1][0] and \
orange_range[0][1] <= hsv_color[1] <= orange_range[1][1] and \
orange_range[0][2] <= hsv_color[2] <= orange_range[1][2]:
return "Orange"
elif purple_range[0][0] <= hsv_color[0] <= purple_range[1][0] and \
purple_range[0][1] <= hsv_color[1] <= purple_range[1][1] and \
purple_range[0][2] <= hsv_color[2] <= purple_range[1][2]:
return "Purple"
elif pink_range[0][0] <= hsv_color[0] <= pink_range[1][0] and \
pink_range[0][1] <= hsv_color[1] <= pink_range[1][1] and \
pink_range[0][2] <= hsv_color[2] <= pink_range[1][2]:
return "Pink"
def dinamik_renk_esigi(kare, hedef_renk_alt, hedef_renk_ust):
"""Hedef renge gore dinamik renk esigi uygula."""
hsv_kare = cv2.cvtColor(kare, cv2.COLOR_BGR2HSV)
maske = cv2.inRange(hsv_kare, hedef_renk_alt, hedef_renk_ust)
return maske

def kontur_filtresi(konturlar, min_alan):
"""Minimum alana gore konturlari filtrele."""
filtreli_konturlar = [kontur for kontur in konturlar if cv2.contourArea(kontur) > min_alan]
return filtreli_konturlar

def renk_tespiti(hsv_renk):
"""HSV degerlerine gore rengi belirle."""
# HSV'de renk araliklarini tanimla
mavi_aralik = (np.array([100, 80, 50]), np.array([140, 255, 255]))
kirmizi_aralik = (np.array([0, 100, 100]), np.array([10, 255, 255]))
sari_aralik = (np.array([20, 100, 100]), np.array([30, 255, 255]))
yesil_aralik = (np.array([40, 100, 100]), np.array([80, 255, 255]))
turuncu_aralik = (np.array([10, 100, 100]), np.array([20, 255, 255]))
mor_aralik = (np.array([120, 100, 100]), np.array([160, 255, 255]))
pembe_aralik = (np.array([160, 100, 100]), np.array([180, 255, 255]))

if mavi_aralik[0][0] <= hsv_renk[0] <= mavi_aralik[1][0] and \
mavi_aralik[0][1] <= hsv_renk[1] <= mavi_aralik[1][1] and \
mavi_aralik[0][2] <= hsv_renk[2] <= mavi_aralik[1][2]:
return "Mavi"
elif kirmizi_aralik[0][0] <= hsv_renk[0] <= kirmizi_aralik[1][0] and \
kirmizi_aralik[0][1] <= hsv_renk[1] <= kirmizi_aralik[1][1] and \
kirmizi_aralik[0][2] <= hsv_renk[2] <= kirmizi_aralik[1][2]:
return "Kirmizi"
elif sari_aralik[0][0] <= hsv_renk[0] <= sari_aralik[1][0] and \
sari_aralik[0][1] <= hsv_renk[1] <= sari_aralik[1][1] and \
sari_aralik[0][2] <= hsv_renk[2] <= sari_aralik[1][2]:
return "Sari"
elif yesil_aralik[0][0] <= hsv_renk[0] <= yesil_aralik[1][0] and \
yesil_aralik[0][1] <= hsv_renk[1] <= yesil_aralik[1][1] and \
yesil_aralik[0][2] <= hsv_renk[2] <= yesil_aralik[1][2]:
return "Yesil"
elif turuncu_aralik[0][0] <= hsv_renk[0] <= turuncu_aralik[1][0] and \
turuncu_aralik[0][1] <= hsv_renk[1] <= turuncu_aralik[1][1] and \
turuncu_aralik[0][2] <= hsv_renk[2] <= turuncu_aralik[1][2]:
return "Turuncu"
elif mor_aralik[0][0] <= hsv_renk[0] <= mor_aralik[1][0] and \
mor_aralik[0][1] <= hsv_renk[1] <= mor_aralik[1][1] and \
mor_aralik[0][2] <= hsv_renk[2] <= mor_aralik[1][2]:
return "Mor"
elif pembe_aralik[0][0] <= hsv_renk[0] <= pembe_aralik[1][0] and \
pembe_aralik[0][1] <= hsv_renk[1] <= pembe_aralik[1][1] and \
pembe_aralik[0][2] <= hsv_renk[2] <= pembe_aralik[1][2]:
return "Pembe"
else:
return "Undefined"
return "Tanimsiz"

def detect_shape_and_color(contour, hsv_frame):
"""Determine the shape and color of the given contour."""
epsilon = 0.02 * cv2.arcLength(contour, True)
approx = cv2.approxPolyDP(contour, epsilon, True)
sides = len(approx)
def sekil_ve_renk_tespiti(kontur, hsv_kare):
"""Verilen konturun sekil ve rengini belirle."""
epsilon = 0.02 * cv2.arcLength(kontur, True)
yaklasik = cv2.approxPolyDP(kontur, epsilon, True)
kenar_sayisi = len(yaklasik)

color = detect_color(hsv_frame[int(contour.mean(axis=0)[:, 1]), int(contour.mean(axis=0)[:, 0])])
renk = renk_tespiti(hsv_kare[int(kontur.mean(axis=0)[:, 1]), int(kontur.mean(axis=0)[:, 0])])

if sides == 3:
return "Triangle", color
elif sides == 4:
x, y, w, h = cv2.boundingRect(approx)
aspect_ratio = float(w) / h
if kenar_sayisi == 3:
return "Ucgen", renk
elif kenar_sayisi == 4:
x, y, w, h = cv2.boundingRect(yaklasik)
en_boy_orani = float(w) / h

if 0.8 <= aspect_ratio <= 1.2:
return "Square", color
if 0.8 <= en_boy_orani <= 1.2:
return "Kare", renk
else:
return "Rectangle", color
elif sides == 5:
return "Pentagon", color
elif sides == 6:
return "Hexagon", color
return "Dikdortgen", renk
elif kenar_sayisi == 5:
return "Besgen", renk
elif kenar_sayisi == 6:
return "Altigen", renk
else:
(x, y), radius = cv2.minEnclosingCircle(approx)
circularity = cv2.contourArea(approx) / (np.pi * radius ** 2)
(x, y), yaricap = cv2.minEnclosingCircle(yaklasik)
yuvarlaklik = cv2.contourArea(yaklasik) / (np.pi * yaricap ** 2)

return "Circle", color if circularity >= 0.6 else "Undefined"
return "Daire", renk if yuvarlaklik >= 0.6 else "Tanimsiz"

def process_frame(frame):
"""Process each frame to detect shapes and colors."""
hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
def kare_isleme(kare):
"""Her kareyi isleyerek sekil ve renk tespiti yap."""
hsv_kare = cv2.cvtColor(kare, cv2.COLOR_BGR2HSV)

# Define target color ranges in HSV for different colors
blue_lower = np.array([100, 80, 50])
blue_upper = np.array([140, 255, 255])
# Farkli renkler icin HSV'de hedef renk araliklarini tanimla
mavi_alt = np.array([100, 80, 50])
mavi_ust = np.array([140, 255, 255])

red_lower = np.array([0, 100, 100])
red_upper = np.array([10, 255, 255])
kirmizi_alt = np.array([0, 100, 100])
kirmizi_ust = np.array([10, 255, 255])

yellow_lower = np.array([20, 100, 100])
yellow_upper = np.array([30, 255, 255])
sari_alt = np.array([20, 100, 100])
sari_ust = np.array([30, 255, 255])

green_lower = np.array([40, 100, 100])
green_upper = np.array([80, 255, 255])
yesil_alt = np.array([40, 100, 100])
yesil_ust = np.array([80, 255, 255])

orange_lower = np.array([10, 100, 100])
orange_upper = np.array([20, 255, 255])
turuncu_alt = np.array([10, 100, 100])
turuncu_ust = np.array([20, 255, 255])

purple_lower = np.array([120, 100, 100])
purple_upper = np.array([160, 255, 255])
mor_alt = np.array([120, 100, 100])
mor_ust = np.array([160, 255, 255])

pink_lower = np.array([160, 100, 100])
pink_upper = np.array([180, 255, 255])
pembe_alt = np.array([160, 100, 100])
pembe_ust = np.array([180, 255, 255])

# Apply color thresholding for each color
blue_mask = dynamic_color_threshold(frame, blue_lower, blue_upper)
red_mask = dynamic_color_threshold(frame, red_lower, red_upper)
yellow_mask = dynamic_color_threshold(frame, yellow_lower, yellow_upper)
green_mask = dynamic_color_threshold(frame, green_lower, green_upper)
orange_mask = dynamic_color_threshold(frame, orange_lower, orange_upper)
purple_mask = dynamic_color_threshold(frame, purple_lower, purple_upper)
pink_mask = dynamic_color_threshold(frame, pink_lower, pink_upper)
# Her renk icin renk esigi uygula
mavi_maske = dinamik_renk_esigi(kare, mavi_alt, mavi_ust)
kirmizi_maske = dinamik_renk_esigi(kare, kirmizi_alt, kirmizi_ust)
sari_maske = dinamik_renk_esigi(kare, sari_alt, sari_ust)
yesil_maske = dinamik_renk_esigi(kare, yesil_alt, yesil_ust)
turuncu_maske = dinamik_renk_esigi(kare, turuncu_alt, turuncu_ust)
mor_maske = dinamik_renk_esigi(kare, mor_alt, mor_ust)
pembe_maske = dinamik_renk_esigi(kare, pembe_alt, pembe_ust)

# Combine masks for different colors
mask = cv2.bitwise_or(cv2.bitwise_or(blue_mask, red_mask, yellow_mask),
cv2.bitwise_or(green_mask, orange_mask, purple_mask, pink_mask))
# Farkli renk maskelerini birlestir
maske = cv2.bitwise_or(cv2.bitwise_or(mavi_maske, kirmizi_maske, sari_maske),
cv2.bitwise_or(yesil_maske, turuncu_maske, mor_maske, pembe_maske))

kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
maske = cv2.morphologyEx(maske, cv2.MORPH_OPEN, kernel)
maske = cv2.morphologyEx(maske, cv2.MORPH_CLOSE, kernel)

contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
filtered_contours = filter_contours(contours, min_contour_area)
konturlar, _ = cv2.findContours(maske, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
filtreli_konturlar = kontur_filtresi(konturlar, min_kontur_alani)

for contour in filtered_contours:
shape, color = detect_shape_and_color(contour, hsv_frame)
for kontur in filtreli_konturlar:
sekil, renk = sekil_ve_renk_tespiti(kontur, hsv_kare)

if color != "Undefined": # If color is defined and not "Undefined," only then draw
x, y, w, h = cv2.boundingRect(contour)
if renk != "Tanimsiz": # Eger renk tanimliysa ve "Tanimsiz" degilse, o zaman ciz
x, y, w, h = cv2.boundingRect(kontur)

if shape != "Circle":
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
if sekil != "Daire":
cv2.rectangle(kare, (x, y), (x + w, y + h), (0, 255, 0), 2)

if shape == "Circle":
cv2.circle(frame, (int(x + w / 2), int(y + h / 2)), int((w + h) / 4), (255, 0, 0), 2)
if sekil == "Daire":
cv2.circle(kare, (int(x + w / 2), int(y + h / 2)), int((w + h) / 4), (255, 0, 0), 2)

cv2.putText(frame, f"{color} {shape}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(kare, f"{renk} {sekil}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

return frame
return kare

# Open the camera connection
cap = cv2.VideoCapture(0)
# Kamera baglantisini ac
kamera = cv2.VideoCapture(0)

# Set frame dimensions (1920x1080)
cap.set(3, 1920)
cap.set(4, 1080)
# Kare boyutlarini ayarla (1920x1080)
kamera.set(3, 1920)
kamera.set(4, 1080)

# Set minimum contour area
min_contour_area = 50 # If you make this too small, it will pick up a lot of noise as 'blobs' instead of actual objects to 50 pixels squared
# Minimum kontur alanini ayarla
min_kontur_alani = 50 # Bunu cok kucuk yaparsaniz, 'gurultu' olarak bircok gereksiz kucuk nesneyi alir

while True:
ret, frame = cap.read()
ret, kare = kamera.read()

processed_frame = process_frame(frame)
cv2.imshow("Object Detection", processed_frame)
islenmis_kare = kare_isleme(kare)
cv2.imshow("Nesne Tespiti", islenmis_kare)

if cv2.waitKey(1) & 0xFF == ord('q'):
break

cap.release()
kamera.release()
cv2.destroyAllWindows()